Update models.py

src/tasknet/models.py

@@ -342,15 +342,14 @@ class default:
  save_steps = 1000000
  label_names = ["labels"]
  include_inputs_for_metrics = True
- model_name = "sileod/deberta-v3-base-tasksource-nli"
 
  default, hparams = to_dict(default), to_dict(hparams)
  self.p = hparams.get('p', 1)
  self.num_proc = hparams.get('num_proc',None)
  self.batched = hparams.get('batched',False)
 
  trainer_args = transformers.TrainingArguments(
- **fc.project({**default,**hparams}, dir(transformers.TrainingArguments))
+  **{**default, **fc.project(hparams, dir(transformers.TrainingArguments))},
  )
  if not tokenizer:
  tokenizer = AutoTokenizer.from_pretrained(hparams["model_name"])
@@ -383,7 +382,7 @@ class default:
  task: dataset["test"]
  for task, dataset in self.processed_tasks.items()
  }
- # We preventstrainer from automatically evaluating on each dataset:
+ # We prevent Trainer from automatically evaluating on each dataset:
  # transformers.Trainer recognizes eval_dataset instances of "dict"
  # But we use a custom "evaluate" function so that we can use different metrics for each task
  self.eval_dataset = MappingProxyType(self.eval_dataset)