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Dummy Model for Lab4
This model is a fine-tuned version of bert-base-uncased on SST-2 dataset.
Results of the evaluation set:
Accuracy: 0.64
This model was fine-tuneded for personal research usage.
with randomly selected 100 training datas and 100 evaluation datas from SST-2 dataset.
# Evaluation
import evaluate
predictions = trainer.predict(Resrt_eval)
print(predictions.predictions.shape, predictions.label_ids.shape)
preds = np.argmax(predictions.predictions, axis=-1)
metric = evaluate.load("glue", "sst2")
metric.compute(predictions=preds, references=predictions.label_ids)
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: unset
train_batch_size: unset
eval_batch_size: unset
seed of training dataset: 49282927487
seed of evaluation dataset:492829487
lr_scheduler_type: linear
num_epochs: 3.0
Training results
Epoch Training Loss Validation Loss
1 No log 0.674658 0.480000
2 No log 0.640980 0.600000
3 No log 0.640266 0.640000
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