| 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 | |
|  | |