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\u001b[?25l\u001b[?25hdone\n","  Created wheel for simcse: filename=simcse-0.4-py3-none-any.whl size=15032 sha256=956ed4d068d630aa122b638fc6f18439533f6dd8a733b9d904aa0e093c67271c\n","  Stored in directory: /root/.cache/pip/wheels/29/b0/58/448de751ccbe2a0e75c054e34416e7a5a1aead371d5d163450\n","  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4710 sha256=5c155044c366e301e88f73145bb8a4b1842750eef736bbd787930b056cc3a4df\n","  Stored in directory: /root/.cache/pip/wheels/13/e4/6c/e8059816e86796a597c6e6b0d4c880630f51a1fcfa0befd5e6\n","  Building wheel for flask-cachebuster (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for flask-cachebuster: filename=Flask_CacheBuster-1.0.0-py3-none-any.whl size=3371 sha256=515f857290f3dd522fd806c3f40c9270671e5d275bfceaf793920bf663733ad2\n","  Stored in directory: /root/.cache/pip/wheels/28/c0/c4/44687421dab41455be93112bd1b0dee1f3c5a9aa27bee63708\n","Successfully built simcse ffmpy flask-cachebuster\n","Installing collected packages: tqdm, tokenizers, scipy, sacremoses, pynacl, monotonic, cryptography, bcrypt, backoff, xxhash, transformers, setuptools, scikit-learn, pydub, pycryptodome, paramiko, markdown2, Flask-Login, Flask-Cors, flask-cachebuster, ffmpy, analytics-python, simcse, prettytable, gradio, datasets\n","  Attempting uninstall: tqdm\n","    Found existing installation: tqdm 4.62.3\n","    Uninstalling tqdm-4.62.3:\n","      Successfully uninstalled tqdm-4.62.3\n","  Attempting uninstall: scipy\n","    Found existing installation: scipy 1.4.1\n","    Uninstalling scipy-1.4.1:\n","      Successfully uninstalled scipy-1.4.1\n","  Attempting uninstall: setuptools\n","    Found existing installation: setuptools 57.4.0\n","    Uninstalling setuptools-57.4.0:\n","      Successfully uninstalled setuptools-57.4.0\n","  Attempting uninstall: scikit-learn\n","    Found existing installation: scikit-learn 1.0.1\n","    Uninstalling scikit-learn-1.0.1:\n","      Successfully uninstalled scikit-learn-1.0.1\n","  Attempting uninstall: prettytable\n","    Found existing installation: prettytable 2.4.0\n","    Uninstalling prettytable-2.4.0:\n","      Successfully uninstalled prettytable-2.4.0\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n","albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n","Successfully installed Flask-Cors-3.0.10 Flask-Login-0.5.0 analytics-python-1.4.0 backoff-1.10.0 bcrypt-3.2.0 cryptography-36.0.0 datasets-1.2.1 ffmpy-0.3.0 flask-cachebuster-1.0.0 gradio-2.4.6 markdown2-2.4.1 monotonic-1.6 paramiko-2.8.1 prettytable-2.1.0 pycryptodome-3.11.0 pydub-0.25.1 pynacl-1.4.0 sacremoses-0.0.46 scikit-learn-0.24.0 scipy-1.5.4 setuptools-49.3.0 simcse-0.4 tokenizers-0.9.4 tqdm-4.49.0 transformers-4.2.1 xxhash-2.0.2\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["pkg_resources"]}}},"metadata":{}}]},{"cell_type":"code","metadata":{"id":"_D36n4TMePzA"},"source":["import torch\n","import pandas as pd\n","import numpy as np\n","from simcse import SimCSE\n","from scipy import spatial\n","from sklearn.metrics.pairwise import cosine_similarity, f1_score, matthews_corrcoef\n","\n","from transformers import BertConfig, BertModel\n","from transformers import AutoModelForSequenceClassification, DistilBertForSequenceClassification, Trainer, TrainingArguments, AutoTokenizer\n","from datasets import load_metric\n","import csv\n","\n","if torch.cuda.is_available():\n","  device = \"cuda:0\"\n","else:\n","  device = \"cpu:0\"\n","dtype = torch.cuda.FloatTensor"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"-VE_kGEdVa04","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1638413816009,"user_tz":480,"elapsed":9,"user":{"displayName":"zeeshan ahmad","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15348670853435150367"}},"outputId":"29d003dc-b246-4e23-d974-74da8f827b08"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"code","metadata":{"id":"UOvIP1QwNxnw"},"source":["data_folder = 'drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/'"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"eKXdkvm1eODL"},"source":["## Evaluation of triplets on dev set\n"]},{"cell_type":"markdown","metadata":{"id":"lFMgqREoPNZH"},"source":["## Eval - Bert base uncased - LR 5e-5 - 2 epoch"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5fgsVeiW9I8A","executionInfo":{"status":"ok","timestamp":1638232765686,"user_tz":480,"elapsed":1176284,"user":{"displayName":"zeeshan ahmad","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15348670853435150367"}},"outputId":"1b1cf056-d1fd-493d-a674-79be26bed69b"},"source":["model = SimCSE(\"/content/drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/twiBot-360k-similarity-metadata-2epoch_LR5e-5_bert_base_uncased\")\n","# \"drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/twiBot-360k-similarity-metadata-1epoch\"\n","\n","df_dev = pd.read_csv(data_folder + \"/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/dev_all_data_triplets_with_similarity_corrected_metadata.csv\" , usecols = ['sent0', 'sent1', 'hard_neg'])\n","n = len(df_dev)\n","# df_dev = df_dev[:n]\n","batch_size = 120000\n","count = 0\n","for i in range(0, len(df_dev), batch_size):\n","  print(\"{}/{}\".format(i+1, len(df_dev)))\n","  encoded_sent0 = model.encode(list(df_dev[i:i+batch_size].sent0), device).cpu().numpy()\n","  encoded_sent1 = model.encode(list(df_dev[i:i+batch_size].sent1), device).cpu().numpy()\n","  encoded_hard_neg = model.encode(list(df_dev[i:i+batch_size].hard_neg), device).cpu().numpy()\n","  for j in range(len(encoded_sent0)):\n","    cos_sim_positive = cosine_similarity([encoded_sent0[j]], [encoded_sent1[j]])\n","    cos_sim_negative = cosine_similarity([encoded_sent0[j]], [encoded_hard_neg[j]])\n","    count += (cos_sim_positive > cos_sim_negative)[0][0]\n","  # print(\"Accuracy as of batch\", str(i+1), str(count/i+batch_size))\n","print(\"Accuracy\", count/n)"],"execution_count":null,"outputs":[{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["Some weights of BertModel were not initialized from the model checkpoint at /content/drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/twiBot-360k-similarity-metadata-2epoch_LR5e-5_bert_base_uncased and are newly initialized: ['bert.pooler.dense.weight', 'bert.pooler.dense.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["1/480000\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["100%|██████████| 1875/1875 [07:03<00:00,  4.43it/s]\n","100%|██████████| 1875/1875 [07:07<00:00,  4.38it/s]\n","100%|██████████| 1875/1875 [07:12<00:00,  4.34it/s]\n","  0%|          | 0/1875 [00:00<?, ?it/s]"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["120001/480000\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["100%|██████████| 1875/1875 [07:03<00:00,  4.43it/s]\n","100%|██████████| 1875/1875 [07:06<00:00,  4.39it/s]\n","100%|██████████| 1875/1875 [07:11<00:00,  4.34it/s]\n","  0%|          | 0/1875 [00:00<?, ?it/s]"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["240001/480000\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["100%|██████████| 1875/1875 [07:02<00:00,  4.44it/s]\n","100%|██████████| 1875/1875 [07:09<00:00,  4.36it/s]\n","100%|██████████| 1875/1875 [07:11<00:00,  4.35it/s]\n","  0%|          | 0/1875 [00:00<?, ?it/s]"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["360001/480000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 1875/1875 [07:02<00:00,  4.44it/s]\n","100%|██████████| 1875/1875 [07:09<00:00,  4.36it/s]\n","100%|██████████| 1875/1875 [07:11<00:00,  4.34it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Accuracy 0.9268416666666667\n"]}]},{"cell_type":"markdown","metadata":{"id":"_w30h6PzQLdE"},"source":["## Eval princeton-nlp/sup-simcse-bert-base-uncased - LR 5e-5 - 1 epoch"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KJZOnCaTQSL7","executionInfo":{"status":"ok","timestamp":1638405840376,"user_tz":480,"elapsed":5535145,"user":{"displayName":"zeeshan ahmad","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15348670853435150367"}},"outputId":"21ffa31f-a349-4c36-f44e-a51502cc1908"},"source":["model = SimCSE(\"/content/drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/twiBot-360k-similarity-metadata-1epoch_LR5e-5_sup-simcse-bert-base-uncased\")\n","# \"drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/twiBot-360k-similarity-metadata-1epoch\"\n","\n","df_dev = pd.read_csv(data_folder + \"/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/dev_all_data_triplets_with_similarity_corrected_metadata.csv\" , usecols = ['sent0', 'sent1', 'hard_neg'])\n","n = len(df_dev)\n","# df_dev = df_dev[:n]\n","batch_size = 120000\n","count = 0\n","for i in range(0, len(df_dev), batch_size):\n","  print(\"{}/{}\".format(i+1, len(df_dev)))\n","  encoded_sent0 = model.encode(list(df_dev[i:i+batch_size].sent0), device).cpu().numpy()\n","  encoded_sent1 = model.encode(list(df_dev[i:i+batch_size].sent1), device).cpu().numpy()\n","  encoded_hard_neg = model.encode(list(df_dev[i:i+batch_size].hard_neg), device).cpu().numpy()\n","  for j in range(len(encoded_sent0)):\n","    cos_sim_positive = cosine_similarity([encoded_sent0[j]], [encoded_sent1[j]])\n","    cos_sim_negative = cosine_similarity([encoded_sent0[j]], [encoded_hard_neg[j]])\n","    count += (cos_sim_positive > cos_sim_negative)[0][0]\n","  # print(\"Accuracy as of batch\", str(i+1), str(count/i+batch_size))\n","print(\"Accuracy\", count/n)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Some weights of BertModel were not initialized from the model checkpoint at /content/drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/twiBot-360k-similarity-metadata-1epoch_LR5e-5_sup-simcse-bert-base-uncased and are newly initialized: ['bert.pooler.dense.weight', 'bert.pooler.dense.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]},{"output_type":"stream","name":"stdout","text":["1/480000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 1875/1875 [07:06<00:00,  4.40it/s]\n","100%|██████████| 1875/1875 [07:10<00:00,  4.36it/s]\n","100%|██████████| 1875/1875 [07:14<00:00,  4.31it/s]\n","  0%|          | 0/1875 [00:00<?, ?it/s]"]},{"output_type":"stream","name":"stdout","text":["120001/480000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 1875/1875 [07:05<00:00,  4.40it/s]\n","100%|██████████| 1875/1875 [07:10<00:00,  4.36it/s]\n","100%|██████████| 1875/1875 [07:15<00:00,  4.31it/s]\n","  0%|          | 0/1875 [00:00<?, ?it/s]"]},{"output_type":"stream","name":"stdout","text":["240001/480000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 1875/1875 [07:06<00:00,  4.40it/s]\n","100%|██████████| 1875/1875 [07:13<00:00,  4.33it/s]\n","100%|██████████| 1875/1875 [07:14<00:00,  4.31it/s]\n","  0%|          | 0/1875 [00:00<?, ?it/s]"]},{"output_type":"stream","name":"stdout","text":["360001/480000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 1875/1875 [07:05<00:00,  4.40it/s]\n","100%|██████████| 1875/1875 [07:13<00:00,  4.33it/s]\n","100%|██████████| 1875/1875 [07:15<00:00,  4.31it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Accuracy 0.9154666666666667\n"]}]},{"cell_type":"markdown","metadata":{"id":"4vLUYueamc_F"},"source":["## Exploring"]},{"cell_type":"code","metadata":{"id":"VegOl_OlF6uZ","colab":{"base_uri":"https://localhost:8080/","height":264,"referenced_widgets":["ea82499d74e24c99b84fd05eca6b9ee6","c500952fdecc46e7861be225869b4b5b","766319c3d0bd4c3c996a7666d15d5867","2bf633c530284687a67658a449120778","80cbe5c178d04f04a1de773ec77a740b","e9110ceaa64a473abef8432c83d9d6b6","497e1ae67c834526af806bff4d849bec","79e86bc6d4e7442cbfbd36b83965f040","d1c0f24bfeff42deadf8e0e126de7bfe","a84ecf10a3a64ead922d4fbd47124912","a1c8e26b1c304375856d123f4f386fcc","b0f799259d944b64aa02f43a91232a30","f48212ead29c4f19bea546b546f3e27d","469d2f31507d4e1ba5056490049c3084","87f9206a8daa41069bcf23a9b276fe41","fc8c0ed22ff045ebb4b89459cabcb409","f618996d73e74b4884afe9e24566c043","c1944831b8f5446fb0f24a735c13c0fa","6dd60641f21246c2aa199dc2a751f960","4438642da1f0461daefce513e8386773","64fa7274f4bd41018bdd4ea61b702911","38e1fa2a83984526b475a504832a2acb","55ab2779c9cd431a8e3b5407f0bfa3a8","fce306d473f9481da89316bfa7c269ba","de2b67a8f5364ff7a4abc9c207c4da3a","fd81256a47914b83923e034073e1a12b","8328591a40f84473adee42c92ea2abef","766999c0ce5c4e96b96652fc7b6eb46f","7b7d7f28fc1e4f3f9f46cee4ef18b82a","ef7fbf4929e4486f86c7bbc5692ac9c8","50ddbb3f3515449fa935838d25a5b175","4deb49faef25423d96deb5890e69c050","c0cf5bbf80a740778d8e266e0ac54aa3","8715b5c6612144cca78cd9df36e14ae2","b4d130eaebaa49fc844f47e6ad5d8bbf","510bfc85cdbb497c833097e1521f1586","d6e50903858142bf80b4e9c364f6f417","5350c499b5df42c380cd2386ee1ee086","73b8f405255d4d8c8f258f1ef896d3e8","08b2631a3a174e9f847a84c873966383"]},"executionInfo":{"status":"ok","timestamp":1638068415952,"user_tz":480,"elapsed":22434,"user":{"displayName":"Vrundha Subramanyan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgS9gWhR-SR1T_C70ku5yZ4Pm8jwm-Z6eT973Hn=s64","userId":"05356123305398771631"}},"outputId":"5c3f3937-174c-4fad-91bd-a0e337decbfa"},"source":["model = SimCSE(\"princeton-nlp/sup-simcse-bert-base-uncased\")\n","## is supervised and case insensitive"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ea82499d74e24c99b84fd05eca6b9ee6","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Downloading', max=689.0, style=ProgressStyle(description_…"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d1c0f24bfeff42deadf8e0e126de7bfe","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f618996d73e74b4884afe9e24566c043","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Downloading', max=112.0, style=ProgressStyle(description_…"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"de2b67a8f5364ff7a4abc9c207c4da3a","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Downloading', max=252.0, style=ProgressStyle(description_…"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c0cf5bbf80a740778d8e266e0ac54aa3","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Downloading', max=437998343.0, style=ProgressStyle(descri…"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n"]}]},{"cell_type":"markdown","metadata":{"id":"LjTBXkJN7Kxj"},"source":["## Fine-tuning with Classifier"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LLCxsW2X8prO","executionInfo":{"status":"ok","timestamp":1638407654683,"user_tz":480,"elapsed":225,"user":{"displayName":"zeeshan ahmad","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15348670853435150367"}},"outputId":"09cc7eac-9fd4-40ba-f4f3-7da1582d86fe"},"source":["!ls /content/drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/twiBot-360k-similarity-metadata-2epoch_LR5e-5_bert_base_uncased"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["config.json\t   scheduler.pt\t\t    training_args.bin\n","eval_results.txt   special_tokens_map.json  train_results.txt\n","optimizer.pt\t   tokenizer_config.json    vocab.txt\n","pytorch_model.bin  trainer_state.json\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"q2xyjlcx7Wih","executionInfo":{"status":"ok","timestamp":1638407702905,"user_tz":480,"elapsed":3732,"user":{"displayName":"zeeshan ahmad","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15348670853435150367"}},"outputId":"ceebe103-1dec-4f2b-cb39-7ea49ffb54b7"},"source":["# Task 00 - contrastive learning embeddings without tuning classifier\n","model_twitter_classifier = AutoModelForSequenceClassification.from_pretrained(\"/content/drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/twiBot-360k-similarity-metadata-1epoch_LR5e-5_sup-simcse-bert-base-uncased\")\n","# prevent backprop for base model\n","for param in model_twitter_classifier.base_model.parameters():\n","    param.requires_grad = False"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at /content/drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/twiBot-360k-similarity-metadata-1epoch_LR5e-5_sup-simcse-bert-base-uncased were not used when initializing BertForSequenceClassification: ['mlp.dense.weight', 'mlp.dense.bias']\n","- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForSequenceClassification were not initialized from the model checkpoint at /content/drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/twiBot-360k-similarity-metadata-1epoch_LR5e-5_sup-simcse-bert-base-uncased and are newly initialized: ['bert.pooler.dense.weight', 'bert.pooler.dense.bias', 'classifier.weight', 'classifier.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Mz9xB833sFQj","executionInfo":{"status":"ok","timestamp":1638356943475,"user_tz":480,"elapsed":6284,"user":{"displayName":"zeeshan ahmad","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15348670853435150367"}},"outputId":"bcb50db3-fff3-4ca2-837c-a253f5fe4436"},"source":["# Task 01 - 360k model, no freeze on params Bert-base-uncased-2epoch-LR5e-5\" \n","model_twitter_classifier = AutoModelForSequenceClassification.from_pretrained(\"/content/drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/twiBot-360k-similarity-metadata-2epoch_LR5e-5_bert_base_uncased\")\n","# prevent backprop for base model\n","for param in model_twitter_classifier.base_model.parameters():\n","    param.requires_grad = True"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at /content/drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/twiBot-360k-similarity-metadata-2epoch_LR5e-5_bert_base_uncased were not used when initializing BertForSequenceClassification: ['mlp.dense.weight', 'mlp.dense.bias']\n","- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForSequenceClassification were not initialized from the model checkpoint at /content/drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/twiBot-360k-similarity-metadata-2epoch_LR5e-5_bert_base_uncased and are newly initialized: ['bert.pooler.dense.weight', 'bert.pooler.dense.bias', 'classifier.weight', 'classifier.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}]},{"cell_type":"code","metadata":{"id":"vF0BQS4m7puG"},"source":["training_args = TrainingArguments(\"test_trainer\", evaluation_strategy=\"epoch\")\n","tokenizer = AutoTokenizer.from_pretrained(\"/content/drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/twiBot-360k-similarity-metadata-1epoch_LR5e-5_sup-simcse-bert-base-uncased\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"MxTnWMjQ791G","colab":{"base_uri":"https://localhost:8080/","referenced_widgets":["0a1c55df1adb4421ae9bc5769169c8b2","678b3a2f6bfb4514987d11abc02d1e98","9c200ad24edc45e38f119fb461910694","3455c3a8771c4beca9badd26ec56ff6e","6adbaadf67be4589a9f1b078ed786a3c","fd4e0e803a9a45bc96bed7c2e74b20fd","76a0a382e11f47ebbb23dc176e2c308c","e9eb849d9b954ce68cd2396f65b0fe32"]},"executionInfo":{"status":"ok","timestamp":1638407762101,"user_tz":480,"elapsed":978,"user":{"displayName":"zeeshan ahmad","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15348670853435150367"}},"outputId":"2a67fa15-f373-4e7b-8932-4ecaee5c683d"},"source":["metric = load_metric(\"accuracy\")\n","\n","def compute_metrics(eval_pred):\n","    logits, labels = eval_pred\n","    predictions = np.argmax(logits, axis=-1)\n","    return metric.compute(predictions=predictions, references=labels)\n","    "],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0a1c55df1adb4421ae9bc5769169c8b2","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1248.0, style=ProgressStyle(description…"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n"]}]},{"cell_type":"code","metadata":{"id":"gQu25uC3LsHw"},"source":["\n","df_train = pd.read_csv(data_folder + \"Model_TwiBot_Similarity_With_Metadata_360k_Tweets/train_all_data_triplets_with_similarity_new.csv\", usecols = ['sent0', 'sent0_label'])\n","df_train = df_train.drop_duplicates()\n","df_train = df_train.sample(frac=1).reset_index(drop=True)\n","\n","df_dev = pd.read_csv(data_folder + \"/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/dev_all_data_triplets_with_similarity_corrected_metadata.csv\" , usecols = ['sent0', 'sent0_label'])\n","df_dev = df_dev.drop_duplicates()\n","df_dev = df_dev.sample(frac=1).reset_index(drop=True)\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"NLiMT44J8Q80"},"source":["train_sentences = list(df_train.sent0) #[\"rt <_user_> : hahaha <_user_> i got my revenge do n't worry 😂 😂 <_user_> <_url_>\", \"<_user_> <_user_> i was involved in the crash mentionned in the news\", \"election results : see the final results and what votes are still being counted in the races that matter to you across the ohio valley . <_url_>\"]\n","train_labels = df_train.sent0_label #[0, 0, 1]\n","\n","dev_sentences = list(df_dev.sent0)\n","dev_labels = df_dev.sent0_label"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Mgc8n2dz_cQA"},"source":["class TwiDataset(torch.utils.data.Dataset):\n","    def __init__(self, encodings, labels):\n","        self.encodings = encodings\n","        self.labels = labels\n","\n","    def __getitem__(self, idx):\n","        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n","        item['labels'] = torch.tensor(self.labels[idx])\n","        return item\n","\n","    def __len__(self):\n","        return len(self.labels)\n","\n","train_tokens = tokenizer(train_sentences, padding=True, truncation=True)\n","train_dataset = TwiDataset(train_tokens, train_labels)\n","\n","# dev_tokens = tokenizer(dev_sentences, padding=True, truncation=True)\n","# dev_dataset = TwiDataset(dev_tokens, dev_labels)\n","\n","\n","# test_dataset = TwiDataset(test_encodings, test_labels)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"v8jRRrz37gAN"},"source":["## Train classifier task 00 - bert-base-uncased-1epoch-LR5e-5"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0__OsUWRExU8","executionInfo":{"status":"ok","timestamp":1638182459793,"user_tz":480,"elapsed":4981789,"user":{"displayName":"zeeshan ahmad","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15348670853435150367"}},"outputId":"7aa15496-e0fe-4930-ae66-bfacad6c5e3b"},"source":["training_args = TrainingArguments(\n","    output_dir = data_folder+'Model_TwiBot_Similarity_With_Metadata_360k_Tweets/Classifier/ \\\n","                            twiBot-360k-similarity-metadata-1epoch_LR5e-5_bert_base_uncased/results',          # output directory\n","    num_train_epochs = 1,              # total number of training epochs\n","    per_device_train_batch_size = 16,  # batch size per device during training\n","    per_device_eval_batch_size = 64,   # batch size for evaluation\n","    warmup_steps = 500,                # number of warmup steps for learning rate scheduler\n","    weight_decay = 0.01,               # strength of weight decay\n","    logging_dir = data_folder+'Model_TwiBot_Similarity_With_Metadata_360k_Tweets/Classifier/ \\\n","                            twiBot-360k-similarity-metadata-1epoch_LR5e-5_bert_base_uncased/logs',            # directory for storing logs\n","    logging_steps=10,\n",")\n","\n","trainer = Trainer(\n","    model=model_twitter_classifier,       # the instantiated 🤗 Transformers model to be trained\n","    args=training_args,                   # training arguments, defined above\n","    train_dataset=train_dataset           # training dataset\n","    # eval_dataset=val_dataset            # evaluation dataset\n",")\n","\n","trainer.train()"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/_distutils_hack/__init__.py:19: UserWarning: Distutils was imported before Setuptools. This usage is discouraged and may exhibit undesirable behaviors or errors. Please use Setuptools' objects directly or at least import Setuptools first.\n","  \"Distutils was imported before Setuptools. This usage is discouraged \"\n"]},{"output_type":"display_data","data":{"text/html":["\n","    <div>\n","        <style>\n","            /* Turns off some styling */\n","            progress {\n","                /* gets rid of default border in Firefox and Opera. */\n","                border: none;\n","                /* Needs to be in here for Safari polyfill so background images work as expected. */\n","                background-size: auto;\n","            }\n","        </style>\n","      \n","      <progress value='22145' max='22145' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [22145/22145 1:22:58, Epoch 1/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>10</td>\n","      <td>0.668900</td>\n","    </tr>\n","    <tr>\n","      <td>20</td>\n","      <td>0.667800</td>\n","    </tr>\n","    <tr>\n","      <td>30</td>\n","      <td>0.658100</td>\n","    </tr>\n","    <tr>\n","      <td>40</td>\n","      <td>0.679400</td>\n","    </tr>\n","    <tr>\n","      <td>50</td>\n","      <td>0.660300</td>\n","    </tr>\n","    <tr>\n","      <td>60</td>\n","      <td>0.658200</td>\n","    </tr>\n","    <tr>\n","      <td>70</td>\n","      <td>0.642700</td>\n","    </tr>\n","    <tr>\n","      <td>80</td>\n","      <td>0.663500</td>\n","    </tr>\n","    <tr>\n","      <td>90</td>\n","      <td>0.662000</td>\n","    </tr>\n","    <tr>\n","      <td>100</td>\n","      <td>0.658900</td>\n","    </tr>\n","    <tr>\n","      <td>110</td>\n","      <td>0.647100</td>\n","    </tr>\n","    <tr>\n","      <td>120</td>\n","      <td>0.665000</td>\n","    </tr>\n","    <tr>\n","      <td>130</td>\n","      <td>0.646600</td>\n","    </tr>\n","    <tr>\n","      <td>140</td>\n","      <td>0.646900</td>\n","    </tr>\n","    <tr>\n","      <td>150</td>\n","      <td>0.645600</td>\n","    </tr>\n","    <tr>\n","      <td>160</td>\n","      <td>0.629200</td>\n","    </tr>\n","    <tr>\n","      <td>170</td>\n","      <td>0.634000</td>\n","    </tr>\n","    <tr>\n","      <td>180</td>\n","      <td>0.627500</td>\n","    </tr>\n","    <tr>\n","      <td>190</td>\n","      <td>0.618300</td>\n","    </tr>\n","    <tr>\n","      <td>200</td>\n","      <td>0.619800</td>\n","    </tr>\n","    <tr>\n","      <td>210</td>\n","      <td>0.621600</td>\n","    </tr>\n","    <tr>\n","      <td>220</td>\n","      <td>0.618800</td>\n","    </tr>\n","    <tr>\n","      <td>230</td>\n","      <td>0.612000</td>\n","    </tr>\n","    <tr>\n","      <td>240</td>\n","      <td>0.598400</td>\n","    </tr>\n","    <tr>\n","      <td>250</td>\n","      <td>0.593500</td>\n","    </tr>\n","    <tr>\n","      <td>260</td>\n","      <td>0.595200</td>\n","    </tr>\n","    <tr>\n","      <td>270</td>\n","      <td>0.589900</td>\n","    </tr>\n","    <tr>\n","      <td>280</td>\n","      <td>0.578700</td>\n","    </tr>\n","    <tr>\n","      <td>290</td>\n","      <td>0.569700</td>\n","    </tr>\n","    <tr>\n","      <td>300</td>\n","      <td>0.579100</td>\n","    </tr>\n","    <tr>\n","      <td>310</td>\n","      <td>0.558000</td>\n","    </tr>\n","    <tr>\n","      <td>320</td>\n","      <td>0.563300</td>\n","    </tr>\n","    <tr>\n","      <td>330</td>\n","      <td>0.557700</td>\n","    </tr>\n","    <tr>\n","      <td>340</td>\n","      <td>0.553000</td>\n","    </tr>\n","    <tr>\n","      <td>350</td>\n","      <td>0.538200</td>\n","    </tr>\n","    <tr>\n","      <td>360</td>\n","      <td>0.537900</td>\n","    </tr>\n","    <tr>\n","      <td>370</td>\n","      <td>0.509800</td>\n","    </tr>\n","    <tr>\n","      <td>380</td>\n","      <td>0.505100</td>\n","    </tr>\n","    <tr>\n","      <td>390</td>\n","      <td>0.505800</td>\n","    </tr>\n","    <tr>\n","      <td>400</td>\n","      <td>0.492500</td>\n","    </tr>\n","    <tr>\n","      <td>410</td>\n","      <td>0.487600</td>\n","    </tr>\n","    <tr>\n","      <td>420</td>\n","      <td>0.490400</td>\n","    </tr>\n","    <tr>\n","      <td>430</td>\n","      <td>0.475600</td>\n","    </tr>\n","    <tr>\n","      <td>440</td>\n","      <td>0.473100</td>\n","    </tr>\n","    <tr>\n","      <td>450</td>\n","      <td>0.456200</td>\n","    </tr>\n","    <tr>\n","      <td>460</td>\n","      <td>0.454200</td>\n","    </tr>\n","    <tr>\n","      <td>470</td>\n","      <td>0.443600</td>\n","    </tr>\n","    <tr>\n","      <td>480</td>\n","      <td>0.436700</td>\n","    </tr>\n","    <tr>\n","      <td>490</td>\n","      <td>0.437600</td>\n","    </tr>\n","    <tr>\n","      <td>500</td>\n","      <td>0.421500</td>\n","    </tr>\n","    <tr>\n","      <td>510</td>\n","      <td>0.395600</td>\n","    </tr>\n","    <tr>\n","      <td>520</td>\n","      <td>0.404100</td>\n","    </tr>\n","    <tr>\n","      <td>530</td>\n","      <td>0.401500</td>\n","    </tr>\n","    <tr>\n","      <td>540</td>\n","      <td>0.391000</td>\n","    </tr>\n","    <tr>\n","      <td>550</td>\n","      <td>0.384600</td>\n","    </tr>\n","    <tr>\n","      <td>560</td>\n","      <td>0.380500</td>\n","    </tr>\n","    <tr>\n","      <td>570</td>\n","      <td>0.365900</td>\n","    </tr>\n","    <tr>\n","      <td>580</td>\n","      <td>0.349600</td>\n","    </tr>\n","    <tr>\n","      <td>590</td>\n","      <td>0.363500</td>\n","    </tr>\n","    <tr>\n","      <td>600</td>\n","      <td>0.369400</td>\n","    </tr>\n","    <tr>\n","      <td>610</td>\n","      <td>0.346900</td>\n","    </tr>\n","    <tr>\n","      <td>620</td>\n","      <td>0.341900</td>\n","    </tr>\n","    <tr>\n","      <td>630</td>\n","      <td>0.327400</td>\n","    </tr>\n","    <tr>\n","      <td>640</td>\n","      <td>0.323400</td>\n","    </tr>\n","    <tr>\n","      <td>650</td>\n","      <td>0.321400</td>\n","    </tr>\n","    <tr>\n","      <td>660</td>\n","      <td>0.302200</td>\n","    </tr>\n","    <tr>\n","      <td>670</td>\n","      <td>0.306600</td>\n","    </tr>\n","    <tr>\n","      <td>680</td>\n","      <td>0.304700</td>\n","    </tr>\n","    <tr>\n","      <td>690</td>\n","      <td>0.291200</td>\n","    </tr>\n","    <tr>\n","      <td>700</td>\n","      <td>0.288500</td>\n","    </tr>\n","    <tr>\n","      <td>710</td>\n","      <td>0.285600</td>\n","    </tr>\n","    <tr>\n","      <td>720</td>\n","      <td>0.274000</td>\n","    </tr>\n","    <tr>\n","      <td>730</td>\n","      <td>0.276300</td>\n","    </tr>\n","    <tr>\n","      <td>740</td>\n","      <td>0.271500</td>\n","    </tr>\n","    <tr>\n","      <td>750</td>\n","      <td>0.265200</td>\n","    </tr>\n","    <tr>\n","      <td>760</td>\n","      <td>0.266300</td>\n","    </tr>\n","    <tr>\n","      <td>770</td>\n","      <td>0.258600</td>\n","    </tr>\n","    <tr>\n","      <td>780</td>\n","      <td>0.251600</td>\n","    </tr>\n","    <tr>\n","      <td>790</td>\n","      <td>0.249000</td>\n","    </tr>\n","    <tr>\n","      <td>800</td>\n","      <td>0.241400</td>\n","    </tr>\n","    <tr>\n","      <td>810</td>\n","      <td>0.251500</td>\n","    </tr>\n","    <tr>\n","      <td>820</td>\n","      <td>0.242600</td>\n","    </tr>\n","    <tr>\n","      <td>830</td>\n","      <td>0.243800</td>\n","    </tr>\n","    <tr>\n","      <td>840</td>\n","      <td>0.227200</td>\n","    </tr>\n","    <tr>\n","      <td>850</td>\n","      <td>0.245300</td>\n","    </tr>\n","    <tr>\n","      <td>860</td>\n","      <td>0.220800</td>\n","    </tr>\n","    <tr>\n","      <td>870</td>\n","      <td>0.217700</td>\n","    </tr>\n","    <tr>\n","      <td>880</td>\n","      <td>0.223500</td>\n","    </tr>\n","    <tr>\n","      <td>890</td>\n","      <td>0.230300</td>\n","    </tr>\n","    <tr>\n","      <td>900</td>\n","      <td>0.224000</td>\n","    </tr>\n","    <tr>\n","      <td>910</td>\n","      <td>0.204100</td>\n","    </tr>\n","    <tr>\n","      <td>920</td>\n","      <td>0.211100</td>\n","    </tr>\n","    <tr>\n","      <td>930</td>\n","      <td>0.208000</td>\n","    </tr>\n","    <tr>\n","      <td>940</td>\n","      <td>0.196500</td>\n","    </tr>\n","    <tr>\n","      <td>950</td>\n","      <td>0.209400</td>\n","    </tr>\n","    <tr>\n","      <td>960</td>\n","      <td>0.194300</td>\n","    </tr>\n","    <tr>\n","      <td>970</td>\n","      <td>0.194500</td>\n","    </tr>\n","    <tr>\n","      <td>980</td>\n","      <td>0.192000</td>\n","    </tr>\n","    <tr>\n","      <td>990</td>\n","      <td>0.185800</td>\n","    </tr>\n","    <tr>\n","      <td>1000</td>\n","      <td>0.193100</td>\n","    </tr>\n","    <tr>\n","      <td>1010</td>\n","      <td>0.180900</td>\n","    </tr>\n","    <tr>\n","      <td>1020</td>\n","      <td>0.185900</td>\n","    </tr>\n","    <tr>\n","      <td>1030</td>\n","      <td>0.177500</td>\n","    </tr>\n","    <tr>\n","      <td>1040</td>\n","      <td>0.177900</td>\n","    </tr>\n","    <tr>\n","      <td>1050</td>\n","      <td>0.175800</td>\n","    </tr>\n","    <tr>\n","      <td>1060</td>\n","      <td>0.172000</td>\n","    </tr>\n","    <tr>\n","      <td>1070</td>\n","      <td>0.175500</td>\n","    </tr>\n","    <tr>\n","      <td>1080</td>\n","      <td>0.172700</td>\n","    </tr>\n","    <tr>\n","      <td>1090</td>\n","      <td>0.171300</td>\n","    </tr>\n","    <tr>\n","      <td>1100</td>\n","      <td>0.165600</td>\n","    </tr>\n","    <tr>\n","      <td>1110</td>\n","      <td>0.171800</td>\n","    </tr>\n","    <tr>\n","      <td>1120</td>\n","      <td>0.157100</td>\n","    </tr>\n","    <tr>\n","      <td>1130</td>\n","      <td>0.159400</td>\n","    </tr>\n","    <tr>\n","      <td>1140</td>\n","      <td>0.158700</td>\n","    </tr>\n","    <tr>\n","      <td>1150</td>\n","      <td>0.153100</td>\n","    </tr>\n","    <tr>\n","      <td>1160</td>\n","      <td>0.158700</td>\n","    </tr>\n","    <tr>\n","      <td>1170</td>\n","      <td>0.153500</td>\n","    </tr>\n","    <tr>\n","      <td>1180</td>\n","      <td>0.160300</td>\n","    </tr>\n","    <tr>\n","      <td>1190</td>\n","      <td>0.148300</td>\n","    </tr>\n","    <tr>\n","      <td>1200</td>\n","      <td>0.142800</td>\n","    </tr>\n","    <tr>\n","      <td>1210</td>\n","      <td>0.140500</td>\n","    </tr>\n","    <tr>\n","      <td>1220</td>\n","      <td>0.133400</td>\n","    </tr>\n","    <tr>\n","      <td>1230</td>\n","      <td>0.143900</td>\n","    </tr>\n","    <tr>\n","      <td>1240</td>\n","      <td>0.139800</td>\n","    </tr>\n","    <tr>\n","      <td>1250</td>\n","      <td>0.140800</td>\n","    </tr>\n","    <tr>\n","      <td>1260</td>\n","      <td>0.132400</td>\n","    </tr>\n","    <tr>\n","      <td>1270</td>\n","      <td>0.142100</td>\n","    </tr>\n","    <tr>\n","      <td>1280</td>\n","      <td>0.133300</td>\n","    </tr>\n","    <tr>\n","      <td>1290</td>\n","      <td>0.130700</td>\n","    </tr>\n","    <tr>\n","      <td>1300</td>\n","      <td>0.134000</td>\n","    </tr>\n","    <tr>\n","      <td>1310</td>\n","      <td>0.121400</td>\n","    </tr>\n","    <tr>\n","      <td>1320</td>\n","      <td>0.134100</td>\n","    </tr>\n","    <tr>\n","      <td>1330</td>\n","      <td>0.122400</td>\n","    </tr>\n","    <tr>\n","      <td>1340</td>\n","      <td>0.130200</td>\n","    </tr>\n","    <tr>\n","      <td>1350</td>\n","      <td>0.120200</td>\n","    </tr>\n","    <tr>\n","      <td>1360</td>\n","      <td>0.124000</td>\n","    </tr>\n","    <tr>\n","      <td>1370</td>\n","      <td>0.119400</td>\n","    </tr>\n","    <tr>\n","      <td>1380</td>\n","      <td>0.117100</td>\n","    </tr>\n","    <tr>\n","      <td>1390</td>\n","      <td>0.116500</td>\n","    </tr>\n","    <tr>\n","      <td>1400</td>\n","      <td>0.121700</td>\n","    </tr>\n","    <tr>\n","      <td>1410</td>\n","      <td>0.115000</td>\n","    </tr>\n","    <tr>\n","      <td>1420</td>\n","      <td>0.114700</td>\n","    </tr>\n","    <tr>\n","      <td>1430</td>\n","      <td>0.117300</td>\n","    </tr>\n","    <tr>\n","      <td>1440</td>\n","      <td>0.118100</td>\n","    </tr>\n","    <tr>\n","      <td>1450</td>\n","      <td>0.113000</td>\n","    </tr>\n","    <tr>\n","      <td>1460</td>\n","      <td>0.106200</td>\n","    </tr>\n","    <tr>\n","      <td>1470</td>\n","      <td>0.113300</td>\n","    </tr>\n","    <tr>\n","      <td>1480</td>\n","      <td>0.108200</td>\n","    </tr>\n","    <tr>\n","      <td>1490</td>\n","      <td>0.105500</td>\n","    </tr>\n","    <tr>\n","      <td>1500</td>\n","      <td>0.108300</td>\n","    </tr>\n","    <tr>\n","      <td>1510</td>\n","      <td>0.103300</td>\n","    </tr>\n","    <tr>\n","      <td>1520</td>\n","      <td>0.113100</td>\n","    </tr>\n","    <tr>\n","      <td>1530</td>\n","      <td>0.102500</td>\n","    </tr>\n","    <tr>\n","      <td>1540</td>\n","      <td>0.111700</td>\n","    </tr>\n","    <tr>\n","      <td>1550</td>\n","      <td>0.102200</td>\n","    </tr>\n","    <tr>\n","      <td>1560</td>\n","      <td>0.101700</td>\n","    </tr>\n","    <tr>\n","      <td>1570</td>\n","      <td>0.108100</td>\n","    </tr>\n","    <tr>\n","      <td>1580</td>\n","      <td>0.106200</td>\n","    </tr>\n","    <tr>\n","      <td>1590</td>\n","      <td>0.100700</td>\n","    </tr>\n","    <tr>\n","      <td>1600</td>\n","      <td>0.095600</td>\n","    </tr>\n","    <tr>\n","      <td>1610</td>\n","      <td>0.107700</td>\n","    </tr>\n","    <tr>\n","      <td>1620</td>\n","      <td>0.097100</td>\n","    </tr>\n","    <tr>\n","      <td>1630</td>\n","      <td>0.097700</td>\n","    </tr>\n","    <tr>\n","      <td>1640</td>\n","      <td>0.095900</td>\n","    </tr>\n","    <tr>\n","      <td>1650</td>\n","      <td>0.089400</td>\n","    </tr>\n","    <tr>\n","      <td>1660</td>\n","      <td>0.092400</td>\n","    </tr>\n","    <tr>\n","      <td>1670</td>\n","      <td>0.097200</td>\n","    </tr>\n","    <tr>\n","      <td>1680</td>\n","      <td>0.098700</td>\n","    </tr>\n","    <tr>\n","      <td>1690</td>\n","      <td>0.086300</td>\n","    </tr>\n","    <tr>\n","      <td>1700</td>\n","      <td>0.095400</td>\n","    </tr>\n","    <tr>\n","      <td>1710</td>\n","      <td>0.087700</td>\n","    </tr>\n","    <tr>\n","      <td>1720</td>\n","      <td>0.082700</td>\n","    </tr>\n","    <tr>\n","      <td>1730</td>\n","      <td>0.082900</td>\n","    </tr>\n","    <tr>\n","      <td>1740</td>\n","      <td>0.085700</td>\n","    </tr>\n","    <tr>\n","      <td>1750</td>\n","      <td>0.084700</td>\n","    </tr>\n","    <tr>\n","      <td>1760</td>\n","      <td>0.087600</td>\n","    </tr>\n","    <tr>\n","      <td>1770</td>\n","      <td>0.089400</td>\n","    </tr>\n","    <tr>\n","      <td>1780</td>\n","      <td>0.080600</td>\n","    </tr>\n","    <tr>\n","      <td>1790</td>\n","      <td>0.079900</td>\n","    </tr>\n","    <tr>\n","      <td>1800</td>\n","      <td>0.082200</td>\n","    </tr>\n","    <tr>\n","      <td>1810</td>\n","      <td>0.081600</td>\n","    </tr>\n","    <tr>\n","      <td>1820</td>\n","      <td>0.082100</td>\n","    </tr>\n","    <tr>\n","      <td>1830</td>\n","      <td>0.082400</td>\n","    </tr>\n","    <tr>\n","      <td>1840</td>\n","      <td>0.077500</td>\n","    </tr>\n","    <tr>\n","      <td>1850</td>\n","      <td>0.077000</td>\n","    </tr>\n","    <tr>\n","      <td>1860</td>\n","      <td>0.082400</td>\n","    </tr>\n","    <tr>\n","      <td>1870</td>\n","      <td>0.077600</td>\n","    </tr>\n","    <tr>\n","      <td>1880</td>\n","      <td>0.074500</td>\n","    </tr>\n","    <tr>\n","      <td>1890</td>\n","      <td>0.091300</td>\n","    </tr>\n","    <tr>\n","      <td>1900</td>\n","      <td>0.076200</td>\n","    </tr>\n","    <tr>\n","      <td>1910</td>\n","      <td>0.076400</td>\n","    </tr>\n","    <tr>\n","      <td>1920</td>\n","      <td>0.073200</td>\n","    </tr>\n","    <tr>\n","      <td>1930</td>\n","      <td>0.075700</td>\n","    </tr>\n","    <tr>\n","      <td>1940</td>\n","      <td>0.068400</td>\n","    </tr>\n","    <tr>\n","      <td>1950</td>\n","      <td>0.072900</td>\n","    </tr>\n","    <tr>\n","      <td>1960</td>\n","      <td>0.070100</td>\n","    </tr>\n","    <tr>\n","      <td>1970</td>\n","      <td>0.078700</td>\n","    </tr>\n","    <tr>\n","      <td>1980</td>\n","      <td>0.072000</td>\n","    </tr>\n","    <tr>\n","      <td>1990</td>\n","      <td>0.066800</td>\n","    </tr>\n","    <tr>\n","      <td>2000</td>\n","      <td>0.071500</td>\n","    </tr>\n","    <tr>\n","      <td>2010</td>\n","      <td>0.072900</td>\n","    </tr>\n","    <tr>\n","      <td>2020</td>\n","      <td>0.067900</td>\n","    </tr>\n","    <tr>\n","      <td>2030</td>\n","      <td>0.068800</td>\n","    </tr>\n","    <tr>\n","      <td>2040</td>\n","      <td>0.069100</td>\n","    </tr>\n","    <tr>\n","      <td>2050</td>\n","      <td>0.064100</td>\n","    </tr>\n","    <tr>\n","      <td>2060</td>\n","      <td>0.066100</td>\n","    </tr>\n","    <tr>\n","      <td>2070</td>\n","      <td>0.065000</td>\n","    </tr>\n","    <tr>\n","      <td>2080</td>\n","      <td>0.064900</td>\n","    </tr>\n","    <tr>\n","      <td>2090</td>\n","      <td>0.063100</td>\n","    </tr>\n","    <tr>\n","      <td>2100</td>\n","      <td>0.065000</td>\n","    </tr>\n","    <tr>\n","      <td>2110</td>\n","      <td>0.059600</td>\n","    </tr>\n","    <tr>\n","      <td>2120</td>\n","      <td>0.064300</td>\n","    </tr>\n","    <tr>\n","      <td>2130</td>\n","      <td>0.063800</td>\n","    </tr>\n","    <tr>\n","      <td>2140</td>\n","      <td>0.059000</td>\n","    </tr>\n","    <tr>\n","      <td>2150</td>\n","      <td>0.063100</td>\n","    </tr>\n","    <tr>\n","      <td>2160</td>\n","      <td>0.061900</td>\n","    </tr>\n","    <tr>\n","      <td>2170</td>\n","      <td>0.077900</td>\n","    </tr>\n","    <tr>\n","      <td>2180</td>\n","      <td>0.061500</td>\n","    </tr>\n","    <tr>\n","      <td>2190</td>\n","      <td>0.058000</td>\n","    </tr>\n","    <tr>\n","      <td>2200</td>\n","      <td>0.057900</td>\n","    </tr>\n","    <tr>\n","      <td>2210</td>\n","      <td>0.062100</td>\n","    </tr>\n","    <tr>\n","      <td>2220</td>\n","      <td>0.062600</td>\n","    </tr>\n","    <tr>\n","      <td>2230</td>\n","      <td>0.057200</td>\n","    </tr>\n","    <tr>\n","      <td>2240</td>\n","      <td>0.058600</td>\n","    </tr>\n","    <tr>\n","      <td>2250</td>\n","      <td>0.055100</td>\n","    </tr>\n","    <tr>\n","      <td>2260</td>\n","      <td>0.059100</td>\n","    </tr>\n","    <tr>\n","      <td>2270</td>\n","      <td>0.052900</td>\n","    </tr>\n","    <tr>\n","      <td>2280</td>\n","      <td>0.056500</td>\n","    </tr>\n","    <tr>\n","      <td>2290</td>\n","      <td>0.058800</td>\n","    </tr>\n","    <tr>\n","      <td>2300</td>\n","      <td>0.054100</td>\n","    </tr>\n","    <tr>\n","      <td>2310</td>\n","      <td>0.056100</td>\n","    </tr>\n","    <tr>\n","      <td>2320</td>\n","      <td>0.054000</td>\n","    </tr>\n","    <tr>\n","      <td>2330</td>\n","      <td>0.067900</td>\n","    </tr>\n","    <tr>\n","      <td>2340</td>\n","      <td>0.053900</td>\n","    </tr>\n","    <tr>\n","      <td>2350</td>\n","      <td>0.053600</td>\n","    </tr>\n","    <tr>\n","      <td>2360</td>\n","      <td>0.054400</td>\n","    </tr>\n","    <tr>\n","      <td>2370</td>\n","      <td>0.052300</td>\n","    </tr>\n","    <tr>\n","      <td>2380</td>\n","      <td>0.051600</td>\n","    </tr>\n","    <tr>\n","      <td>2390</td>\n","      <td>0.049400</td>\n","    </tr>\n","    <tr>\n","      <td>2400</td>\n","      <td>0.052000</td>\n","    </tr>\n","    <tr>\n","      <td>2410</td>\n","      <td>0.051600</td>\n","    </tr>\n","    <tr>\n","      <td>2420</td>\n","      <td>0.050300</td>\n","    </tr>\n","    <tr>\n","      <td>2430</td>\n","      <td>0.050800</td>\n","    </tr>\n","    <tr>\n","      <td>2440</td>\n","      <td>0.049800</td>\n","    </tr>\n","    <tr>\n","      <td>2450</td>\n","      <td>0.049700</td>\n","    </tr>\n","    <tr>\n","      <td>2460</td>\n","      <td>0.054300</td>\n","    </tr>\n","    <tr>\n","      <td>2470</td>\n","      <td>0.050800</td>\n","    </tr>\n","    <tr>\n","      <td>2480</td>\n","      <td>0.046600</td>\n","    </tr>\n","    <tr>\n","      <td>2490</td>\n","      <td>0.051300</td>\n","    </tr>\n","    <tr>\n","      <td>2500</td>\n","      <td>0.050500</td>\n","    </tr>\n","    <tr>\n","      <td>2510</td>\n","      <td>0.047600</td>\n","    </tr>\n","    <tr>\n","      <td>2520</td>\n","      <td>0.053400</td>\n","    </tr>\n","    <tr>\n","      <td>2530</td>\n","      <td>0.049000</td>\n","    </tr>\n","    <tr>\n","      <td>2540</td>\n","      <td>0.050600</td>\n","    </tr>\n","    <tr>\n","      <td>2550</td>\n","      <td>0.050100</td>\n","    </tr>\n","    <tr>\n","      <td>2560</td>\n","      <td>0.046200</td>\n","    </tr>\n","    <tr>\n","      <td>2570</td>\n","      <td>0.043600</td>\n","    </tr>\n","    <tr>\n","      <td>2580</td>\n","      <td>0.043700</td>\n","    </tr>\n","    <tr>\n","      <td>2590</td>\n","      <td>0.042500</td>\n","    </tr>\n","    <tr>\n","      <td>2600</td>\n","      <td>0.050900</td>\n","    </tr>\n","    <tr>\n","      <td>2610</td>\n","      <td>0.047200</td>\n","    </tr>\n","    <tr>\n","      <td>2620</td>\n","      <td>0.044400</td>\n","    </tr>\n","    <tr>\n","      <td>2630</td>\n","      <td>0.045200</td>\n","    </tr>\n","    <tr>\n","      <td>2640</td>\n","      <td>0.043200</td>\n","    </tr>\n","    <tr>\n","      <td>2650</td>\n","      <td>0.043700</td>\n","    </tr>\n","    <tr>\n","      <td>2660</td>\n","      <td>0.043100</td>\n","    </tr>\n","    <tr>\n","      <td>2670</td>\n","      <td>0.043000</td>\n","    </tr>\n","    <tr>\n","      <td>2680</td>\n","      <td>0.042800</td>\n","    </tr>\n","    <tr>\n","      <td>2690</td>\n","      <td>0.042100</td>\n","    </tr>\n","    <tr>\n","      <td>2700</td>\n","      <td>0.044200</td>\n","    </tr>\n","    <tr>\n","      <td>2710</td>\n","      <td>0.040200</td>\n","    </tr>\n","    <tr>\n","      <td>2720</td>\n","      <td>0.040000</td>\n","    </tr>\n","    <tr>\n","      <td>2730</td>\n","      <td>0.041300</td>\n","    </tr>\n","    <tr>\n","      <td>2740</td>\n","      <td>0.043200</td>\n","    </tr>\n","    <tr>\n","      <td>2750</td>\n","      <td>0.053200</td>\n","    </tr>\n","    <tr>\n","      <td>2760</td>\n","      <td>0.040300</td>\n","    </tr>\n","    <tr>\n","      <td>2770</td>\n","      <td>0.041800</td>\n","    </tr>\n","    <tr>\n","      <td>2780</td>\n","      <td>0.042200</td>\n","    </tr>\n","    <tr>\n","      <td>2790</td>\n","      <td>0.038500</td>\n","    </tr>\n","    <tr>\n","      <td>2800</td>\n","      <td>0.037900</td>\n","    </tr>\n","    <tr>\n","      <td>2810</td>\n","      <td>0.037700</td>\n","    </tr>\n","    <tr>\n","      <td>2820</td>\n","      <td>0.039700</td>\n","    </tr>\n","    <tr>\n","      <td>2830</td>\n","      <td>0.037100</td>\n","    </tr>\n","    <tr>\n","      <td>2840</td>\n","      <td>0.040100</td>\n","    </tr>\n","    <tr>\n","      <td>2850</td>\n","      <td>0.051200</td>\n","    </tr>\n","    <tr>\n","      <td>2860</td>\n","      <td>0.039500</td>\n","    </tr>\n","    <tr>\n","      <td>2870</td>\n","      <td>0.041100</td>\n","    </tr>\n","    <tr>\n","      <td>2880</td>\n","      <td>0.040900</td>\n","    </tr>\n","    <tr>\n","      <td>2890</td>\n","      <td>0.047500</td>\n","    </tr>\n","    <tr>\n","      <td>2900</td>\n","      <td>0.037800</td>\n","    </tr>\n","    <tr>\n","      <td>2910</td>\n","      <td>0.035500</td>\n","    </tr>\n","    <tr>\n","      <td>2920</td>\n","      <td>0.046200</td>\n","    </tr>\n","    <tr>\n","      <td>2930</td>\n","      <td>0.040400</td>\n","    </tr>\n","    <tr>\n","      <td>2940</td>\n","      <td>0.037600</td>\n","    </tr>\n","    <tr>\n","      <td>2950</td>\n","      <td>0.036200</td>\n","    </tr>\n","    <tr>\n","      <td>2960</td>\n","      <td>0.036800</td>\n","    </tr>\n","    <tr>\n","      <td>2970</td>\n","      <td>0.035600</td>\n","    </tr>\n","    <tr>\n","      <td>2980</td>\n","      <td>0.036600</td>\n","    </tr>\n","    <tr>\n","      <td>2990</td>\n","      <td>0.037600</td>\n","    </tr>\n","    <tr>\n","      <td>3000</td>\n","      <td>0.036400</td>\n","    </tr>\n","    <tr>\n","      <td>3010</td>\n","      <td>0.033800</td>\n","    </tr>\n","    <tr>\n","      <td>3020</td>\n","      <td>0.038500</td>\n","    </tr>\n","    <tr>\n","      <td>3030</td>\n","      <td>0.055100</td>\n","    </tr>\n","    <tr>\n","      <td>3040</td>\n","      <td>0.034700</td>\n","    </tr>\n","    <tr>\n","      <td>3050</td>\n","      <td>0.035000</td>\n","    </tr>\n","    <tr>\n","      <td>3060</td>\n","      <td>0.033400</td>\n","    </tr>\n","    <tr>\n","      <td>3070</td>\n","      <td>0.034900</td>\n","    </tr>\n","    <tr>\n","      <td>3080</td>\n","      <td>0.034200</td>\n","    </tr>\n","    <tr>\n","      <td>3090</td>\n","      <td>0.034600</td>\n","    </tr>\n","    <tr>\n","      <td>3100</td>\n","      <td>0.030500</td>\n","    </tr>\n","    <tr>\n","      <td>3110</td>\n","      <td>0.032800</td>\n","    </tr>\n","    <tr>\n","      <td>3120</td>\n","      <td>0.033300</td>\n","    </tr>\n","    <tr>\n","      <td>3130</td>\n","      <td>0.034400</td>\n","    </tr>\n","    <tr>\n","      <td>3140</td>\n","      <td>0.047700</td>\n","    </tr>\n","    <tr>\n","      <td>3150</td>\n","      <td>0.031000</td>\n","    </tr>\n","    <tr>\n","      <td>3160</td>\n","      <td>0.033500</td>\n","    </tr>\n","    <tr>\n","      <td>3170</td>\n","      <td>0.032200</td>\n","    </tr>\n","    <tr>\n","      <td>3180</td>\n","      <td>0.030100</td>\n","    </tr>\n","    <tr>\n","      <td>3190</td>\n","      <td>0.035100</td>\n","    </tr>\n","    <tr>\n","      <td>3200</td>\n","      <td>0.033300</td>\n","    </tr>\n","    <tr>\n","      <td>3210</td>\n","      <td>0.030900</td>\n","    </tr>\n","    <tr>\n","      <td>3220</td>\n","      <td>0.031700</td>\n","    </tr>\n","    <tr>\n","      <td>3230</td>\n","      <td>0.030600</td>\n","    </tr>\n","    <tr>\n","      <td>3240</td>\n","      <td>0.030300</td>\n","    </tr>\n","    <tr>\n","      <td>3250</td>\n","      <td>0.031000</td>\n","    </tr>\n","    <tr>\n","      <td>3260</td>\n","      <td>0.034200</td>\n","    </tr>\n","    <tr>\n","      <td>3270</td>\n","      <td>0.030900</td>\n","    </tr>\n","    <tr>\n","      <td>3280</td>\n","      <td>0.031300</td>\n","    </tr>\n","    <tr>\n","      <td>3290</td>\n","      <td>0.043800</td>\n","    </tr>\n","    <tr>\n","      <td>3300</td>\n","      <td>0.027500</td>\n","    </tr>\n","    <tr>\n","      <td>3310</td>\n","      <td>0.028700</td>\n","    </tr>\n","    <tr>\n","      <td>3320</td>\n","      <td>0.034000</td>\n","    </tr>\n","    <tr>\n","      <td>3330</td>\n","      <td>0.029100</td>\n","    </tr>\n","    <tr>\n","      <td>3340</td>\n","      <td>0.026700</td>\n","    </tr>\n","    <tr>\n","      <td>3350</td>\n","      <td>0.029200</td>\n","    </tr>\n","    <tr>\n","      <td>3360</td>\n","      <td>0.029800</td>\n","    </tr>\n","    <tr>\n","      <td>3370</td>\n","      <td>0.033200</td>\n","    </tr>\n","    <tr>\n","      <td>3380</td>\n","      <td>0.026500</td>\n","    </tr>\n","    <tr>\n","      <td>3390</td>\n","      <td>0.029100</td>\n","    </tr>\n","    <tr>\n","      <td>3400</td>\n","      <td>0.026000</td>\n","    </tr>\n","    <tr>\n","      <td>3410</td>\n","      <td>0.024900</td>\n","    </tr>\n","    <tr>\n","      <td>3420</td>\n","      <td>0.027900</td>\n","    </tr>\n","    <tr>\n","      <td>3430</td>\n","      <td>0.026500</td>\n","    </tr>\n","    <tr>\n","      <td>3440</td>\n","      <td>0.028500</td>\n","    </tr>\n","    <tr>\n","      <td>3450</td>\n","      <td>0.025800</td>\n","    </tr>\n","    <tr>\n","      <td>3460</td>\n","      <td>0.026000</td>\n","    </tr>\n","    <tr>\n","      <td>3470</td>\n","      <td>0.026600</td>\n","    </tr>\n","    <tr>\n","      <td>3480</td>\n","      <td>0.027800</td>\n","    </tr>\n","    <tr>\n","      <td>3490</td>\n","      <td>0.027200</td>\n","    </tr>\n","    <tr>\n","      <td>3500</td>\n","      <td>0.048800</td>\n","    </tr>\n","    <tr>\n","      <td>3510</td>\n","      <td>0.024400</td>\n","    </tr>\n","    <tr>\n","      <td>3520</td>\n","      <td>0.024000</td>\n","    </tr>\n","    <tr>\n","      <td>3530</td>\n","      <td>0.027300</td>\n","    </tr>\n","    <tr>\n","      <td>3540</td>\n","      <td>0.026000</td>\n","    </tr>\n","    <tr>\n","      <td>3550</td>\n","      <td>0.026000</td>\n","    </tr>\n","    <tr>\n","      <td>3560</td>\n","      <td>0.025300</td>\n","    </tr>\n","    <tr>\n","      <td>3570</td>\n","      <td>0.027200</td>\n","    </tr>\n","    <tr>\n","      <td>3580</td>\n","      <td>0.024600</td>\n","    </tr>\n","    <tr>\n","      <td>3590</td>\n","      <td>0.026800</td>\n","    </tr>\n","    <tr>\n","      <td>3600</td>\n","      <td>0.025900</td>\n","    </tr>\n","    <tr>\n","      <td>3610</td>\n","      <td>0.022600</td>\n","    </tr>\n","    <tr>\n","      <td>3620</td>\n","      <td>0.028000</td>\n","    </tr>\n","    <tr>\n","      <td>3630</td>\n","      <td>0.025600</td>\n","    </tr>\n","    <tr>\n","      <td>3640</td>\n","      <td>0.067700</td>\n","    </tr>\n","    <tr>\n","      <td>3650</td>\n","      <td>0.028100</td>\n","    </tr>\n","    <tr>\n","      <td>3660</td>\n","      <td>0.027400</td>\n","    </tr>\n","    <tr>\n","      <td>3670</td>\n","      <td>0.022700</td>\n","    </tr>\n","    <tr>\n","      <td>3680</td>\n","      <td>0.022400</td>\n","    </tr>\n","    <tr>\n","      <td>3690</td>\n","      <td>0.025300</td>\n","    </tr>\n","    <tr>\n","      <td>3700</td>\n","      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<tr>\n","      <td>21160</td>\n","      <td>0.003200</td>\n","    </tr>\n","    <tr>\n","      <td>21170</td>\n","      <td>0.017200</td>\n","    </tr>\n","    <tr>\n","      <td>21180</td>\n","      <td>0.001300</td>\n","    </tr>\n","    <tr>\n","      <td>21190</td>\n","      <td>0.001400</td>\n","    </tr>\n","    <tr>\n","      <td>21200</td>\n","      <td>0.001300</td>\n","    </tr>\n","    <tr>\n","      <td>21210</td>\n","      <td>0.001300</td>\n","    </tr>\n","    <tr>\n","      <td>21220</td>\n","      <td>0.001100</td>\n","    </tr>\n","    <tr>\n","      <td>21230</td>\n","      <td>0.032300</td>\n","    </tr>\n","    <tr>\n","      <td>21240</td>\n","      <td>0.001100</td>\n","    </tr>\n","    <tr>\n","      <td>21250</td>\n","      <td>0.001500</td>\n","    </tr>\n","    <tr>\n","      <td>21260</td>\n","      <td>0.001400</td>\n","    </tr>\n","    <tr>\n","      <td>21270</td>\n","      <td>0.047200</td>\n","    </tr>\n","    <tr>\n","      <td>21280</td>\n","      <td>0.000900</td>\n","    </tr>\n","    <tr>\n","      <td>21290</td>\n","      <td>0.001000</td>\n","    </tr>\n","    <tr>\n","      <td>21300</td>\n","      <td>0.001900</td>\n","    </tr>\n","    <tr>\n","      <td>21310</td>\n","      <td>0.001400</td>\n","    </tr>\n","    <tr>\n","      <td>21320</td>\n","      <td>0.003200</td>\n","    </tr>\n","    <tr>\n","      <td>21330</td>\n","      <td>0.001300</td>\n","    </tr>\n","    <tr>\n","      <td>21340</td>\n","      <td>0.001100</td>\n","    </tr>\n","    <tr>\n","      <td>21350</td>\n","      <td>0.001300</td>\n","    </tr>\n","    <tr>\n","      <td>21360</td>\n","      <td>0.004400</td>\n","    </tr>\n","    <tr>\n","      <td>21370</td>\n","      <td>0.025600</td>\n","    </tr>\n","    <tr>\n","      <td>21380</td>\n","      <td>0.001400</td>\n","    </tr>\n","    <tr>\n","      <td>21390</td>\n","      <td>0.004000</td>\n","    </tr>\n","    <tr>\n","      <td>21400</td>\n","      <td>0.001300</td>\n","    </tr>\n","    <tr>\n","      <td>21410</td>\n","      <td>0.001800</td>\n","    </tr>\n","    <tr>\n","      <td>21420</td>\n","      <td>0.017300</td>\n","    </tr>\n","    <tr>\n","      <td>21430</td>\n","      <td>0.001200</td>\n","    </tr>\n","    <tr>\n","      <td>21440</td>\n","      <td>0.001600</td>\n","    </tr>\n","    <tr>\n","      <td>21450</td>\n","      <td>0.001200</td>\n","    </tr>\n","    <tr>\n","      <td>21460</td>\n","      <td>0.001200</td>\n","    </tr>\n","    <tr>\n","      <td>21470</td>\n","      <td>0.013500</td>\n","    </tr>\n","    <tr>\n","      <td>21480</td>\n","      <td>0.040900</td>\n","    </tr>\n","    <tr>\n","      <td>21490</td>\n","      <td>0.001500</td>\n","    </tr>\n","    <tr>\n","      <td>21500</td>\n","      <td>0.001200</td>\n","    </tr>\n","    <tr>\n","      <td>21510</td>\n","      <td>0.001400</td>\n","    </tr>\n","    <tr>\n","      <td>21520</td>\n","      <td>0.001400</td>\n","    </tr>\n","    <tr>\n","      <td>21530</td>\n","      <td>0.001200</td>\n","    </tr>\n","    <tr>\n","      <td>21540</td>\n","      <td>0.001200</td>\n","    </tr>\n","    <tr>\n","      <td>21550</td>\n","      <td>0.002300</td>\n","    </tr>\n","    <tr>\n","      <td>21560</td>\n","      <td>0.001400</td>\n","    </tr>\n","    <tr>\n","      <td>21570</td>\n","      <td>0.001400</td>\n","    </tr>\n","    <tr>\n","      <td>21580</td>\n","      <td>0.001000</td>\n","    </tr>\n","    <tr>\n","      <td>21590</td>\n","      <td>0.007000</td>\n","    </tr>\n","    <tr>\n","      <td>21600</td>\n","      <td>0.001200</td>\n","    </tr>\n","    <tr>\n","      <td>21610</td>\n","      <td>0.001500</td>\n","    </tr>\n","    <tr>\n","      <td>21620</td>\n","      <td>0.019100</td>\n","    </tr>\n","    <tr>\n","      <td>21630</td>\n","      <td>0.001400</td>\n","    </tr>\n","    <tr>\n","      <td>21640</td>\n","      <td>0.001300</td>\n","    </tr>\n","    <tr>\n","      <td>21650</td>\n","      <td>0.002300</td>\n","    </tr>\n","    <tr>\n","      <td>21660</td>\n","      <td>0.001400</td>\n","    </tr>\n","    <tr>\n","      <td>21670</td>\n","      <td>0.001300</td>\n","    </tr>\n","    <tr>\n","      <td>21680</td>\n","      <td>0.002200</td>\n","    </tr>\n","    <tr>\n","      <td>21690</td>\n","      <td>0.001400</td>\n","    </tr>\n","    <tr>\n","      <td>21700</td>\n","      <td>0.001200</td>\n","    </tr>\n","    <tr>\n","      <td>21710</td>\n","      <td>0.001400</td>\n","    </tr>\n","    <tr>\n","      <td>21720</td>\n","      <td>0.001800</td>\n","    </tr>\n","    <tr>\n","      <td>21730</td>\n","      <td>0.001200</td>\n","    </tr>\n","    <tr>\n","      <td>21740</td>\n","      <td>0.001800</td>\n","    </tr>\n","    <tr>\n","      <td>21750</td>\n","      <td>0.001500</td>\n","    </tr>\n","    <tr>\n","      <td>21760</td>\n","      <td>0.001600</td>\n","    </tr>\n","    <tr>\n","      <td>21770</td>\n","      <td>0.001400</td>\n","    </tr>\n","    <tr>\n","      <td>21780</td>\n","      <td>0.001100</td>\n","    </tr>\n","    <tr>\n","      <td>21790</td>\n","      <td>0.001900</td>\n","    </tr>\n","    <tr>\n","      <td>21800</td>\n","      <td>0.001400</td>\n","    </tr>\n","    <tr>\n","      <td>21810</td>\n","      <td>0.001300</td>\n","    </tr>\n","    <tr>\n","      <td>21820</td>\n","      <td>0.001600</td>\n","    </tr>\n","    <tr>\n","      <td>21830</td>\n","      <td>0.001600</td>\n","    </tr>\n","    <tr>\n","      <td>21840</td>\n","      <td>0.001300</td>\n","    </tr>\n","    <tr>\n","      <td>21850</td>\n","      <td>0.001300</td>\n","    </tr>\n","    <tr>\n","      <td>21860</td>\n","      <td>0.001100</td>\n","    </tr>\n","    <tr>\n","      <td>21870</td>\n","      <td>0.001500</td>\n","    </tr>\n","    <tr>\n","      <td>21880</td>\n","      <td>0.001200</td>\n","    </tr>\n","    <tr>\n","      <td>21890</td>\n","      <td>0.001300</td>\n","    </tr>\n","    <tr>\n","      <td>21900</td>\n","      <td>0.013800</td>\n","    </tr>\n","    <tr>\n","      <td>21910</td>\n","      <td>0.001400</td>\n","    </tr>\n","    <tr>\n","      <td>21920</td>\n","      <td>0.001300</td>\n","    </tr>\n","    <tr>\n","      <td>21930</td>\n","      <td>0.001300</td>\n","    </tr>\n","    <tr>\n","      <td>21940</td>\n","      <td>0.001200</td>\n","    </tr>\n","    <tr>\n","      <td>21950</td>\n","      <td>0.046100</td>\n","    </tr>\n","    <tr>\n","      <td>21960</td>\n","      <td>0.001500</td>\n","    </tr>\n","    <tr>\n","      <td>21970</td>\n","      <td>0.003400</td>\n","    </tr>\n","    <tr>\n","      <td>21980</td>\n","      <td>0.001300</td>\n","    </tr>\n","    <tr>\n","      <td>21990</td>\n","      <td>0.001200</td>\n","    </tr>\n","    <tr>\n","      <td>22000</td>\n","      <td>0.001100</td>\n","    </tr>\n","    <tr>\n","      <td>22010</td>\n","      <td>0.001300</td>\n","    </tr>\n","    <tr>\n","      <td>22020</td>\n","      <td>0.001500</td>\n","    </tr>\n","    <tr>\n","      <td>22030</td>\n","      <td>0.001300</td>\n","    </tr>\n","    <tr>\n","      <td>22040</td>\n","      <td>0.001300</td>\n","    </tr>\n","    <tr>\n","      <td>22050</td>\n","      <td>0.001600</td>\n","    </tr>\n","    <tr>\n","      <td>22060</td>\n","      <td>0.001600</td>\n","    </tr>\n","    <tr>\n","      <td>22070</td>\n","      <td>0.003000</td>\n","    </tr>\n","    <tr>\n","      <td>22080</td>\n","      <td>0.001400</td>\n","    </tr>\n","    <tr>\n","      <td>22090</td>\n","      <td>0.001500</td>\n","    </tr>\n","    <tr>\n","      <td>22100</td>\n","      <td>0.001700</td>\n","    </tr>\n","    <tr>\n","      <td>22110</td>\n","      <td>0.001700</td>\n","    </tr>\n","    <tr>\n","      <td>22120</td>\n","      <td>0.001700</td>\n","    </tr>\n","    <tr>\n","      <td>22130</td>\n","      <td>0.001400</td>\n","    </tr>\n","    <tr>\n","      <td>22140</td>\n","      <td>0.001400</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=22145, training_loss=0.03263004273605161, metrics={'train_runtime': 4978.6797, 'train_samples_per_second': 4.448, 'total_flos': 88910428149525264, 'epoch': 1.0})"]},"metadata":{},"execution_count":28}]},{"cell_type":"markdown","metadata":{"id":"P4Gv5XV07l2J"},"source":["## Train classifier task 00 - bert-base-uncased-2epoch-LR5e-5"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"K-bgKSsq5OOJ","executionInfo":{"status":"ok","timestamp":1638253615613,"user_tz":480,"elapsed":9944551,"user":{"displayName":"zeeshan ahmad","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15348670853435150367"}},"outputId":"e13ffc28-6edb-4065-a0c1-87efce157d2a"},"source":["training_args = TrainingArguments(\n","    output_dir = data_folder+'Model_TwiBot_Similarity_With_Metadata_360k_Tweets/Classifier/ \\\n","                            twiBot-360k-similarity-metadata-2epoch_LR5e-5_bert_base_uncased/results',          # output directory\n","    num_train_epochs = 2,              # total number of training epochs\n","    per_device_train_batch_size = 16,  # batch size per device during training\n","    per_device_eval_batch_size = 64,   # batch size for evaluation\n","    warmup_steps = 500,                # number of warmup steps for learning rate scheduler\n","    weight_decay = 0.01,               # strength of weight decay\n","    logging_dir = data_folder+'Model_TwiBot_Similarity_With_Metadata_360k_Tweets/Classifier/ \\\n","                            twiBot-360k-similarity-metadata-2epoch_LR5e-5_bert_base_uncased/logs',            # directory for storing logs\n","    logging_steps=200,\n",")\n","\n","trainer = Trainer(\n","    model=model_twitter_classifier,       # the instantiated 🤗 Transformers model to be trained\n","    args=training_args,                   # training arguments, defined above\n","    train_dataset=train_dataset           # training dataset\n","    # eval_dataset=val_dataset            # evaluation dataset\n",")\n","\n","trainer.train()"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/_distutils_hack/__init__.py:19: UserWarning: Distutils was imported before Setuptools. This usage is discouraged and may exhibit undesirable behaviors or errors. Please use Setuptools' objects directly or at least import Setuptools first.\n","  \"Distutils was imported before Setuptools. This usage is discouraged \"\n"]},{"output_type":"display_data","data":{"text/html":["\n","    <div>\n","        <style>\n","            /* Turns off some styling */\n","            progress {\n","                /* gets rid of default border in Firefox and Opera. */\n","                border: none;\n","                /* Needs to be in here for Safari polyfill so background images work as expected. */\n","                background-size: auto;\n","            }\n","        </style>\n","      \n","      <progress value='44290' max='44290' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [44290/44290 2:45:29, Epoch 2/2]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>200</td>\n","      <td>0.673100</td>\n","    </tr>\n","    <tr>\n","      <td>400</td>\n","      <td>0.583400</td>\n","    </tr>\n","    <tr>\n","      <td>600</td>\n","      <td>0.440600</td>\n","    </tr>\n","    <tr>\n","      <td>800</td>\n","      <td>0.305000</td>\n","    </tr>\n","    <tr>\n","      <td>1000</td>\n","      <td>0.222700</td>\n","    </tr>\n","    <tr>\n","      <td>1200</td>\n","      <td>0.171400</td>\n","    </tr>\n","    <tr>\n","      <td>1400</td>\n","      <td>0.137900</td>\n","    </tr>\n","    <tr>\n","      <td>1600</td>\n","      <td>0.113100</td>\n","    </tr>\n","    <tr>\n","      <td>1800</td>\n","      <td>0.094100</td>\n","    </tr>\n","    <tr>\n","      <td>2000</td>\n","      <td>0.078400</td>\n","    </tr>\n","    <tr>\n","      <td>2200</td>\n","      <td>0.070300</td>\n","    </tr>\n","    <tr>\n","      <td>2400</td>\n","      <td>0.057700</td>\n","    </tr>\n","    <tr>\n","      <td>2600</td>\n","      <td>0.053200</td>\n","    </tr>\n","    <tr>\n","      <td>2800</td>\n","      <td>0.049400</td>\n","    </tr>\n","    <tr>\n","      <td>3000</td>\n","      <td>0.046000</td>\n","    </tr>\n","    <tr>\n","      <td>3200</td>\n","      <td>0.037400</td>\n","    </tr>\n","    <tr>\n","      <td>3400</td>\n","      <td>0.035400</td>\n","    </tr>\n","    <tr>\n","      <td>3600</td>\n","      <td>0.033800</td>\n","    </tr>\n","    <tr>\n","      <td>3800</td>\n","      <td>0.027100</td>\n","    </tr>\n","    <tr>\n","      <td>4000</td>\n","      <td>0.024800</td>\n","    </tr>\n","    <tr>\n","      <td>4200</td>\n","      <td>0.023900</td>\n","    </tr>\n","    <tr>\n","      <td>4400</td>\n","      <td>0.020100</td>\n","    </tr>\n","    <tr>\n","      <td>4600</td>\n","      <td>0.018200</td>\n","    </tr>\n","    <tr>\n","      <td>4800</td>\n","      <td>0.016100</td>\n","    </tr>\n","    <tr>\n","      <td>5000</td>\n","      <td>0.016900</td>\n","    </tr>\n","    <tr>\n","      <td>5200</td>\n","      <td>0.015000</td>\n","    </tr>\n","    <tr>\n","      <td>5400</td>\n","      <td>0.017400</td>\n","    </tr>\n","    <tr>\n","      <td>5600</td>\n","      <td>0.014100</td>\n","    </tr>\n","    <tr>\n","      <td>5800</td>\n","      <td>0.012900</td>\n","    </tr>\n","    <tr>\n","      <td>6000</td>\n","      <td>0.014100</td>\n","    </tr>\n","    <tr>\n","      <td>6200</td>\n","      <td>0.012500</td>\n","    </tr>\n","    <tr>\n","      <td>6400</td>\n","      <td>0.011300</td>\n","    </tr>\n","    <tr>\n","      <td>6600</td>\n","      <td>0.009300</td>\n","    </tr>\n","    <tr>\n","      <td>6800</td>\n","      <td>0.012100</td>\n","    </tr>\n","    <tr>\n","      <td>7000</td>\n","      <td>0.010800</td>\n","    </tr>\n","    <tr>\n","      <td>7200</td>\n","      <td>0.007400</td>\n","    </tr>\n","    <tr>\n","      <td>7400</td>\n","      <td>0.010300</td>\n","    </tr>\n","    <tr>\n","      <td>7600</td>\n","      <td>0.008600</td>\n","    </tr>\n","    <tr>\n","      <td>7800</td>\n","      <td>0.007500</td>\n","    </tr>\n","    <tr>\n","      <td>8000</td>\n","      <td>0.009900</td>\n","    </tr>\n","    <tr>\n","      <td>8200</td>\n","      <td>0.008500</td>\n","    </tr>\n","    <tr>\n","      <td>8400</td>\n","      <td>0.009300</td>\n","    </tr>\n","    <tr>\n","      <td>8600</td>\n","      <td>0.010800</td>\n","    </tr>\n","    <tr>\n","      <td>8800</td>\n","      <td>0.006800</td>\n","    </tr>\n","    <tr>\n","      <td>9000</td>\n","      <td>0.008300</td>\n","    </tr>\n","    <tr>\n","      <td>9200</td>\n","      <td>0.008700</td>\n","    </tr>\n","    <tr>\n","      <td>9400</td>\n","      <td>0.008700</td>\n","    </tr>\n","    <tr>\n","      <td>9600</td>\n","      <td>0.006500</td>\n","    </tr>\n","    <tr>\n","      <td>9800</td>\n","      <td>0.005600</td>\n","    </tr>\n","    <tr>\n","      <td>10000</td>\n","      <td>0.012400</td>\n","    </tr>\n","    <tr>\n","      <td>10200</td>\n","      <td>0.007800</td>\n","    </tr>\n","    <tr>\n","      <td>10400</td>\n","      <td>0.011000</td>\n","    </tr>\n","    <tr>\n","      <td>10600</td>\n","      <td>0.010200</td>\n","    </tr>\n","    <tr>\n","      <td>10800</td>\n","      <td>0.009500</td>\n","    </tr>\n","    <tr>\n","      <td>11000</td>\n","      <td>0.007500</td>\n","    </tr>\n","    <tr>\n","      <td>11200</td>\n","      <td>0.009800</td>\n","    </tr>\n","    <tr>\n","      <td>11400</td>\n","      <td>0.008500</td>\n","    </tr>\n","    <tr>\n","      <td>11600</td>\n","      <td>0.006100</td>\n","    </tr>\n","    <tr>\n","      <td>11800</td>\n","      <td>0.005900</td>\n","    </tr>\n","    <tr>\n","      <td>12000</td>\n","      <td>0.007900</td>\n","    </tr>\n","    <tr>\n","      <td>12200</td>\n","      <td>0.011900</td>\n","    </tr>\n","    <tr>\n","      <td>12400</td>\n","      <td>0.006900</td>\n","    </tr>\n","    <tr>\n","      <td>12600</td>\n","      <td>0.006500</td>\n","    </tr>\n","    <tr>\n","      <td>12800</td>\n","      <td>0.006800</td>\n","    </tr>\n","    <tr>\n","      <td>13000</td>\n","      <td>0.004400</td>\n","    </tr>\n","    <tr>\n","      <td>13200</td>\n","      <td>0.004600</td>\n","    </tr>\n","    <tr>\n","      <td>13400</td>\n","      <td>0.009000</td>\n","    </tr>\n","    <tr>\n","      <td>13600</td>\n","      <td>0.009100</td>\n","    </tr>\n","    <tr>\n","      <td>13800</td>\n","      <td>0.004600</td>\n","    </tr>\n","    <tr>\n","      <td>14000</td>\n","      <td>0.005200</td>\n","    </tr>\n","    <tr>\n","      <td>14200</td>\n","      <td>0.005900</td>\n","    </tr>\n","    <tr>\n","      <td>14400</td>\n","      <td>0.004000</td>\n","    </tr>\n","    <tr>\n","      <td>14600</td>\n","      <td>0.004600</td>\n","    </tr>\n","    <tr>\n","      <td>14800</td>\n","      <td>0.005000</td>\n","    </tr>\n","    <tr>\n","      <td>15000</td>\n","      <td>0.003700</td>\n","    </tr>\n","    <tr>\n","      <td>15200</td>\n","      <td>0.006900</td>\n","    </tr>\n","    <tr>\n","      <td>15400</td>\n","      <td>0.008300</td>\n","    </tr>\n","    <tr>\n","      <td>15600</td>\n","      <td>0.006500</td>\n","    </tr>\n","    <tr>\n","      <td>15800</td>\n","      <td>0.004000</td>\n","    </tr>\n","    <tr>\n","      <td>16000</td>\n","      <td>0.006300</td>\n","    </tr>\n","    <tr>\n","      <td>16200</td>\n","      <td>0.006000</td>\n","    </tr>\n","    <tr>\n","      <td>16400</td>\n","      <td>0.011900</td>\n","    </tr>\n","    <tr>\n","      <td>16600</td>\n","      <td>0.004600</td>\n","    </tr>\n","    <tr>\n","      <td>16800</td>\n","      <td>0.008700</td>\n","    </tr>\n","    <tr>\n","      <td>17000</td>\n","      <td>0.006800</td>\n","    </tr>\n","    <tr>\n","      <td>17200</td>\n","      <td>0.004300</td>\n","    </tr>\n","    <tr>\n","      <td>17400</td>\n","      <td>0.004200</td>\n","    </tr>\n","    <tr>\n","      <td>17600</td>\n","      <td>0.003700</td>\n","    </tr>\n","    <tr>\n","      <td>17800</td>\n","      <td>0.006200</td>\n","    </tr>\n","    <tr>\n","      <td>18000</td>\n","      <td>0.009200</td>\n","    </tr>\n","    <tr>\n","      <td>18200</td>\n","      <td>0.006600</td>\n","    </tr>\n","    <tr>\n","      <td>18400</td>\n","      <td>0.003800</td>\n","    </tr>\n","    <tr>\n","      <td>18600</td>\n","      <td>0.003900</td>\n","    </tr>\n","    <tr>\n","      <td>18800</td>\n","      <td>0.006900</td>\n","    </tr>\n","    <tr>\n","      <td>19000</td>\n","      <td>0.004000</td>\n","    </tr>\n","    <tr>\n","      <td>19200</td>\n","      <td>0.003600</td>\n","    </tr>\n","    <tr>\n","      <td>19400</td>\n","      <td>0.006700</td>\n","    </tr>\n","    <tr>\n","      <td>19600</td>\n","      <td>0.002300</td>\n","    </tr>\n","    <tr>\n","      <td>19800</td>\n","      <td>0.005700</td>\n","    </tr>\n","    <tr>\n","      <td>20000</td>\n","      <td>0.010300</td>\n","    </tr>\n","    <tr>\n","      <td>20200</td>\n","      <td>0.006100</td>\n","    </tr>\n","    <tr>\n","      <td>20400</td>\n","      <td>0.002000</td>\n","    </tr>\n","    <tr>\n","      <td>20600</td>\n","      <td>0.010000</td>\n","    </tr>\n","    <tr>\n","      <td>20800</td>\n","      <td>0.009500</td>\n","    </tr>\n","    <tr>\n","      <td>21000</td>\n","      <td>0.007600</td>\n","    </tr>\n","    <tr>\n","      <td>21200</td>\n","      <td>0.004800</td>\n","    </tr>\n","    <tr>\n","      <td>21400</td>\n","      <td>0.006200</td>\n","    </tr>\n","    <tr>\n","      <td>21600</td>\n","      <td>0.006500</td>\n","    </tr>\n","    <tr>\n","      <td>21800</td>\n","      <td>0.008000</td>\n","    </tr>\n","    <tr>\n","      <td>22000</td>\n","      <td>0.006100</td>\n","    </tr>\n","    <tr>\n","      <td>22200</td>\n","      <td>0.005200</td>\n","    </tr>\n","    <tr>\n","      <td>22400</td>\n","      <td>0.003800</td>\n","    </tr>\n","    <tr>\n","      <td>22600</td>\n","      <td>0.008800</td>\n","    </tr>\n","    <tr>\n","      <td>22800</td>\n","      <td>0.001800</td>\n","    </tr>\n","    <tr>\n","      <td>23000</td>\n","      <td>0.007400</td>\n","    </tr>\n","    <tr>\n","      <td>23200</td>\n","      <td>0.004300</td>\n","    </tr>\n","    <tr>\n","      <td>23400</td>\n","      <td>0.006200</td>\n","    </tr>\n","    <tr>\n","      <td>23600</td>\n","      <td>0.004700</td>\n","    </tr>\n","    <tr>\n","      <td>23800</td>\n","      <td>0.007400</td>\n","    </tr>\n","    <tr>\n","      <td>24000</td>\n","      <td>0.008500</td>\n","    </tr>\n","    <tr>\n","      <td>24200</td>\n","      <td>0.006500</td>\n","    </tr>\n","    <tr>\n","      <td>24400</td>\n","      <td>0.005600</td>\n","    </tr>\n","    <tr>\n","      <td>24600</td>\n","      <td>0.007000</td>\n","    </tr>\n","    <tr>\n","      <td>24800</td>\n","      <td>0.004700</td>\n","    </tr>\n","    <tr>\n","      <td>25000</td>\n","      <td>0.007300</td>\n","    </tr>\n","    <tr>\n","      <td>25200</td>\n","      <td>0.008100</td>\n","    </tr>\n","    <tr>\n","      <td>25400</td>\n","      <td>0.006000</td>\n","    </tr>\n","    <tr>\n","      <td>25600</td>\n","      <td>0.004600</td>\n","    </tr>\n","    <tr>\n","      <td>25800</td>\n","      <td>0.004900</td>\n","    </tr>\n","    <tr>\n","      <td>26000</td>\n","      <td>0.006700</td>\n","    </tr>\n","    <tr>\n","      <td>26200</td>\n","      <td>0.008400</td>\n","    </tr>\n","    <tr>\n","      <td>26400</td>\n","      <td>0.008400</td>\n","    </tr>\n","    <tr>\n","      <td>26600</td>\n","      <td>0.004600</td>\n","    </tr>\n","    <tr>\n","      <td>26800</td>\n","      <td>0.005700</td>\n","    </tr>\n","    <tr>\n","      <td>27000</td>\n","      <td>0.004700</td>\n","    </tr>\n","    <tr>\n","      <td>27200</td>\n","      <td>0.003300</td>\n","    </tr>\n","    <tr>\n","      <td>27400</td>\n","      <td>0.002500</td>\n","    </tr>\n","    <tr>\n","      <td>27600</td>\n","      <td>0.004900</td>\n","    </tr>\n","    <tr>\n","      <td>27800</td>\n","      <td>0.005700</td>\n","    </tr>\n","    <tr>\n","      <td>28000</td>\n","      <td>0.002600</td>\n","    </tr>\n","    <tr>\n","      <td>28200</td>\n","      <td>0.004700</td>\n","    </tr>\n","    <tr>\n","      <td>28400</td>\n","      <td>0.007400</td>\n","    </tr>\n","    <tr>\n","      <td>28600</td>\n","      <td>0.007300</td>\n","    </tr>\n","    <tr>\n","      <td>28800</td>\n","      <td>0.001500</td>\n","    </tr>\n","    <tr>\n","      <td>29000</td>\n","      <td>0.004700</td>\n","    </tr>\n","    <tr>\n","      <td>29200</td>\n","      <td>0.010700</td>\n","    </tr>\n","    <tr>\n","      <td>29400</td>\n","      <td>0.007600</td>\n","    </tr>\n","    <tr>\n","      <td>29600</td>\n","      <td>0.008200</td>\n","    </tr>\n","    <tr>\n","      <td>29800</td>\n","      <td>0.005900</td>\n","    </tr>\n","    <tr>\n","      <td>30000</td>\n","      <td>0.003100</td>\n","    </tr>\n","    <tr>\n","      <td>30200</td>\n","      <td>0.011200</td>\n","    </tr>\n","    <tr>\n","      <td>30400</td>\n","      <td>0.006900</td>\n","    </tr>\n","    <tr>\n","      <td>30600</td>\n","      <td>0.009500</td>\n","    </tr>\n","    <tr>\n","      <td>30800</td>\n","      <td>0.005100</td>\n","    </tr>\n","    <tr>\n","      <td>31000</td>\n","      <td>0.007300</td>\n","    </tr>\n","    <tr>\n","      <td>31200</td>\n","      <td>0.005600</td>\n","    </tr>\n","    <tr>\n","      <td>31400</td>\n","      <td>0.006900</td>\n","    </tr>\n","    <tr>\n","      <td>31600</td>\n","      <td>0.003600</td>\n","    </tr>\n","    <tr>\n","      <td>31800</td>\n","      <td>0.010800</td>\n","    </tr>\n","    <tr>\n","      <td>32000</td>\n","      <td>0.002200</td>\n","    </tr>\n","    <tr>\n","      <td>32200</td>\n","      <td>0.006600</td>\n","    </tr>\n","    <tr>\n","      <td>32400</td>\n","      <td>0.006200</td>\n","    </tr>\n","    <tr>\n","      <td>32600</td>\n","      <td>0.004500</td>\n","    </tr>\n","    <tr>\n","      <td>32800</td>\n","      <td>0.009900</td>\n","    </tr>\n","    <tr>\n","      <td>33000</td>\n","      <td>0.008600</td>\n","    </tr>\n","    <tr>\n","      <td>33200</td>\n","      <td>0.008200</td>\n","    </tr>\n","    <tr>\n","      <td>33400</td>\n","      <td>0.006400</td>\n","    </tr>\n","    <tr>\n","      <td>33600</td>\n","      <td>0.003500</td>\n","    </tr>\n","    <tr>\n","      <td>33800</td>\n","      <td>0.003700</td>\n","    </tr>\n","    <tr>\n","      <td>34000</td>\n","      <td>0.005200</td>\n","    </tr>\n","    <tr>\n","      <td>34200</td>\n","      <td>0.001500</td>\n","    </tr>\n","    <tr>\n","      <td>34400</td>\n","      <td>0.005000</td>\n","    </tr>\n","    <tr>\n","      <td>34600</td>\n","      <td>0.003700</td>\n","    </tr>\n","    <tr>\n","      <td>34800</td>\n","      <td>0.005400</td>\n","    </tr>\n","    <tr>\n","      <td>35000</td>\n","      <td>0.003900</td>\n","    </tr>\n","    <tr>\n","      <td>35200</td>\n","      <td>0.004700</td>\n","    </tr>\n","    <tr>\n","      <td>35400</td>\n","      <td>0.005600</td>\n","    </tr>\n","    <tr>\n","      <td>35600</td>\n","      <td>0.008200</td>\n","    </tr>\n","    <tr>\n","      <td>35800</td>\n","      <td>0.001500</td>\n","    </tr>\n","    <tr>\n","      <td>36000</td>\n","      <td>0.002100</td>\n","    </tr>\n","    <tr>\n","      <td>36200</td>\n","      <td>0.003100</td>\n","    </tr>\n","    <tr>\n","      <td>36400</td>\n","      <td>0.009700</td>\n","    </tr>\n","    <tr>\n","      <td>36600</td>\n","      <td>0.002400</td>\n","    </tr>\n","    <tr>\n","      <td>36800</td>\n","      <td>0.001900</td>\n","    </tr>\n","    <tr>\n","      <td>37000</td>\n","      <td>0.005700</td>\n","    </tr>\n","    <tr>\n","      <td>37200</td>\n","      <td>0.006700</td>\n","    </tr>\n","    <tr>\n","      <td>37400</td>\n","      <td>0.007900</td>\n","    </tr>\n","    <tr>\n","      <td>37600</td>\n","      <td>0.008400</td>\n","    </tr>\n","    <tr>\n","      <td>37800</td>\n","      <td>0.004600</td>\n","    </tr>\n","    <tr>\n","      <td>38000</td>\n","      <td>0.003700</td>\n","    </tr>\n","    <tr>\n","      <td>38200</td>\n","      <td>0.004100</td>\n","    </tr>\n","    <tr>\n","      <td>38400</td>\n","      <td>0.003900</td>\n","    </tr>\n","    <tr>\n","      <td>38600</td>\n","      <td>0.007600</td>\n","    </tr>\n","    <tr>\n","      <td>38800</td>\n","      <td>0.007600</td>\n","    </tr>\n","    <tr>\n","      <td>39000</td>\n","      <td>0.003700</td>\n","    </tr>\n","    <tr>\n","      <td>39200</td>\n","      <td>0.002600</td>\n","    </tr>\n","    <tr>\n","      <td>39400</td>\n","      <td>0.007700</td>\n","    </tr>\n","    <tr>\n","      <td>39600</td>\n","      <td>0.006600</td>\n","    </tr>\n","    <tr>\n","      <td>39800</td>\n","      <td>0.004900</td>\n","    </tr>\n","    <tr>\n","      <td>40000</td>\n","      <td>0.006800</td>\n","    </tr>\n","    <tr>\n","      <td>40200</td>\n","      <td>0.003500</td>\n","    </tr>\n","    <tr>\n","      <td>40400</td>\n","      <td>0.008500</td>\n","    </tr>\n","    <tr>\n","      <td>40600</td>\n","      <td>0.008600</td>\n","    </tr>\n","    <tr>\n","      <td>40800</td>\n","      <td>0.003800</td>\n","    </tr>\n","    <tr>\n","      <td>41000</td>\n","      <td>0.003000</td>\n","    </tr>\n","    <tr>\n","      <td>41200</td>\n","      <td>0.003800</td>\n","    </tr>\n","    <tr>\n","      <td>41400</td>\n","      <td>0.004200</td>\n","    </tr>\n","    <tr>\n","      <td>41600</td>\n","      <td>0.008900</td>\n","    </tr>\n","    <tr>\n","      <td>41800</td>\n","      <td>0.004900</td>\n","    </tr>\n","    <tr>\n","      <td>42000</td>\n","      <td>0.005000</td>\n","    </tr>\n","    <tr>\n","      <td>42200</td>\n","      <td>0.011300</td>\n","    </tr>\n","    <tr>\n","      <td>42400</td>\n","      <td>0.004600</td>\n","    </tr>\n","    <tr>\n","      <td>42600</td>\n","      <td>0.004100</td>\n","    </tr>\n","    <tr>\n","      <td>42800</td>\n","      <td>0.005700</td>\n","    </tr>\n","    <tr>\n","      <td>43000</td>\n","      <td>0.004900</td>\n","    </tr>\n","    <tr>\n","      <td>43200</td>\n","      <td>0.007700</td>\n","    </tr>\n","    <tr>\n","      <td>43400</td>\n","      <td>0.006100</td>\n","    </tr>\n","    <tr>\n","      <td>43600</td>\n","      <td>0.008500</td>\n","    </tr>\n","    <tr>\n","      <td>43800</td>\n","      <td>0.008900</td>\n","    </tr>\n","    <tr>\n","      <td>44000</td>\n","      <td>0.009600</td>\n","    </tr>\n","    <tr>\n","      <td>44200</td>\n","      <td>0.004000</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=44290, training_loss=0.020996497264966708, metrics={'train_runtime': 9930.1626, 'train_samples_per_second': 4.46, 'total_flos': 177820856299050528, 'epoch': 2.0})"]},"metadata":{},"execution_count":18}]},{"cell_type":"markdown","metadata":{"id":"ty1ww6KKUufE"},"source":["## Train classifier task 00 - sup-simcse-bert-base-uncased-1epoch-LR5e-5"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"upd0cjTmUVFR","executionInfo":{"status":"ok","timestamp":1638412814823,"user_tz":480,"elapsed":247185,"user":{"displayName":"zeeshan ahmad","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15348670853435150367"}},"outputId":"2ff48e20-c57b-430a-8aa2-b3a7d5e83425"},"source":["training_args = TrainingArguments(\n","    output_dir = data_folder+'Model_TwiBot_Similarity_With_Metadata_360k_Tweets/Classifier/twiBot-360k-similarity-metadata-1epoch_LR5e-5_sup-simcse-bert-base-uncased/results',          # output directory\n","    num_train_epochs = 1,              # total number of training epochs\n","    per_device_train_batch_size = 16,  # batch size per device during training\n","    per_device_eval_batch_size = 64,   # batch size for evaluation\n","    warmup_steps = 500,                # number of warmup steps for learning rate scheduler\n","    weight_decay = 0.01,               # strength of weight decay\n","    logging_dir = data_folder+'Model_TwiBot_Similarity_With_Metadata_360k_Tweets/Classifier/twiBot-360k-similarity-metadata-1epoch_LR5e-5_sup-simcse-bert-base-uncased/logs',            # directory for storing logs\n","    logging_steps=200,\n",")\n","\n","trainer = Trainer(\n","    model=model_twitter_classifier,       # the instantiated 🤗 Transformers model to be trained\n","    args=training_args,                   # training arguments, defined above\n","    train_dataset=train_dataset           # training dataset\n","    # eval_dataset=val_dataset            # evaluation dataset\n",")\n","\n","trainer.train()"],"execution_count":null,"outputs":[{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/_distutils_hack/__init__.py:19: UserWarning: Distutils was imported before Setuptools. This usage is discouraged and may exhibit undesirable behaviors or errors. Please use Setuptools' objects directly or at least import Setuptools first.\n","  \"Distutils was imported before Setuptools. This usage is discouraged \"\n"]},{"data":{"text/html":["\n","    <div>\n","        <style>\n","            /* Turns off some styling */\n","            progress {\n","                /* gets rid of default border in Firefox and Opera. */\n","                border: none;\n","                /* Needs to be in here for Safari polyfill so background images work as expected. */\n","                background-size: auto;\n","            }\n","        </style>\n","      \n","      <progress value='21041' max='22145' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [21041/22145 1:18:36 < 04:07, 4.46 it/s, Epoch 0.95/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>200</td>\n","      <td>0.693400</td>\n","    </tr>\n","    <tr>\n","      <td>400</td>\n","      <td>0.667600</td>\n","    </tr>\n","    <tr>\n","      <td>600</td>\n","      <td>0.619400</td>\n","    </tr>\n","    <tr>\n","      <td>800</td>\n","      <td>0.566900</td>\n","    </tr>\n","    <tr>\n","      <td>1000</td>\n","      <td>0.533500</td>\n","    </tr>\n","    <tr>\n","      <td>1200</td>\n","      <td>0.500500</td>\n","    </tr>\n","    <tr>\n","      <td>1400</td>\n","      <td>0.472700</td>\n","    </tr>\n","    <tr>\n","      <td>1600</td>\n","      <td>0.453600</td>\n","    </tr>\n","    <tr>\n","      <td>1800</td>\n","      <td>0.438200</td>\n","    </tr>\n","    <tr>\n","      <td>2000</td>\n","      <td>0.420900</td>\n","    </tr>\n","    <tr>\n","      <td>2200</td>\n","      <td>0.400200</td>\n","    </tr>\n","    <tr>\n","      <td>2400</td>\n","      <td>0.391300</td>\n","    </tr>\n","    <tr>\n","      <td>2600</td>\n","      <td>0.386700</td>\n","    </tr>\n","    <tr>\n","      <td>2800</td>\n","      <td>0.388700</td>\n","    </tr>\n","    <tr>\n","      <td>3000</td>\n","      <td>0.376900</td>\n","    </tr>\n","    <tr>\n","      <td>3200</td>\n","      <td>0.368800</td>\n","    </tr>\n","    <tr>\n","      <td>3400</td>\n","      <td>0.356400</td>\n","    </tr>\n","    <tr>\n","      <td>3600</td>\n","      <td>0.370600</td>\n","    </tr>\n","    <tr>\n","      <td>3800</td>\n","      <td>0.346900</td>\n","    </tr>\n","    <tr>\n","      <td>4000</td>\n","      <td>0.346200</td>\n","    </tr>\n","    <tr>\n","      <td>4200</td>\n","      <td>0.340400</td>\n","    </tr>\n","    <tr>\n","      <td>4400</td>\n","      <td>0.341900</td>\n","    </tr>\n","    <tr>\n","      <td>4600</td>\n","      <td>0.336800</td>\n","    </tr>\n","    <tr>\n","      <td>4800</td>\n","      <td>0.328200</td>\n","    </tr>\n","    <tr>\n","      <td>5000</td>\n","      <td>0.346800</td>\n","    </tr>\n","    <tr>\n","      <td>5200</td>\n","      <td>0.340800</td>\n","    </tr>\n","    <tr>\n","      <td>5400</td>\n","      <td>0.333900</td>\n","    </tr>\n","    <tr>\n","      <td>5600</td>\n","      <td>0.328800</td>\n","    </tr>\n","    <tr>\n","      <td>5800</td>\n","      <td>0.336800</td>\n","    </tr>\n","    <tr>\n","      <td>6000</td>\n","      <td>0.323500</td>\n","    </tr>\n","    <tr>\n","      <td>6200</td>\n","      <td>0.332000</td>\n","    </tr>\n","    <tr>\n","      <td>6400</td>\n","      <td>0.322600</td>\n","    </tr>\n","    <tr>\n","      <td>6600</td>\n","      <td>0.320200</td>\n","    </tr>\n","    <tr>\n","      <td>6800</td>\n","      <td>0.330200</td>\n","    </tr>\n","    <tr>\n","      <td>7000</td>\n","      <td>0.325900</td>\n","    </tr>\n","    <tr>\n","      <td>7200</td>\n","      <td>0.318200</td>\n","    </tr>\n","    <tr>\n","      <td>7400</td>\n","      <td>0.337100</td>\n","    </tr>\n","    <tr>\n","      <td>7600</td>\n","      <td>0.326800</td>\n","    </tr>\n","    <tr>\n","      <td>7800</td>\n","      <td>0.320200</td>\n","    </tr>\n","    <tr>\n","      <td>8000</td>\n","      <td>0.303100</td>\n","    </tr>\n","    <tr>\n","      <td>8200</td>\n","      <td>0.314500</td>\n","    </tr>\n","    <tr>\n","      <td>8400</td>\n","      <td>0.307300</td>\n","    </tr>\n","    <tr>\n","      <td>8600</td>\n","      <td>0.330300</td>\n","    </tr>\n","    <tr>\n","      <td>8800</td>\n","      <td>0.323500</td>\n","    </tr>\n","    <tr>\n","      <td>9000</td>\n","      <td>0.322200</td>\n","    </tr>\n","    <tr>\n","      <td>9200</td>\n","      <td>0.315200</td>\n","    </tr>\n","    <tr>\n","      <td>9400</td>\n","      <td>0.310800</td>\n","    </tr>\n","    <tr>\n","      <td>9600</td>\n","      <td>0.314100</td>\n","    </tr>\n","    <tr>\n","      <td>9800</td>\n","      <td>0.317000</td>\n","    </tr>\n","    <tr>\n","      <td>10000</td>\n","      <td>0.311200</td>\n","    </tr>\n","    <tr>\n","      <td>10200</td>\n","      <td>0.323000</td>\n","    </tr>\n","    <tr>\n","      <td>10400</td>\n","      <td>0.312800</td>\n","    </tr>\n","    <tr>\n","      <td>10600</td>\n","      <td>0.313200</td>\n","    </tr>\n","    <tr>\n","      <td>10800</td>\n","      <td>0.315100</td>\n","    </tr>\n","    <tr>\n","      <td>11000</td>\n","      <td>0.319900</td>\n","    </tr>\n","    <tr>\n","      <td>11200</td>\n","      <td>0.321700</td>\n","    </tr>\n","    <tr>\n","      <td>11400</td>\n","      <td>0.317500</td>\n","    </tr>\n","    <tr>\n","      <td>11600</td>\n","      <td>0.324800</td>\n","    </tr>\n","    <tr>\n","      <td>11800</td>\n","      <td>0.308600</td>\n","    </tr>\n","    <tr>\n","      <td>12000</td>\n","      <td>0.323500</td>\n","    </tr>\n","    <tr>\n","      <td>12200</td>\n","      <td>0.308900</td>\n","    </tr>\n","    <tr>\n","      <td>12400</td>\n","      <td>0.307100</td>\n","    </tr>\n","    <tr>\n","      <td>12600</td>\n","      <td>0.310700</td>\n","    </tr>\n","    <tr>\n","      <td>12800</td>\n","      <td>0.321300</td>\n","    </tr>\n","    <tr>\n","      <td>13000</td>\n","      <td>0.319700</td>\n","    </tr>\n","    <tr>\n","      <td>13200</td>\n","      <td>0.301200</td>\n","    </tr>\n","    <tr>\n","      <td>13400</td>\n","      <td>0.304600</td>\n","    </tr>\n","    <tr>\n","      <td>13600</td>\n","      <td>0.328500</td>\n","    </tr>\n","    <tr>\n","      <td>13800</td>\n","      <td>0.302900</td>\n","    </tr>\n","    <tr>\n","      <td>14000</td>\n","      <td>0.311500</td>\n","    </tr>\n","    <tr>\n","      <td>14200</td>\n","      <td>0.333500</td>\n","    </tr>\n","    <tr>\n","      <td>14400</td>\n","      <td>0.314200</td>\n","    </tr>\n","    <tr>\n","      <td>14600</td>\n","      <td>0.322600</td>\n","    </tr>\n","    <tr>\n","      <td>14800</td>\n","      <td>0.302400</td>\n","    </tr>\n","    <tr>\n","      <td>15000</td>\n","      <td>0.293600</td>\n","    </tr>\n","    <tr>\n","      <td>15200</td>\n","      <td>0.311400</td>\n","    </tr>\n","    <tr>\n","      <td>15400</td>\n","      <td>0.305800</td>\n","    </tr>\n","    <tr>\n","      <td>15600</td>\n","      <td>0.301700</td>\n","    </tr>\n","    <tr>\n","      <td>15800</td>\n","      <td>0.301800</td>\n","    </tr>\n","    <tr>\n","      <td>16000</td>\n","      <td>0.334100</td>\n","    </tr>\n","    <tr>\n","      <td>16200</td>\n","      <td>0.310100</td>\n","    </tr>\n","    <tr>\n","      <td>16400</td>\n","      <td>0.317800</td>\n","    </tr>\n","    <tr>\n","      <td>16600</td>\n","      <td>0.313300</td>\n","    </tr>\n","    <tr>\n","      <td>16800</td>\n","      <td>0.325200</td>\n","    </tr>\n","    <tr>\n","      <td>17000</td>\n","      <td>0.308400</td>\n","    </tr>\n","    <tr>\n","      <td>17200</td>\n","      <td>0.306200</td>\n","    </tr>\n","    <tr>\n","      <td>17400</td>\n","      <td>0.302700</td>\n","    </tr>\n","    <tr>\n","      <td>17600</td>\n","      <td>0.312500</td>\n","    </tr>\n","    <tr>\n","      <td>17800</td>\n","      <td>0.323000</td>\n","    </tr>\n","    <tr>\n","      <td>18000</td>\n","      <td>0.295900</td>\n","    </tr>\n","    <tr>\n","      <td>18200</td>\n","      <td>0.312900</td>\n","    </tr>\n","    <tr>\n","      <td>18400</td>\n","      <td>0.307200</td>\n","    </tr>\n","    <tr>\n","      <td>18600</td>\n","      <td>0.304200</td>\n","    </tr>\n","    <tr>\n","      <td>18800</td>\n","      <td>0.320400</td>\n","    </tr>\n","    <tr>\n","      <td>19000</td>\n","      <td>0.304000</td>\n","    </tr>\n","    <tr>\n","      <td>19200</td>\n","      <td>0.297300</td>\n","    </tr>\n","    <tr>\n","      <td>19400</td>\n","      <td>0.318500</td>\n","    </tr>\n","    <tr>\n","      <td>19600</td>\n","      <td>0.302800</td>\n","    </tr>\n","    <tr>\n","      <td>19800</td>\n","      <td>0.310900</td>\n","    </tr>\n","    <tr>\n","      <td>20000</td>\n","      <td>0.310800</td>\n","    </tr>\n","    <tr>\n","      <td>20200</td>\n","      <td>0.307100</td>\n","    </tr>\n","    <tr>\n","      <td>20400</td>\n","      <td>0.313700</td>\n","    </tr>\n","    <tr>\n","      <td>20600</td>\n","      <td>0.303700</td>\n","    </tr>\n","    <tr>\n","      <td>20800</td>\n","      <td>0.315900</td>\n","    </tr>\n","    <tr>\n","      <td>21000</td>\n","      <td>0.299500</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"output_type":"display_data","data":{"text/html":["\n","    <div>\n","        <style>\n","            /* Turns off some styling */\n","            progress {\n","                /* gets rid of default border in Firefox and Opera. */\n","                border: none;\n","                /* Needs to be in here for Safari polyfill so background images work as expected. */\n","                background-size: auto;\n","            }\n","        </style>\n","      \n","      <progress value='22145' max='22145' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [22145/22145 1:22:43, Epoch 1/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>200</td>\n","      <td>0.693400</td>\n","    </tr>\n","    <tr>\n","      <td>400</td>\n","      <td>0.667600</td>\n","    </tr>\n","    <tr>\n","      <td>600</td>\n","      <td>0.619400</td>\n","    </tr>\n","    <tr>\n","      <td>800</td>\n","      <td>0.566900</td>\n","    </tr>\n","    <tr>\n","      <td>1000</td>\n","      <td>0.533500</td>\n","    </tr>\n","    <tr>\n","      <td>1200</td>\n","      <td>0.500500</td>\n","    </tr>\n","    <tr>\n","      <td>1400</td>\n","      <td>0.472700</td>\n","    </tr>\n","    <tr>\n","      <td>1600</td>\n","      <td>0.453600</td>\n","    </tr>\n","    <tr>\n","      <td>1800</td>\n","      <td>0.438200</td>\n","    </tr>\n","    <tr>\n","      <td>2000</td>\n","      <td>0.420900</td>\n","    </tr>\n","    <tr>\n","      <td>2200</td>\n","      <td>0.400200</td>\n","    </tr>\n","    <tr>\n","      <td>2400</td>\n","      <td>0.391300</td>\n","    </tr>\n","    <tr>\n","      <td>2600</td>\n","      <td>0.386700</td>\n","    </tr>\n","    <tr>\n","      <td>2800</td>\n","      <td>0.388700</td>\n","    </tr>\n","    <tr>\n","      <td>3000</td>\n","      <td>0.376900</td>\n","    </tr>\n","    <tr>\n","      <td>3200</td>\n","      <td>0.368800</td>\n","    </tr>\n","    <tr>\n","      <td>3400</td>\n","      <td>0.356400</td>\n","    </tr>\n","    <tr>\n","      <td>3600</td>\n","      <td>0.370600</td>\n","    </tr>\n","    <tr>\n","      <td>3800</td>\n","      <td>0.346900</td>\n","    </tr>\n","    <tr>\n","      <td>4000</td>\n","      <td>0.346200</td>\n","    </tr>\n","    <tr>\n","      <td>4200</td>\n","      <td>0.340400</td>\n","    </tr>\n","    <tr>\n","      <td>4400</td>\n","      <td>0.341900</td>\n","    </tr>\n","    <tr>\n","      <td>4600</td>\n","      <td>0.336800</td>\n","    </tr>\n","    <tr>\n","      <td>4800</td>\n","      <td>0.328200</td>\n","    </tr>\n","    <tr>\n","      <td>5000</td>\n","      <td>0.346800</td>\n","    </tr>\n","    <tr>\n","      <td>5200</td>\n","      <td>0.340800</td>\n","    </tr>\n","    <tr>\n","      <td>5400</td>\n","      <td>0.333900</td>\n","    </tr>\n","    <tr>\n","      <td>5600</td>\n","      <td>0.328800</td>\n","    </tr>\n","    <tr>\n","      <td>5800</td>\n","      <td>0.336800</td>\n","    </tr>\n","    <tr>\n","      <td>6000</td>\n","      <td>0.323500</td>\n","    </tr>\n","    <tr>\n","      <td>6200</td>\n","      <td>0.332000</td>\n","    </tr>\n","    <tr>\n","      <td>6400</td>\n","      <td>0.322600</td>\n","    </tr>\n","    <tr>\n","      <td>6600</td>\n","      <td>0.320200</td>\n","    </tr>\n","    <tr>\n","      <td>6800</td>\n","      <td>0.330200</td>\n","    </tr>\n","    <tr>\n","      <td>7000</td>\n","      <td>0.325900</td>\n","    </tr>\n","    <tr>\n","      <td>7200</td>\n","      <td>0.318200</td>\n","    </tr>\n","    <tr>\n","      <td>7400</td>\n","      <td>0.337100</td>\n","    </tr>\n","    <tr>\n","      <td>7600</td>\n","      <td>0.326800</td>\n","    </tr>\n","    <tr>\n","      <td>7800</td>\n","      <td>0.320200</td>\n","    </tr>\n","    <tr>\n","      <td>8000</td>\n","      <td>0.303100</td>\n","    </tr>\n","    <tr>\n","      <td>8200</td>\n","      <td>0.314500</td>\n","    </tr>\n","    <tr>\n","      <td>8400</td>\n","      <td>0.307300</td>\n","    </tr>\n","    <tr>\n","      <td>8600</td>\n","      <td>0.330300</td>\n","    </tr>\n","    <tr>\n","      <td>8800</td>\n","      <td>0.323500</td>\n","    </tr>\n","    <tr>\n","      <td>9000</td>\n","      <td>0.322200</td>\n","    </tr>\n","    <tr>\n","      <td>9200</td>\n","      <td>0.315200</td>\n","    </tr>\n","    <tr>\n","      <td>9400</td>\n","      <td>0.310800</td>\n","    </tr>\n","    <tr>\n","      <td>9600</td>\n","      <td>0.314100</td>\n","    </tr>\n","    <tr>\n","      <td>9800</td>\n","      <td>0.317000</td>\n","    </tr>\n","    <tr>\n","      <td>10000</td>\n","      <td>0.311200</td>\n","    </tr>\n","    <tr>\n","      <td>10200</td>\n","      <td>0.323000</td>\n","    </tr>\n","    <tr>\n","      <td>10400</td>\n","      <td>0.312800</td>\n","    </tr>\n","    <tr>\n","      <td>10600</td>\n","      <td>0.313200</td>\n","    </tr>\n","    <tr>\n","      <td>10800</td>\n","      <td>0.315100</td>\n","    </tr>\n","    <tr>\n","      <td>11000</td>\n","      <td>0.319900</td>\n","    </tr>\n","    <tr>\n","      <td>11200</td>\n","      <td>0.321700</td>\n","    </tr>\n","    <tr>\n","      <td>11400</td>\n","      <td>0.317500</td>\n","    </tr>\n","    <tr>\n","      <td>11600</td>\n","      <td>0.324800</td>\n","    </tr>\n","    <tr>\n","      <td>11800</td>\n","      <td>0.308600</td>\n","    </tr>\n","    <tr>\n","      <td>12000</td>\n","      <td>0.323500</td>\n","    </tr>\n","    <tr>\n","      <td>12200</td>\n","      <td>0.308900</td>\n","    </tr>\n","    <tr>\n","      <td>12400</td>\n","      <td>0.307100</td>\n","    </tr>\n","    <tr>\n","      <td>12600</td>\n","      <td>0.310700</td>\n","    </tr>\n","    <tr>\n","      <td>12800</td>\n","      <td>0.321300</td>\n","    </tr>\n","    <tr>\n","      <td>13000</td>\n","      <td>0.319700</td>\n","    </tr>\n","    <tr>\n","      <td>13200</td>\n","      <td>0.301200</td>\n","    </tr>\n","    <tr>\n","      <td>13400</td>\n","      <td>0.304600</td>\n","    </tr>\n","    <tr>\n","      <td>13600</td>\n","      <td>0.328500</td>\n","    </tr>\n","    <tr>\n","      <td>13800</td>\n","      <td>0.302900</td>\n","    </tr>\n","    <tr>\n","      <td>14000</td>\n","      <td>0.311500</td>\n","    </tr>\n","    <tr>\n","      <td>14200</td>\n","      <td>0.333500</td>\n","    </tr>\n","    <tr>\n","      <td>14400</td>\n","      <td>0.314200</td>\n","    </tr>\n","    <tr>\n","      <td>14600</td>\n","      <td>0.322600</td>\n","    </tr>\n","    <tr>\n","      <td>14800</td>\n","      <td>0.302400</td>\n","    </tr>\n","    <tr>\n","      <td>15000</td>\n","      <td>0.293600</td>\n","    </tr>\n","    <tr>\n","      <td>15200</td>\n","      <td>0.311400</td>\n","    </tr>\n","    <tr>\n","      <td>15400</td>\n","      <td>0.305800</td>\n","    </tr>\n","    <tr>\n","      <td>15600</td>\n","      <td>0.301700</td>\n","    </tr>\n","    <tr>\n","      <td>15800</td>\n","      <td>0.301800</td>\n","    </tr>\n","    <tr>\n","      <td>16000</td>\n","      <td>0.334100</td>\n","    </tr>\n","    <tr>\n","      <td>16200</td>\n","      <td>0.310100</td>\n","    </tr>\n","    <tr>\n","      <td>16400</td>\n","      <td>0.317800</td>\n","    </tr>\n","    <tr>\n","      <td>16600</td>\n","      <td>0.313300</td>\n","    </tr>\n","    <tr>\n","      <td>16800</td>\n","      <td>0.325200</td>\n","    </tr>\n","    <tr>\n","      <td>17000</td>\n","      <td>0.308400</td>\n","    </tr>\n","    <tr>\n","      <td>17200</td>\n","      <td>0.306200</td>\n","    </tr>\n","    <tr>\n","      <td>17400</td>\n","      <td>0.302700</td>\n","    </tr>\n","    <tr>\n","      <td>17600</td>\n","      <td>0.312500</td>\n","    </tr>\n","    <tr>\n","      <td>17800</td>\n","      <td>0.323000</td>\n","    </tr>\n","    <tr>\n","      <td>18000</td>\n","      <td>0.295900</td>\n","    </tr>\n","    <tr>\n","      <td>18200</td>\n","      <td>0.312900</td>\n","    </tr>\n","    <tr>\n","      <td>18400</td>\n","      <td>0.307200</td>\n","    </tr>\n","    <tr>\n","      <td>18600</td>\n","      <td>0.304200</td>\n","    </tr>\n","    <tr>\n","      <td>18800</td>\n","      <td>0.320400</td>\n","    </tr>\n","    <tr>\n","      <td>19000</td>\n","      <td>0.304000</td>\n","    </tr>\n","    <tr>\n","      <td>19200</td>\n","      <td>0.297300</td>\n","    </tr>\n","    <tr>\n","      <td>19400</td>\n","      <td>0.318500</td>\n","    </tr>\n","    <tr>\n","      <td>19600</td>\n","      <td>0.302800</td>\n","    </tr>\n","    <tr>\n","      <td>19800</td>\n","      <td>0.310900</td>\n","    </tr>\n","    <tr>\n","      <td>20000</td>\n","      <td>0.310800</td>\n","    </tr>\n","    <tr>\n","      <td>20200</td>\n","      <td>0.307100</td>\n","    </tr>\n","    <tr>\n","      <td>20400</td>\n","      <td>0.313700</td>\n","    </tr>\n","    <tr>\n","      <td>20600</td>\n","      <td>0.303700</td>\n","    </tr>\n","    <tr>\n","      <td>20800</td>\n","      <td>0.315900</td>\n","    </tr>\n","    <tr>\n","      <td>21000</td>\n","      <td>0.299500</td>\n","    </tr>\n","    <tr>\n","      <td>21200</td>\n","      <td>0.314100</td>\n","    </tr>\n","    <tr>\n","      <td>21400</td>\n","      <td>0.309000</td>\n","    </tr>\n","    <tr>\n","      <td>21600</td>\n","      <td>0.303700</td>\n","    </tr>\n","    <tr>\n","      <td>21800</td>\n","      <td>0.308600</td>\n","    </tr>\n","    <tr>\n","      <td>22000</td>\n","      <td>0.322400</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=22145, training_loss=0.3414644513073127, metrics={'train_runtime': 4964.2614, 'train_samples_per_second': 4.461, 'total_flos': 88910428149525264, 'epoch': 1.0})"]},"metadata":{},"execution_count":16}]},{"cell_type":"markdown","metadata":{"id":"MP8W8rrSsrkq"},"source":["## Train classifier task 01 (360k model, no freeze on params) - bert-base-uncased-2epoch-LR5e-5"]},{"cell_type":"code","metadata":{"id":"8g6Ceo8FxT6-"},"source":["import shutil\n","shutil.rmtree('/content/drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/Classifier/01-nofreeze-with-val-twiBot-360k-similarity-metadata-2epoch_LR5e-5_bert_base_uncased')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AoghKNwcswY6","executionInfo":{"status":"error","timestamp":1638377517005,"user_tz":480,"elapsed":15148782,"user":{"displayName":"zeeshan ahmad","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15348670853435150367"}},"outputId":"596acb0e-2224-4897-f6dd-5e70181e5541"},"source":["training_args = TrainingArguments(\n","    output_dir = data_folder+'Model_TwiBot_Similarity_With_Metadata_360k_Tweets/Classifier/01-nofreeze-with-val-twiBot-360k-similarity-metadata-2epoch_LR5e-5_bert_base_uncased/results',          # output directory\n","    num_train_epochs = 1,              # total number of training epochs\n","    per_device_train_batch_size = 16,  # batch size per device during training\n","    per_device_eval_batch_size = 64,   # batch size for evaluation\n","    warmup_steps = 500,                # number of warmup steps for learning rate scheduler\n","    weight_decay = 0.01,               # strength of weight decay\n","    logging_dir = data_folder+'Model_TwiBot_Similarity_With_Metadata_360k_Tweets/Classifier/01-nofreeze-with-val-twiBot-360k-similarity-metadata-2epoch_LR5e-5_bert_base_uncased/logs',            # directory for storing logs\n","    logging_steps=5000,\n","    evaluation_strategy=\"steps\"\n",")\n","\n","trainer = Trainer(\n","    model=model_twitter_classifier,       # the instantiated 🤗 Transformers model to be trained\n","    args=training_args,                   # training arguments, defined above\n","    train_dataset=train_dataset,          # training dataset\n","    eval_dataset=dev_dataset,            # evaluation dataset\n","    compute_metrics=compute_metrics\n",")\n","\n","trainer.train()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","    <div>\n","        <style>\n","            /* Turns off some styling */\n","            progress {\n","                /* gets rid of default border in Firefox and Opera. */\n","                border: none;\n","                /* Needs to be in here for Safari polyfill so background images work as expected. */\n","                background-size: auto;\n","            }\n","        </style>\n","      \n","      <progress value='15998' max='22145' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [15998/22145 4:12:25 < 1:37:00, 1.06 it/s, Epoch 0.72/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Accuracy</th>\n","      <th>Runtime</th>\n","      <th>Samples Per Second</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>5000</td>\n","      <td>0.022400</td>\n","      <td>1.181293</td>\n","      <td>0.819554</td>\n","      <td>1546.869000</td>\n","      <td>76.073000</td>\n","    </tr>\n","    <tr>\n","      <td>10000</td>\n","      <td>0.011000</td>\n","      <td>1.474235</td>\n","      <td>0.810325</td>\n","      <td>1546.970700</td>\n","      <td>76.068000</td>\n","    </tr>\n","    <tr>\n","      <td>15000</td>\n","      <td>0.006700</td>\n","      <td>1.434572</td>\n","      <td>0.852288</td>\n","      <td>1546.906700</td>\n","      <td>76.071000</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["12/01/2021 13:59:52 - INFO - /usr/local/lib/python3.7/dist-packages/datasets/metric.py -   Removing /root/.cache/huggingface/metrics/accuracy/default/default_experiment-1-0.arrow\n","12/01/2021 15:20:25 - INFO - /usr/local/lib/python3.7/dist-packages/datasets/metric.py -   Removing /root/.cache/huggingface/metrics/accuracy/default/default_experiment-1-0.arrow\n","12/01/2021 16:40:58 - INFO - /usr/local/lib/python3.7/dist-packages/datasets/metric.py -   Removing /root/.cache/huggingface/metrics/accuracy/default/default_experiment-1-0.arrow\n"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-23-138ce0177b54>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m )\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_wrapped\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1266\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1267\u001b[0;31m             \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1268\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1269\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    305\u001b[0m                 \u001b[0mcreate_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m                 inputs=inputs)\n\u001b[0;32m--> 307\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    154\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[1;32m    155\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 156\u001b[0;31m         allow_unreachable=True, accumulate_grad=True)  # allow_unreachable flag\n\u001b[0m\u001b[1;32m    157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"markdown","metadata":{"id":"hRimdLpls0dS"},"source":["## Copy latest checkpoint folder"]},{"cell_type":"code","metadata":{"id":"EOQB8vG3DEbl"},"source":["!cp -R /content/drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/Classifier/twiBot-360k-similarity-metadata-1epoch_LR5e-5_sup-simcse-bert-base-uncased/results/checkpoint-22000 /content/drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/Classifier/twiBot-360k-similarity-metadata-1epoch_LR5e-5_sup-simcse-bert-base-uncased/classifier_model"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"fVvVu3YuGn8m"},"source":["## Prediction"]},{"cell_type":"code","metadata":{"id":"fOR8trqst4BT"},"source":["# twiBot-360k-similarity-metadata-1epoch_LR5e-5_sup-simcse-bert-base-uncased\n","path_classifier = \"/content/drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/Classifier/twiBot-360k-similarity-metadata-1epoch_LR5e-5_sup-simcse-bert-base-uncased/classifier_model/checkpoint-22000\"\n","path_tokenizer = 'drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/twiBot-360k-similarity-metadata-1epoch_LR5e-5_sup-simcse-bert-base-uncased'"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"5jmTgeywtBmU"},"source":["model = AutoModelForSequenceClassification.from_pretrained(path_classifier)\n","training_args = TrainingArguments(\"test_trainer\", evaluation_strategy=\"epoch\")\n","tokenizer = AutoTokenizer.from_pretrained(path_tokenizer)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"yhOWFJFEV2wp"},"source":["df_dev = pd.read_csv(data_folder + \"/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/dev_all_data_triplets_with_similarity_corrected_metadata.csv\" , usecols = ['sent0', 'sent0_label'])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"6fML1fceXEpV"},"source":["pd.set_option(\"display.max_rows\", None)\n","pd.set_option('display.max_colwidth', None)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"X1aTr2lyWO7Z","executionInfo":{"status":"ok","timestamp":1638413827222,"user_tz":480,"elapsed":464,"user":{"displayName":"zeeshan ahmad","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15348670853435150367"}},"outputId":"6aa14d54-795b-4b02-f355-199185aee141"},"source":["print(len(df_dev))\n","df_dev = df_dev.drop_duplicates()\n","print(len(df_dev))\n","print(df_dev.iloc[0].to_string)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["480000\n","117675\n","<bound method Series.to_string of sent0          RAkufoAddo unprotected unverified defprof nondefimg 388018 109 37 221 1131 My fervent wish today is for us to eschew division, and unite as a people, as we strive collectively for progress and prosperity. May patriotism and compassion forever be our guiding principles. Happy Independence Day! <url>\n","sent0_label                                                                                                                                                                                                                                                                                                              0\n","Name: 0, dtype: object>\n"]}]},{"cell_type":"code","metadata":{"id":"X_RTji3j1_Hv"},"source":["pred_csv_file=\"/content/drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/Classifier/twiBot-360k-similarity-metadata-2epoch_LR5e-5_bert_base_uncased/classifier_model/checkpoint-44000/pred_dev_twiBot-360k-similarity-metadata-2epoch_LR5e-5_bert_base_uncased.csv\""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"VTVD6YB8pBmZ"},"source":["## Dev -  Prediction bert-base-uncased-2epoch-LR5e-5"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"W8s3podlpEmV","executionInfo":{"status":"ok","timestamp":1638280367512,"user_tz":480,"elapsed":14922878,"user":{"displayName":"zeeshan ahmad","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15348670853435150367"}},"outputId":"ee9cbd30-5e9e-4a59-c865-70f68f76fc23"},"source":["#Check if we need to freeze params\n","model.eval()\n","pred_curated = []\n","batch_size = 45 # 117675 / 45 = 1569\n","count = 0\n","for i in range(0, len(df_dev), batch_size):\n","  print(\"{}/{}\".format(int((i/batch_size)+1), int(len(df_dev)/batch_size)))\n","  pt_batch = tokenizer(\n","      list(df_dev[i:i+batch_size].sent0), #[\"We are very happy to show you the 🤗 Transformers library.\", \"We hope you don't hate it.\"],\n","      padding=True,\n","      truncation=True,\n","      max_length=512,\n","      return_tensors=\"pt\"\n","  )\n","  with torch.no_grad():\n","    prediction_logits = model(**pt_batch)\n","    prediction_results = torch.softmax(prediction_logits[0], dim=1)\n","    prediction_results = torch.argmax(prediction_results, dim=1)\n","    pred_curated.extend([[i,j] for i,j in zip(prediction_results.tolist(),df_dev[i:i+batch_size].sent0_label.tolist())])\n","    # print(prediction_results.numpy().shape, np.array(df_dev[i:i+batch_size].sent0_label).shape )\n","    count += np.sum(prediction_results.numpy() == np.array(df_dev[i:i+batch_size].sent0_label))\n","    print(\"Accuracy\", count/(i+batch_size))\n","  if (int((i/batch_size)+1)%5 ==0):\n","    with open(pred_csv_file,\"a\") as f:\n","      # x=pickle.load(f)\n","      # x.extend(pred_cul)\n","      # pickle.dump(pred_cul, f)\n","      # pred_cul = []\n","      f_writer=csv.writer(f)\n","      f_writer.writerows(pred_curated)\n","      pred_curated =[]\n","    print(\"last batch saved\", str(int((i/batch_size)+1)%100))\n","print(\"Final Accuracy=\", count/len(df_dev))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","344/2615\n","Accuracy 0.8488372093023255\n","345/2615\n","Accuracy 0.8488888888888889\n","last batch saved 45\n","346/2615\n","Accuracy 0.8486191393705844\n","347/2615\n","Accuracy 0.8485430675632405\n","348/2615\n","Accuracy 0.8485951468710089\n","349/2615\n","Accuracy 0.8487106017191977\n","350/2615\n","Accuracy 0.8489523809523809\n","last batch saved 50\n","351/2615\n","Accuracy 0.8488762266540044\n","352/2615\n","Accuracy 0.8487373737373738\n","353/2615\n","Accuracy 0.8486622599937047\n","354/2615\n","Accuracy 0.8484620213433772\n","355/2615\n","Accuracy 0.8483255086071988\n","last batch saved 55\n","356/2615\n","Accuracy 0.8482521847690387\n","357/2615\n","Accuracy 0.8483037659508248\n","358/2615\n","Accuracy 0.8481067659838609\n","359/2615\n","Accuracy 0.8482822655524606\n","360/2615\n","Accuracy 0.8482716049382716\n","last batch saved 60\n","361/2615\n","Accuracy 0.84844567559249\n","362/2615\n","Accuracy 0.8484346224677717\n","363/2615\n","Accuracy 0.8481175390266299\n","364/2615\n","Accuracy 0.8481684981684982\n","365/2615\n","Accuracy 0.8482800608828006\n","last batch saved 65\n","366/2615\n","Accuracy 0.8482695810564663\n","367/2615\n","Accuracy 0.8484408113835906\n","368/2615\n","Accuracy 0.8483695652173913\n","369/2615\n","Accuracy 0.8482987052092743\n","370/2615\n","Accuracy 0.8485285285285286\n","last batch saved 70\n","371/2615\n","Accuracy 0.8486972147349506\n","372/2615\n","Accuracy 0.8486857825567503\n","373/2615\n","Accuracy 0.8484956806672624\n","374/2615\n","Accuracy 0.8483065953654189\n","375/2615\n","Accuracy 0.8482962962962963\n","last batch saved 75\n","376/2615\n","Accuracy 0.848404255319149\n","377/2615\n","Accuracy 0.8482169171824344\n","378/2615\n","Accuracy 0.8484420928865374\n","379/2615\n","Accuracy 0.8483729111697449\n","380/2615\n","Accuracy 0.8482456140350877\n","last batch saved 80\n","381/2615\n","Accuracy 0.8481773111694372\n","382/2615\n","Accuracy 0.8480511925538103\n","383/2615\n","Accuracy 0.8480417754569191\n","384/2615\n","Accuracy 0.8480324074074074\n","385/2615\n","Accuracy 0.8481385281385282\n","last batch saved 85\n","386/2615\n","Accuracy 0.8481865284974093\n","387/2615\n","Accuracy 0.8482917025552684\n","388/2615\n","Accuracy 0.8483963344788087\n","389/2615\n","Accuracy 0.8482147957726364\n","390/2615\n","Accuracy 0.8483190883190883\n","last batch saved 90\n","391/2615\n","Accuracy 0.8481386757601591\n","392/2615\n","Accuracy 0.8481292517006803\n","393/2615\n","Accuracy 0.8480067854113655\n","394/2615\n","Accuracy 0.8480541455160745\n","395/2615\n","Accuracy 0.8481012658227848\n","last batch saved 95\n","396/2615\n","Accuracy 0.847979797979798\n","397/2615\n","Accuracy 0.848250769661349\n","398/2615\n","Accuracy 0.8484087102177554\n","399/2615\n","Accuracy 0.8484544695071011\n","400/2615\n","Accuracy 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0.8454565811276815\n","2482/2615\n","Accuracy 0.8454382666308533\n","2483/2615\n","Accuracy 0.8454468161274444\n","2484/2615\n","Accuracy 0.8454911433172303\n","2485/2615\n","Accuracy 0.8454817795662866\n","last batch saved 85\n","2486/2615\n","Accuracy 0.8454813622955216\n","2487/2615\n","Accuracy 0.8454988160657642\n","2488/2615\n","Accuracy 0.8455251875669882\n","2489/2615\n","Accuracy 0.8455426097049239\n","2490/2615\n","Accuracy 0.8455510932619367\n","last batch saved 90\n","2491/2615\n","Accuracy 0.8455506490030777\n","2492/2615\n","Accuracy 0.8455234528268236\n","2493/2615\n","Accuracy 0.8455051923162633\n","2494/2615\n","Accuracy 0.8455136772698922\n","2495/2615\n","Accuracy 0.8455399688265419\n","last batch saved 95\n","2496/2615\n","Accuracy 0.8455128205128205\n","2497/2615\n","Accuracy 0.8455123926489565\n","2498/2615\n","Accuracy 0.8455119651276577\n","2499/2615\n","Accuracy 0.8455026455026455\n","2500/2615\n","Accuracy 0.8455288888888889\n","last batch saved 0\n","2501/2615\n","Accuracy 0.8455462259540628\n","2502/2615\n","Accuracy 0.8455635491606714\n","2503/2615\n","Accuracy 0.8455719802903183\n","2504/2615\n","Accuracy 0.8455715299964501\n","2505/2615\n","Accuracy 0.8455267243291196\n","last batch saved 5\n","2506/2615\n","Accuracy 0.8455085572404009\n","2507/2615\n","Accuracy 0.845508132783761\n","2508/2615\n","Accuracy 0.8454988481304271\n","2509/2615\n","Accuracy 0.8455161418891989\n","2510/2615\n","Accuracy 0.8455334218680832\n","last batch saved 10\n","2511/2615\n","Accuracy 0.8454975883888668\n","2512/2615\n","Accuracy 0.8455060155697098\n","2513/2615\n","Accuracy 0.8455055931379051\n","2514/2615\n","Accuracy 0.8454963316538495\n","2515/2615\n","Accuracy 0.8454959134084382\n","last batch saved 15\n","2516/2615\n","Accuracy 0.8455131602190425\n","2517/2615\n","Accuracy 0.8455215644726968\n","2518/2615\n","Accuracy 0.845503486011826\n","2519/2615\n","Accuracy 0.8455030655904018\n","2520/2615\n","Accuracy 0.8454938271604938\n","last batch saved 20\n","2521/2615\n","Accuracy 0.8455286702807527\n","2522/2615\n","Accuracy 0.8455458630716363\n","2523/2615\n","Accuracy 0.8455454265204563\n","2524/2615\n","Accuracy 0.8455537946821624\n","2525/2615\n","Accuracy 0.8455797579757975\n","last batch saved 25\n","2526/2615\n","Accuracy 0.8456232955045306\n","2527/2615\n","Accuracy 0.8456492107461636\n","2528/2615\n","Accuracy 0.8456575246132209\n","2529/2615\n","Accuracy 0.8456658319054523\n","2530/2615\n","Accuracy 0.8456829161176987\n","last batch saved 30\n","2531/2615\n","Accuracy 0.8456912068132929\n","2532/2615\n","Accuracy 0.84565560821485\n","2533/2615\n","Accuracy 0.8456639031451507\n","2534/2615\n","Accuracy 0.8456809611505745\n","2535/2615\n","Accuracy 0.8456717072101687\n","last batch saved 35\n","2536/2615\n","Accuracy 0.8456887486855941\n","2537/2615\n","Accuracy 0.8456882582227477\n","2538/2615\n","Accuracy 0.845679012345679\n","2539/2615\n","Accuracy 0.8456785261038904\n","2540/2615\n","Accuracy 0.8456692913385827\n","last batch saved 40\n","2541/2615\n","Accuracy 0.8456775547684638\n","2542/2615\n","Accuracy 0.8456683276510184\n","2543/2615\n","Accuracy 0.8456765849608948\n","2544/2615\n","Accuracy 0.8456586303284417\n","2545/2615\n","Accuracy 0.8456843483955468\n","last batch saved 45\n","2546/2615\n","Accuracy 0.8456925896831631\n","2547/2615\n","Accuracy 0.8457095493609039\n","2548/2615\n","Accuracy 0.8457177742892028\n","2549/2615\n","Accuracy 0.8457347107798265\n","2550/2615\n","Accuracy 0.8457777777777777\n","last batch saved 50\n","2551/2615\n","Accuracy 0.8457946774685309\n","2552/2615\n","Accuracy 0.8458028561476837\n","2553/2615\n","Accuracy 0.8458023240631937\n","2554/2615\n","Accuracy 0.8458104933437744\n","2555/2615\n","Accuracy 0.8458273537725592\n","last batch saved 55\n","2556/2615\n","Accuracy 0.8458442010085202\n","2557/2615\n","Accuracy 0.8458697258071525\n","2558/2615\n","Accuracy 0.8458865433064026\n","2559/2615\n","Accuracy 0.8458772958186792\n","2560/2615\n","Accuracy 0.8458854166666666\n","last batch saved 60\n","2561/2615\n","Accuracy 0.8458761768406439\n","2562/2615\n","Accuracy 0.8458582704484344\n","2563/2615\n","Accuracy 0.8458230372393463\n","2564/2615\n","Accuracy 0.8458224995666493\n","2565/2615\n","Accuracy 0.8458392895819796\n","last batch saved 65\n","2566/2615\n","Accuracy 0.8458214254784793\n","2567/2615\n","Accuracy 0.8458555165995758\n","2568/2615\n","Accuracy 0.8458636206299758\n","2569/2615\n","Accuracy 0.8458457679166126\n","2570/2615\n","Accuracy 0.8458538694336359\n","last batch saved 70\n","2571/2615\n","Accuracy 0.8458533212325511\n","2572/2615\n","Accuracy 0.8458354933471575\n","2573/2615\n","Accuracy 0.845843589411409\n","2574/2615\n","Accuracy 0.8458689458689459\n","2575/2615\n","Accuracy 0.8458942826321467\n","last batch saved 75\n","2576/2615\n","Accuracy 0.8459023464458247\n","2577/2615\n","Accuracy 0.8459017807096969\n","2578/2615\n","Accuracy 0.8459356951986897\n","2579/2615\n","Accuracy 0.8459437335745983\n","2580/2615\n","Accuracy 0.8459173126614987\n","last batch saved 80\n","2581/2615\n","Accuracy 0.8459425717852684\n","2582/2615\n","Accuracy 0.8459592047508392\n","2583/2615\n","Accuracy 0.8459500150557061\n","2584/2615\n","Accuracy 0.8459752321981424\n","2585/2615\n","Accuracy 0.8459832366215345\n","last batch saved 85\n","2586/2615\n","Accuracy 0.8459912348543439\n","2587/2615\n","Accuracy 0.8459820469870721\n","2588/2615\n","Accuracy 0.8459900394985402\n","2589/2615\n","Accuracy 0.8459551092227802\n","2590/2615\n","Accuracy 0.845945945945946\n","last batch saved 90\n","2591/2615\n","Accuracy 0.8459196363480423\n","2592/2615\n","Accuracy 0.8459190672153635\n","2593/2615\n","Accuracy 0.8459184985216609\n","2594/2615\n","Accuracy 0.8459264970444615\n","2595/2615\n","Accuracy 0.845943052879469\n","last batch saved 95\n","2596/2615\n","Accuracy 0.8459681561376476\n","2597/2615\n","Accuracy 0.8459761262995764\n","2598/2615\n","Accuracy 0.8459755367376615\n","2599/2615\n","Accuracy 0.8459663973323073\n","2600/2615\n","Accuracy 0.8459401709401709\n","last batch saved 0\n","2601/2615\n","Accuracy 0.8459395958819258\n","2602/2615\n","Accuracy 0.8459817234605859\n","2603/2615\n","Accuracy 0.8459640585649038\n","2604/2615\n","Accuracy 0.8459720088752347\n","2605/2615\n","Accuracy 0.8459714224781403\n","last batch saved 5\n","2606/2615\n","Accuracy 0.845987891191268\n","2607/2615\n","Accuracy 0.8459787750926991\n","2608/2615\n","Accuracy 0.8460122699386503\n","2609/2615\n","Accuracy 0.8460372215834079\n","2610/2615\n","Accuracy 0.8460706683695189\n","last batch saved 10\n","2611/2615\n","Accuracy 0.8460360015319801\n","2612/2615\n","Accuracy 0.8460098689807725\n","2613/2615\n","Accuracy 0.8460262788620998\n","2614/2615\n","Accuracy 0.8460001700246536\n","2615/2615\n","Accuracy 0.8460250690461015\n","last batch saved 15\n","Final Accuracy= 0.8460250690461015\n"]}]},{"cell_type":"code","metadata":{"id":"wqzdfwe628PA","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1638281112941,"user_tz":480,"elapsed":294,"user":{"displayName":"zeeshan ahmad","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15348670853435150367"}},"outputId":"bc9715d0-bf42-4909-c0dc-e24d4afd8d40"},"source":["from sklearn.metrics import f1_score,matthews_corrcoef\n","pred_df = pd.read_csv(pred_csv_file, header=None)\n","if (len(pred_df) != len(df_dev)):\n","  print(\"MISMATCH\")\n","print(\"f1_score\",f1_score(pred_df[1].tolist(), pred_df[0].tolist()))\n","print(\"MCC\", matthews_corrcoef(pred_df[1].tolist(), pred_df[0].tolist()))\n","# f1_score(pred_df[1].tolist(), pred_df[0].tolist(), average=None)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["f1_score 0.8650303549480427\n","MCC 0.6993975611535929\n"]}]},{"cell_type":"markdown","metadata":{"id":"AkJbMqThEFBb"},"source":["## Dev -  Prediction 1epoch_LR5e-5_sup-simcse-bert-base-uncased"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"o7gQ_2CJEPsY","executionInfo":{"status":"ok","timestamp":1638436005258,"user_tz":480,"elapsed":3100815,"user":{"displayName":"zeeshan ahmad","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15348670853435150367"}},"outputId":"73157209-a3ba-4c6b-bf12-dae7a452026f"},"source":["#Check if we need to freeze params\n","model.eval()\n","pred_curated = []\n","batch_size = 45 # 117675 / 45 = 1569\n","count = 0\n","for i in range(0, len(df_dev), batch_size):\n","  print(\"{}/{}\".format(int((i/batch_size)+1), int(len(df_dev)/batch_size)))\n","  pt_batch = tokenizer(\n","      list(df_dev[i:i+batch_size].sent0), #[\"We are very happy to show you the 🤗 Transformers library.\", \"We hope you don't hate it.\"],\n","      padding=True,\n","      truncation=True,\n","      max_length=512,\n","      return_tensors=\"pt\"\n","  )\n","  with torch.no_grad():\n","    prediction_logits = model(**pt_batch)\n","    prediction_results = torch.softmax(prediction_logits[0], dim=1)\n","    prediction_results = torch.argmax(prediction_results, dim=1)\n","    pred_curated.extend([[i,j] for i,j in zip(prediction_results.tolist(),df_dev[i:i+batch_size].sent0_label.tolist())])\n","    # print(prediction_results.numpy().shape, np.array(df_dev[i:i+batch_size].sent0_label).shape )\n","    count += np.sum(prediction_results.numpy() == np.array(df_dev[i:i+batch_size].sent0_label))\n","    print(\"Accuracy\", count/(i+batch_size))\n","  if (int((i/batch_size)+1)%5 ==0):\n","    with open(pred_csv_file,\"a\") as f:\n","      # x=pickle.load(f)\n","      # x.extend(pred_cul)\n","      # pickle.dump(pred_cul, f)\n","      # pred_cul = []\n","      f_writer=csv.writer(f)\n","      f_writer.writerows(pred_curated)\n","      pred_curated =[]\n","    print(\"last batch saved\", str(int((i/batch_size)+1)%100))\n","print(\"Final Accuracy=\", count/len(df_dev))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","last batch saved 40\n","341/2615\n","Accuracy 0.837862495927012\n","342/2615\n","Accuracy 0.837946718648473\n","343/2615\n","Accuracy 0.8380304502753483\n","344/2615\n","Accuracy 0.8377906976744186\n","345/2615\n","Accuracy 0.8378743961352657\n","last batch saved 45\n","346/2615\n","Accuracy 0.8375722543352602\n","347/2615\n","Accuracy 0.8376560999039385\n","348/2615\n","Accuracy 0.8377394636015326\n","349/2615\n","Accuracy 0.837886023559376\n","350/2615\n","Accuracy 0.8379682539682539\n","last batch saved 50\n","351/2615\n","Accuracy 0.8379233934789491\n","352/2615\n","Accuracy 0.8377525252525253\n","353/2615\n","Accuracy 0.8376455775889203\n","354/2615\n","Accuracy 0.8374764595103578\n","355/2615\n","Accuracy 0.8373708920187793\n","last batch saved 55\n","356/2615\n","Accuracy 0.8373283395755305\n","357/2615\n","Accuracy 0.837223778400249\n","358/2615\n","Accuracy 0.8373060211049038\n","359/2615\n","Accuracy 0.8372640049520272\n","360/2615\n","Accuracy 0.8372222222222222\n","last batch saved 60\n","361/2615\n","Accuracy 0.8374884579870729\n","362/2615\n","Accuracy 0.8373848987108655\n","363/2615\n","Accuracy 0.8372819100091827\n","364/2615\n","Accuracy 0.8371794871794872\n","365/2615\n","Accuracy 0.8368949771689498\n","last batch saved 65\n","366/2615\n","Accuracy 0.837037037037037\n","367/2615\n","Accuracy 0.8372388737511354\n","368/2615\n","Accuracy 0.8373188405797102\n","369/2615\n","Accuracy 0.8373381511592893\n","370/2615\n","Accuracy 0.8375375375375376\n","last batch saved 70\n","371/2615\n","Accuracy 0.8377957472297095\n","372/2615\n","Accuracy 0.8375746714456392\n","373/2615\n","Accuracy 0.8375335120643431\n","374/2615\n","Accuracy 0.8373737373737373\n","375/2615\n","Accuracy 0.8376296296296296\n","last batch saved 75\n","376/2615\n","Accuracy 0.8378250591016548\n","377/2615\n","Accuracy 0.8377247273798998\n","378/2615\n","Accuracy 0.8379188712522045\n","379/2615\n","Accuracy 0.838053356786866\n","380/2615\n","Accuracy 0.8380116959064328\n","last batch saved 80\n","381/2615\n","Accuracy 0.8379119276757072\n","382/2615\n","Accuracy 0.8377545084351367\n","383/2615\n","Accuracy 0.838004061502756\n","384/2615\n","Accuracy 0.8381944444444445\n","385/2615\n","Accuracy 0.8383261183261184\n","last batch saved 85\n","386/2615\n","Accuracy 0.8383995394358089\n","387/2615\n","Accuracy 0.8383577375825438\n","388/2615\n","Accuracy 0.8384879725085911\n","389/2615\n","Accuracy 0.8383319051699515\n","390/2615\n","Accuracy 0.8381766381766381\n","last batch saved 90\n","391/2615\n","Accuracy 0.8379653310599602\n","392/2615\n","Accuracy 0.8379251700680272\n","393/2615\n","Accuracy 0.8376024879841674\n","394/2615\n","Accuracy 0.837619853355894\n","395/2615\n","Accuracy 0.8374120956399438\n","last batch saved 95\n","396/2615\n","Accuracy 0.8374298540965207\n","397/2615\n","Accuracy 0.8376714245731878\n","398/2615\n","Accuracy 0.8376326074818538\n","399/2615\n","Accuracy 0.8376496797549429\n","400/2615\n","Accuracy 0.8377222222222223\n","last batch saved 0\n","401/2615\n","Accuracy 0.8378498198947076\n","402/2615\n","Accuracy 0.8380873410724157\n","403/2615\n","Accuracy 0.8381031155224704\n","404/2615\n","Accuracy 0.8378987898789879\n","405/2615\n","Accuracy 0.8378600823045268\n","last batch saved 5\n","406/2615\n","Accuracy 0.8379310344827586\n","407/2615\n","Accuracy 0.8377286377286377\n","408/2615\n","Accuracy 0.8378540305010893\n","409/2615\n","Accuracy 0.8378701439826134\n","410/2615\n","Accuracy 0.8378861788617886\n","last batch saved 10\n","411/2615\n","Accuracy 0.8379562043795621\n","412/2615\n","Accuracy 0.8379180151024811\n","413/2615\n","Accuracy 0.8376647834274953\n","414/2615\n","Accuracy 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0.8362774516199174\n","last batch saved 55\n","2556/2615\n","Accuracy 0.8362719527038776\n","2557/2615\n","Accuracy 0.8362664580889063\n","2558/2615\n","Accuracy 0.8362957171401268\n","2559/2615\n","Accuracy 0.8362902175328905\n","2560/2615\n","Accuracy 0.8363107638888889\n","last batch saved 60\n","2561/2615\n","Accuracy 0.8363312941993145\n","2562/2615\n","Accuracy 0.8363344609246248\n","2563/2615\n","Accuracy 0.8362942732041444\n","2564/2615\n","Accuracy 0.8362887848847287\n","2565/2615\n","Accuracy 0.8363179553822828\n","last batch saved 65\n","2566/2615\n","Accuracy 0.8363124621113709\n","2567/2615\n","Accuracy 0.836332943773536\n","2568/2615\n","Accuracy 0.8363534094842506\n","2569/2615\n","Accuracy 0.836330608537693\n","2570/2615\n","Accuracy 0.8363251188932123\n","last batch saved 70\n","2571/2615\n","Accuracy 0.8363455637668007\n","2572/2615\n","Accuracy 0.8363314325211681\n","2573/2615\n","Accuracy 0.8363432223517727\n","2574/2615\n","Accuracy 0.8363722697056031\n","2575/2615\n","Accuracy 0.8364012944983819\n","last batch saved 75\n","2576/2615\n","Accuracy 0.8364475500345065\n","2577/2615\n","Accuracy 0.8364420299228216\n","2578/2615\n","Accuracy 0.8364796138263942\n","2579/2615\n","Accuracy 0.836482702167076\n","2580/2615\n","Accuracy 0.8364685615848406\n","last batch saved 80\n","2581/2615\n","Accuracy 0.8364888716690344\n","2582/2615\n","Accuracy 0.8364833462432223\n","2583/2615\n","Accuracy 0.8364692218350754\n","2584/2615\n","Accuracy 0.8364895080839353\n","2585/2615\n","Accuracy 0.8364925854287556\n","last batch saved 85\n","2586/2615\n","Accuracy 0.8365128469536822\n","2587/2615\n","Accuracy 0.8364643731477902\n","2588/2615\n","Accuracy 0.8364502833590932\n","2589/2615\n","Accuracy 0.8364276211321402\n","2590/2615\n","Accuracy 0.8364049764049764\n","last batch saved 90\n","2591/2615\n","Accuracy 0.8363737724602256\n","2592/2615\n","Accuracy 0.8363854595336077\n","2593/2615\n","Accuracy 0.8363885675108197\n","2594/2615\n","Accuracy 0.836400239869785\n","2595/2615\n","Accuracy 0.8363947762791694\n","last batch saved 95\n","2596/2615\n","Accuracy 0.8364235576099983\n","2597/2615\n","Accuracy 0.8363667479570445\n","2598/2615\n","Accuracy 0.8363784107433068\n","2599/2615\n","Accuracy 0.8363900645547433\n","2600/2615\n","Accuracy 0.8363846153846154\n","last batch saved 0\n","2601/2615\n","Accuracy 0.8363535392370456\n","2602/2615\n","Accuracy 0.8363822700486805\n","2603/2615\n","Accuracy 0.8363597558372817\n","2604/2615\n","Accuracy 0.8363713944359106\n","2605/2615\n","Accuracy 0.8364000853060354\n","last batch saved 5\n","2606/2615\n","Accuracy 0.8364287541570734\n","2607/2615\n","Accuracy 0.8364147807185782\n","2608/2615\n","Accuracy 0.8364519427402863\n","2609/2615\n","Accuracy 0.8364635237000128\n","2610/2615\n","Accuracy 0.8364836100468285\n","last batch saved 10\n","2611/2615\n","Accuracy 0.8364441040044257\n","2612/2615\n","Accuracy 0.8364216436957631\n","2613/2615\n","Accuracy 0.8364417230088872\n","2614/2615\n","Accuracy 0.8364447844937516\n","2615/2615\n","Accuracy 0.8364648396005948\n","last batch saved 15\n","Final Accuracy= 0.8364648396005948\n"]}]},{"cell_type":"code","metadata":{"id":"tbyydwF3fQor","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1638437570490,"user_tz":480,"elapsed":764,"user":{"displayName":"zeeshan ahmad","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15348670853435150367"}},"outputId":"844ababc-5ef9-4060-8823-08d25075bfd8"},"source":["pred_df = pd.read_csv(pred_csv_file, header=None)\n","if (len(pred_df) != len(df_dev)):\n","  print(\"MISMATCH\")\n","print(\"f1_score\",f1_score(pred_df[1].tolist(), pred_df[0].tolist()))\n","print(\"MCC\", matthews_corrcoef(pred_df[1].tolist(), pred_df[0].tolist()))\n","# f1_score(pred_df[1].tolist(), pred_df[0].tolist(), average=None)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["MISMATCH\n","f1_score 0.8591738814155355\n","MCC 0.6868416814111098\n"]}]},{"cell_type":"markdown","metadata":{"id":"Bfw65BK0K3SK"},"source":["## Test - Prediction bert-base-uncased-2epoch-LR5e-5"]},{"cell_type":"code","metadata":{"id":"8rwSOjiKK8Dn"},"source":["import csv\n","\n","df_test = pd.read_csv(data_folder + \"/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/test_all_data_corrected_metadata.csv\" , usecols = ['tweet', 'label'])\n","test_pred_csv_file=\"/content/drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/Classifier/twiBot-360k-similarity-metadata-2epoch_LR5e-5_bert_base_uncased/classifier_model/checkpoint-44000/pred_test_twiBot-360k-similarity-metadata-2epoch_LR5e-5_bert_base_uncased.csv\""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"swuriWxpMzr4","executionInfo":{"status":"ok","timestamp":1638307232958,"user_tz":480,"elapsed":16,"user":{"displayName":"zeeshan ahmad","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15348670853435150367"}},"outputId":"0267a9f4-ac4b-4dca-dfd3-4ecbd4cf4989"},"source":["print(len(df_test))\n","print(df_test.iloc[0])\n","df_test = df_test.sample(frac=1, random_state=1).reset_index(drop=True)\n","print(df_test.iloc[0])"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["88035\n","tweet    SharonIsrael10 unprotected unverified defprof nondefimg 16596 16944 1 42684 49757 @ArtValley818_ CaliRed.\n","label                                                                                                            1\n","Name: 0, dtype: object\n","tweet    StarleeKine unprotected verified nondefprof nondefimg 50338 706 715 27692 18609 @GudGreaf @WeHaveSnacks I get the lack of enthusiasm. it is not an ideal situation. But it is the only way forward in this moment\n","label                                                                                                                                                                                                                    0\n","Name: 0, dtype: object\n"]}]},{"cell_type":"code","metadata":{"id":"ycOYFcpkL8TN","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1638321089518,"user_tz":480,"elapsed":4651973,"user":{"displayName":"zeeshan ahmad","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15348670853435150367"}},"outputId":"7beb3f22-067c-4c59-9140-75f688467c15"},"source":["#Check if we need to freeze params\n","model.eval()\n","pred_curated = []\n","batch_size = 15 # 88035 / 15 = 5869\n","count = 0\n","for i in range(0, len(df_test), batch_size):\n","  print(\"{}/{}\".format(int((i/batch_size)+1), int(len(df_test)/batch_size)))\n","  pt_batch = tokenizer(\n","      list(df_test[i:i+batch_size].tweet), #[\"We are very happy to show you the 🤗 Transformers library.\", \"We hope you don't hate it.\"],\n","      padding=True,\n","      truncation=True,\n","      max_length=512,\n","      return_tensors=\"pt\"\n","  )\n","  with torch.no_grad():\n","    prediction_logits = model(**pt_batch)\n","    prediction_results = torch.softmax(prediction_logits[0], dim=1)\n","    prediction_results = torch.argmax(prediction_results, dim=1)\n","    pred_curated.extend([[i,j] for i,j in zip(prediction_results.tolist(),df_test[i:i+batch_size].label.tolist())])\n","    # print(prediction_results.numpy().shape, np.array(df_test[i:i+batch_size].label).shape )\n","    # print(\"prediction_results\", prediction_results)\n","    # print(\"actual_results\", df_test[i:i+batch_size].label)\n","    count += np.sum(prediction_results.numpy() == np.array(df_test[i:i+batch_size].label))\n","    # print(\"count\", count, \"curr_total\", (i+batch_size))\n","    print(\"Accuracy\", count/(i+batch_size))\n","  # if (int((i/batch_size)+1)%5 ==0):\n","    \n","    # print(\"last batch saved\", str(int((i/batch_size)+1)%100))\n","with open(test_pred_csv_file,\"a\") as f:\n","  # x=pickle.load(f)\n","  # x.extend(pred_cul)\n","  # pickle.dump(pred_cul, f)\n","  # pred_cul = []\n","  f_writer=csv.writer(f)\n","  f_writer.writerows(pred_curated)\n","  pred_curated =[]\n","print(\"Final Accuracy=\", count/len(df_test))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","Accuracy 0.86\n","3371/5869\n","Accuracy 0.859982201127262\n","3372/5869\n","Accuracy 0.8599446421510478\n","3373/5869\n","Accuracy 0.8598873406463089\n","3374/5869\n","Accuracy 0.8599091088717645\n","3375/5869\n","Accuracy 0.8599111111111111\n","3376/5869\n","Accuracy 0.85989336492891\n","3377/5869\n","Accuracy 0.8599151120323759\n","3378/5869\n","Accuracy 0.859897375172686\n","3379/5869\n","Accuracy 0.8598599191082175\n","3380/5869\n","Accuracy 0.8598027613412229\n","3381/5869\n","Accuracy 0.8598442275460909\n","3382/5869\n","Accuracy 0.8598462448255471\n","3383/5869\n","Accuracy 0.8598679672874174\n","3384/5869\n","Accuracy 0.859869976359338\n","3385/5869\n","Accuracy 0.8598719842442146\n","3386/5869\n","Accuracy 0.8598543020279582\n","3387/5869\n","Accuracy 0.8598563133549847\n","3388/5869\n","Accuracy 0.8598386462022826\n","3389/5869\n","Accuracy 0.8598406609619357\n","3390/5869\n","Accuracy 0.8598623402163225\n","3391/5869\n","Accuracy 0.8598840066843606\n","3392/5869\n","Accuracy 0.8599056603773585\n","3393/5869\n","Accuracy 0.8598683564200805\n","3394/5869\n","Accuracy 0.8598114319387153\n","3395/5869\n","Accuracy 0.8597545409916544\n","3396/5869\n","Accuracy 0.8597762073027091\n","3397/5869\n","Accuracy 0.859778235698165\n","3398/5869\n","Accuracy 0.8597017853639396\n","3399/5869\n","Accuracy 0.8596842208492694\n","3400/5869\n","Accuracy 0.8596666666666667\n","3401/5869\n","Accuracy 0.8596687248848378\n","3402/5869\n","Accuracy 0.8595923966294337\n","3403/5869\n","Accuracy 0.8595748849054755\n","3404/5869\n","Accuracy 0.859596553074814\n","3405/5869\n","Accuracy 0.8595203132648067\n","3406/5869\n","Accuracy 0.8594441182227441\n","3407/5869\n","Accuracy 0.8594658056941591\n","3408/5869\n","Accuracy 0.8594679186228482\n","3409/5869\n","Accuracy 0.8594895863889703\n","3410/5869\n","Accuracy 0.8594721407624634\n","3411/5869\n","Accuracy 0.8595133391967165\n","3412/5869\n","Accuracy 0.8595545134818289\n","3413/5869\n","Accuracy 0.8595565973239574\n","3414/5869\n","Accuracy 0.8595196250732279\n","3415/5869\n","Accuracy 0.8594826744753539\n","3416/5869\n","Accuracy 0.8595042935206869\n","3417/5869\n","Accuracy 0.8595258999122037\n","3418/5869\n","Accuracy 0.8595084844938561\n","3419/5869\n","Accuracy 0.8594910792629423\n","3420/5869\n","Accuracy 0.8594931773879142\n","3421/5869\n","Accuracy 0.8595147617655656\n","3422/5869\n","Accuracy 0.8594584063900254\n","3423/5869\n","Accuracy 0.859421560035057\n","3424/5869\n","Accuracy 0.8594236760124611\n","3425/5869\n","Accuracy 0.859425790754258\n","3426/5869\n","Accuracy 0.859447363300253\n","3427/5869\n","Accuracy 0.8594883766170606\n","3428/5869\n","Accuracy 0.8594904706339945\n","3429/5869\n","Accuracy 0.8594731214153787\n","3430/5869\n","Accuracy 0.8594946550048591\n","3431/5869\n","Accuracy 0.8595161760419703\n","3432/5869\n","Accuracy 0.8594794094794095\n","3433/5869\n","Accuracy 0.8595203417807554\n","3434/5869\n","Accuracy 0.8595224228305184\n","3435/5869\n","Accuracy 0.85954391072295\n","3436/5869\n","Accuracy 0.8595265812960807\n","3437/5869\n","Accuracy 0.8595286587139948\n","3438/5869\n","Accuracy 0.8595501260422727\n","3439/5869\n","Accuracy 0.8595715808859165\n","3440/5869\n","Accuracy 0.8595348837209302\n","3441/5869\n","Accuracy 0.8595369563111499\n","3442/5869\n","Accuracy 0.8595196591129188\n","3443/5869\n","Accuracy 0.859521734921096\n","3444/5869\n","Accuracy 0.8595625241966706\n","3445/5869\n","Accuracy 0.8595645863570391\n","3446/5869\n","Accuracy 0.8595859934223254\n","3447/5869\n","Accuracy 0.859549366598975\n","3448/5869\n","Accuracy 0.8595514307811292\n","3449/5869\n","Accuracy 0.8595341644921233\n","3450/5869\n","Accuracy 0.8595555555555555\n","3451/5869\n","Accuracy 0.8595769342219647\n","3452/5869\n","Accuracy 0.8595983005021244\n","3453/5869\n","Accuracy 0.8595810406409885\n","3454/5869\n","Accuracy 0.8595444894807952\n","3455/5869\n","Accuracy 0.8595851423058369\n","3456/5869\n","Accuracy 0.8595293209876543\n","3457/5869\n","Accuracy 0.8595313855944461\n","3458/5869\n","Accuracy 0.8595334490071332\n","3459/5869\n","Accuracy 0.8595162378336706\n","3460/5869\n","Accuracy 0.8594990366088632\n","3461/5869\n","Accuracy 0.8595203698353077\n","3462/5869\n","Accuracy 0.8595031773541305\n","3463/5869\n","Accuracy 0.8594667436711907\n","3464/5869\n","Accuracy 0.8594688221709007\n","3465/5869\n","Accuracy 0.8594516594516595\n","3466/5869\n","Accuracy 0.8594729755722255\n","3467/5869\n","Accuracy 0.8594365926353236\n","3468/5869\n","Accuracy 0.8594579008073818\n","3469/5869\n","Accuracy 0.8594984145286826\n","3470/5869\n","Accuracy 0.859462055715658\n","3471/5869\n","Accuracy 0.8594257178526842\n","3472/5869\n","Accuracy 0.8594278033794163\n","3473/5869\n","Accuracy 0.859429887705154\n","3474/5869\n","Accuracy 0.859412780656304\n","3475/5869\n","Accuracy 0.8593764988009592\n","3476/5869\n","Accuracy 0.8594169543536632\n","3477/5869\n","Accuracy 0.8593998657846803\n","3478/5869\n","Accuracy 0.859401955146636\n","3479/5869\n","Accuracy 0.8594040433074638\n","3480/5869\n","Accuracy 0.8593869731800766\n","3481/5869\n","Accuracy 0.8593890644450828\n","3482/5869\n","Accuracy 0.8593911545089029\n","3483/5869\n","Accuracy 0.8593741027849555\n","3484/5869\n","Accuracy 0.8593379257558362\n","3485/5869\n","Accuracy 0.8593782879005261\n","3486/5869\n","Accuracy 0.8593803786574871\n","3487/5869\n","Accuracy 0.8593824682152758\n","3488/5869\n","Accuracy 0.8593845565749235\n","3489/5869\n","Accuracy 0.8593866437374605\n","3490/5869\n","Accuracy 0.8594078319006686\n","3491/5869\n","Accuracy 0.8593526210254941\n","3492/5869\n","Accuracy 0.8593928980526919\n","3493/5869\n","Accuracy 0.8592995514839202\n","3494/5869\n","Accuracy 0.8593016599885518\n","3495/5869\n","Accuracy 0.8592846924177396\n","3496/5869\n","Accuracy 0.8592677345537757\n","3497/5869\n","Accuracy 0.8592889143075017\n","3498/5869\n","Accuracy 0.8592910234419668\n","3499/5869\n","Accuracy 0.859274078308088\n","3500/5869\n","Accuracy 0.8592761904761905\n","3501/5869\n","Accuracy 0.8592783014376845\n","3502/5869\n","Accuracy 0.8592994479345136\n","3503/5869\n","Accuracy 0.8592254258254829\n","3504/5869\n","Accuracy 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0.8606060606060606\n","5754/5869\n","Accuracy 0.8606187000347584\n","5755/5869\n","Accuracy 0.860596582681726\n","5756/5869\n","Accuracy 0.8605976372480889\n","5757/5869\n","Accuracy 0.8605871113427133\n","5758/5869\n","Accuracy 0.8605997452819266\n","5759/5869\n","Accuracy 0.8605776465821613\n","5760/5869\n","Accuracy 0.8605787037037037\n","5761/5869\n","Accuracy 0.8605566163281838\n","5762/5869\n","Accuracy 0.860557676732616\n","5763/5869\n","Accuracy 0.8605703048180924\n","5764/5869\n","Accuracy 0.8605944945639602\n","5765/5869\n","Accuracy 0.8605608557386528\n","5766/5869\n","Accuracy 0.8605619146722164\n","5767/5869\n","Accuracy 0.8605745332639732\n","5768/5869\n","Accuracy 0.8605524734165511\n","5769/5869\n","Accuracy 0.8605650892702374\n","5770/5869\n","Accuracy 0.8605545927209706\n","5771/5869\n","Accuracy 0.8605556518223301\n","5772/5869\n","Accuracy 0.8605567105567106\n","5773/5869\n","Accuracy 0.8605462209134477\n","5774/5869\n","Accuracy 0.8605703729361506\n","5775/5869\n","Accuracy 0.8605483405483405\n","5776/5869\n","Accuracy 0.8605147737765466\n","5777/5869\n","Accuracy 0.8605042986555883\n","5778/5869\n","Accuracy 0.8604938271604938\n","5779/5869\n","Accuracy 0.8604948953106074\n","5780/5869\n","Accuracy 0.8604959630911188\n","5781/5869\n","Accuracy 0.8604970305022199\n","5782/5869\n","Accuracy 0.8605096275798455\n","5783/5869\n","Accuracy 0.8605106922589199\n","5784/5869\n","Accuracy 0.8604771784232366\n","5785/5869\n","Accuracy 0.8604897723998848\n","5786/5869\n","Accuracy 0.8604793178937665\n","5787/5869\n","Accuracy 0.8604919071482058\n","5788/5869\n","Accuracy 0.8604929739691315\n","5789/5869\n","Accuracy 0.8604825243277481\n","5790/5869\n","Accuracy 0.8604951065054692\n","5791/5869\n","Accuracy 0.8604846601047602\n","5792/5869\n","Accuracy 0.8604972375690608\n","5793/5869\n","Accuracy 0.860486794407043\n","5794/5869\n","Accuracy 0.8604993671614314\n","5795/5869\n","Accuracy 0.8604774230658614\n","5796/5869\n","Accuracy 0.860501495284104\n","5797/5869\n","Accuracy 0.8605255591972859\n","5798/5869\n","Accuracy 0.8604921237208233\n","5799/5869\n","Accuracy 0.8604931884807725\n","5800/5869\n","Accuracy 0.8604712643678161\n","5801/5869\n","Accuracy 0.8604953168993852\n","5802/5869\n","Accuracy 0.8604848902677238\n","5803/5869\n","Accuracy 0.8604744672295939\n","5804/5869\n","Accuracy 0.8604640477831381\n","5805/5869\n","Accuracy 0.8604536319265002\n","5806/5869\n","Accuracy 0.8604547020323803\n","5807/5869\n","Accuracy 0.8604787325641468\n","5808/5869\n","Accuracy 0.8604912764003673\n","5809/5869\n","Accuracy 0.8604923394732312\n","5810/5869\n","Accuracy 0.8604704532415376\n","5811/5869\n","Accuracy 0.8604715195319222\n","5812/5869\n","Accuracy 0.8604840559761413\n","5813/5869\n","Accuracy 0.8604851195596078\n","5814/5869\n","Accuracy 0.8604747162022703\n","5815/5869\n","Accuracy 0.86048724562912\n","5816/5869\n","Accuracy 0.8604997707473636\n","5817/5869\n","Accuracy 0.8604893702366626\n","5818/5869\n","Accuracy 0.8605133493755013\n","5819/5869\n","Accuracy 0.8605029501059747\n","5820/5869\n","Accuracy 0.8605269186712485\n","5821/5869\n","Accuracy 0.8605508790013171\n","5822/5869\n","Accuracy 0.8605290278254896\n","5823/5869\n","Accuracy 0.8605300818592936\n","5824/5869\n","Accuracy 0.8605425824175824\n","5825/5869\n","Accuracy 0.8605550786838341\n","5826/5869\n","Accuracy 0.8605561277033985\n","5827/5869\n","Accuracy 0.8605571763629083\n","5828/5869\n","Accuracy 0.8605353466026081\n","5829/5869\n","Accuracy 0.8605478355349688\n","5830/5869\n","Accuracy 0.860548885077187\n","5831/5869\n","Accuracy 0.8605270679700452\n","5832/5869\n","Accuracy 0.8605052583447645\n","5833/5869\n","Accuracy 0.8605177438710784\n","5834/5869\n","Accuracy 0.860484516055308\n","5835/5869\n","Accuracy 0.8604741502427877\n","5836/5869\n","Accuracy 0.8604409412839845\n","5837/5869\n","Accuracy 0.8604648506652961\n","5838/5869\n","Accuracy 0.8604430741121388\n","5839/5869\n","Accuracy 0.8604327224981446\n","5840/5869\n","Accuracy 0.8604566210045662\n","5841/5869\n","Accuracy 0.8604576841864977\n","5842/5869\n","Accuracy 0.8604473353874244\n","5843/5869\n","Accuracy 0.8604712191225968\n","5844/5869\n","Accuracy 0.8604836869723933\n","5845/5869\n","Accuracy 0.8604961505560308\n","5846/5869\n","Accuracy 0.8605086098756984\n","5847/5869\n","Accuracy 0.8605210649335842\n","5848/5869\n","Accuracy 0.8605107159142726\n","5849/5869\n","Accuracy 0.860534564312988\n","5850/5869\n","Accuracy 0.8605470085470085\n","5851/5869\n","Accuracy 0.8605594485273173\n","5852/5869\n","Accuracy 0.8605604921394395\n","5853/5869\n","Accuracy 0.8605729255652372\n","5854/5869\n","Accuracy 0.8605853547431955\n","5855/5869\n","Accuracy 0.8605750071164248\n","5856/5869\n","Accuracy 0.8605760473588342\n","5857/5869\n","Accuracy 0.8605998520289113\n","5858/5869\n","Accuracy 0.8606008876749744\n","5859/5869\n","Accuracy 0.8606246799795186\n","5860/5869\n","Accuracy 0.8606257110352673\n","5861/5869\n","Accuracy 0.8606267417391799\n","5862/5869\n","Accuracy 0.8606277720914364\n","5863/5869\n","Accuracy 0.8606060606060606\n","5864/5869\n","Accuracy 0.8606298317417008\n","5865/5869\n","Accuracy 0.8606308610400683\n","5866/5869\n","Accuracy 0.8606091601318332\n","5867/5869\n","Accuracy 0.8606215555934322\n","5868/5869\n","Accuracy 0.86062258577596\n","5869/5869\n","Accuracy 0.8606236156074288\n","Final Accuracy= 0.8606236156074288\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZgDaUMSpRPyb","executionInfo":{"status":"ok","timestamp":1638321319580,"user_tz":480,"elapsed":465,"user":{"displayName":"zeeshan ahmad","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15348670853435150367"}},"outputId":"08ac5ee2-6d62-435f-f5e2-3ef274b93d7a"},"source":["from sklearn.metrics import f1_score,matthews_corrcoef\n","pred_df = pd.read_csv(test_pred_csv_file, header=None)\n","if (len(pred_df) != len(df_test)):\n","  print(\"MISMATCH\")\n","print(\"f1_score\",f1_score(pred_df[1].tolist(), pred_df[0].tolist()))\n","print(\"MCC\", matthews_corrcoef(pred_df[1].tolist(), pred_df[0].tolist()))\n","# f1_score(pred_df[1].tolist(), pred_df[0].tolist(), average=None)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["f1_score 0.8683645882504398\n","MCC 0.7272838572815044\n"]}]},{"cell_type":"markdown","metadata":{"id":"2DajdqRSs9Rn"},"source":["## Test - Prediction 01 - 01 = 360k model, no freeze on params\n","bert-base-uncased-2epoch-LR5e-5"]},{"cell_type":"code","metadata":{"id":"p7gWRDRstEpH"},"source":["df_test = pd.read_csv(data_folder + \"/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/test_all_data_corrected_metadata.csv\" , usecols = ['tweet', 'label'])\n","test_pred_csv_file=\"/content/drive/MyDrive/CSCI_544_NLP_Project/Twitter_Bot_Detection/Data/Model_TwiBot_Similarity_With_Metadata_360k_Tweets/Classifier/01-nofreeze-twiBot-360k-similarity-metadata-2epoch_LR5e-5_bert_base_uncased/classifier_model/checkpoint-22000/pred_test_twiBot-360k-similarity-metadata-2epoch_LR5e-5_bert_base_uncased.csv\""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YJnPo5IxtQGh","executionInfo":{"status":"ok","timestamp":1638340578421,"user_tz":480,"elapsed":217,"user":{"displayName":"zeeshan ahmad","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15348670853435150367"}},"outputId":"ce7c7ec9-0b73-4a07-d0fa-3e0b8daeeba7"},"source":["print(len(df_test))\n","print(df_test.iloc[0])\n","df_test = df_test.sample(frac=1, random_state=1).reset_index(drop=True)\n","print(df_test.iloc[0])"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["88035\n","tweet    SharonIsrael10 unprotected unverified defprof nondefimg 16596 16944 1 42684 49757 @ArtValley818_ CaliRed.\n","label                                                                                                            1\n","Name: 0, dtype: object\n","tweet    StarleeKine unprotected verified nondefprof nondefimg 50338 706 715 27692 18609 @GudGreaf @WeHaveSnacks I get the lack of enthusiasm. it is not an ideal situation. But it is the only way forward in this moment\n","label                                                                                                                                                                                                                    0\n","Name: 0, dtype: object\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"B0l2sJ4CtSd6","executionInfo":{"status":"ok","timestamp":1638354879490,"user_tz":480,"elapsed":14285352,"user":{"displayName":"zeeshan ahmad","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15348670853435150367"}},"outputId":"6201f45d-fe8f-4e8b-c701-5ec2b412791c"},"source":["#Check if we need to freeze params\n","model.eval()\n","pred_curated = []\n","batch_size = 15 # 88035 / 15 = 5869\n","count = 0\n","for i in range(0, len(df_test), batch_size):\n","  print(\"{}/{}\".format(int((i/batch_size)+1), int(len(df_test)/batch_size)))\n","  pt_batch = tokenizer(\n","      list(df_test[i:i+batch_size].tweet), #[\"We are very happy to show you the 🤗 Transformers library.\", \"We hope you don't hate it.\"],\n","      padding=True,\n","      truncation=True,\n","      max_length=512,\n","      return_tensors=\"pt\"\n","  )\n","  with torch.no_grad():\n","    prediction_logits = model(**pt_batch)\n","    prediction_results = torch.softmax(prediction_logits[0], dim=1)\n","    prediction_results = torch.argmax(prediction_results, dim=1)\n","    pred_curated.extend([[i,j] for i,j in zip(prediction_results.tolist(),df_test[i:i+batch_size].label.tolist())])\n","    # print(prediction_results.numpy().shape, np.array(df_test[i:i+batch_size].label).shape )\n","    # print(\"prediction_results\", prediction_results)\n","    # print(\"actual_results\", df_test[i:i+batch_size].label)\n","    count += np.sum(prediction_results.numpy() == np.array(df_test[i:i+batch_size].label))\n","    # print(\"count\", count, \"curr_total\", (i+batch_size))\n","    print(\"Accuracy\", count/(i+batch_size))\n","  # if (int((i/batch_size)+1)%5 ==0):\n","    \n","    # print(\"last batch saved\", str(int((i/batch_size)+1)%100))\n","with open(test_pred_csv_file,\"a\") as f:\n","  # x=pickle.load(f)\n","  # x.extend(pred_cul)\n","  # pickle.dump(pred_cul, f)\n","  # pred_cul = []\n","  f_writer=csv.writer(f)\n","  f_writer.writerows(pred_curated)\n","  pred_curated =[]\n","print(\"Final Accuracy=\", count/len(df_test))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","Accuracy 0.8470029673590505\n","3371/5869\n","Accuracy 0.8469890240284782\n","3372/5869\n","Accuracy 0.8469750889679716\n","3373/5869\n","Accuracy 0.8469413973712817\n","3374/5869\n","Accuracy 0.8469670025686623\n","3375/5869\n","Accuracy 0.8469728395061729\n","3376/5869\n","Accuracy 0.8469391785150079\n","3377/5869\n","Accuracy 0.8469845030105616\n","3378/5869\n","Accuracy 0.8469705940398657\n","3379/5869\n","Accuracy 0.8469566933017658\n","3380/5869\n","Accuracy 0.8469230769230769\n","3381/5869\n","Accuracy 0.8469683525584146\n","3382/5869\n","Accuracy 0.8469741770155726\n","3383/5869\n","Accuracy 0.8469799980293625\n","3384/5869\n","Accuracy 0.8469661150512214\n","3385/5869\n","Accuracy 0.8469719350073855\n","3386/5869\n","Accuracy 0.8469580626107501\n","3387/5869\n","Accuracy 0.8469441984056687\n","3388/5869\n","Accuracy 0.8469106650924833\n","3389/5869\n","Accuracy 0.8468771515688011\n","3390/5869\n","Accuracy 0.8469026548672567\n","3391/5869\n","Accuracy 0.8469281431239556\n","3392/5869\n","Accuracy 0.8469536163522012\n","3393/5869\n","Accuracy 0.846959426269771\n","3394/5869\n","Accuracy 0.8469063052445492\n","3395/5869\n","Accuracy 0.8468532155130093\n","3396/5869\n","Accuracy 0.8468983117393012\n","3397/5869\n","Accuracy 0.8469041310960651\n","3398/5869\n","Accuracy 0.8468510888758093\n","3399/5869\n","Accuracy 0.8468569187015789\n","3400/5869\n","Accuracy 0.846843137254902\n","3401/5869\n","Accuracy 0.8468489659903949\n","3402/5869\n","Accuracy 0.8467960023515579\n","3403/5869\n","Accuracy 0.8467822509550397\n","3404/5869\n","Accuracy 0.8467880924402663\n","3405/5869\n","Accuracy 0.8466960352422908\n","3406/5869\n","Accuracy 0.8466040321002153\n","3407/5869\n","Accuracy 0.8466294883083847\n","3408/5869\n","Accuracy 0.8466353677621283\n","3409/5869\n","Accuracy 0.8466607998435514\n","3410/5869\n","Accuracy 0.8466471163245357\n","3411/5869\n","Accuracy 0.8466725300498388\n","3412/5869\n","Accuracy 0.8466588511137163\n","3413/5869\n","Accuracy 0.8466647133509132\n","3414/5869\n","Accuracy 0.8465924624096856\n","3415/5869\n","Accuracy 0.8465788189360663\n","3416/5869\n","Accuracy 0.8465651834504293\n","3417/5869\n","Accuracy 0.8465320456540826\n","3418/5869\n","Accuracy 0.8465184318314803\n","3419/5869\n","Accuracy 0.8465048259725065\n","3420/5869\n","Accuracy 0.8464912280701754\n","3421/5869\n","Accuracy 0.8465166130760986\n","3422/5869\n","Accuracy 0.8465030196766024\n","3423/5869\n","Accuracy 0.8464699581263998\n","3424/5869\n","Accuracy 0.8464758566978193\n","3425/5869\n","Accuracy 0.8464817518248176\n","3426/5869\n","Accuracy 0.8464681844716871\n","3427/5869\n","Accuracy 0.8465129851181792\n","3428/5869\n","Accuracy 0.8465188642551537\n","3429/5869\n","Accuracy 0.8465247399630602\n","3430/5869\n","Accuracy 0.846530612244898\n","3431/5869\n","Accuracy 0.8465364811036626\n","3432/5869\n","Accuracy 0.8465423465423465\n","3433/5869\n","Accuracy 0.8465482085639382\n","3434/5869\n","Accuracy 0.8465734808774995\n","3435/5869\n","Accuracy 0.8465793304221252\n","3436/5869\n","Accuracy 0.8465463717500971\n","3437/5869\n","Accuracy 0.846571622539036\n","3438/5869\n","Accuracy 0.846577467519876\n","3439/5869\n","Accuracy 0.8466026945817583\n","3440/5869\n","Accuracy 0.8465891472868217\n","3441/5869\n","Accuracy 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header=None)\n","if (len(pred_df) != len(df_test)):\n","  print(\"MISMATCH\")\n","print(\"f1_score\",f1_score(pred_df[1].tolist(), pred_df[0].tolist()))\n","print(\"MCC\", matthews_corrcoef(pred_df[1].tolist(), pred_df[0].tolist()))\n","# f1_score(pred_df[1].tolist(), pred_df[0].tolist(), average=None)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["f1_score 0.8507313829787234\n","MCC 0.6952919178055991\n"]}]}]}