metadata
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: scibert_claim_id_2e-05
results: []
scibert_claim_id_2e-05
This model is a fine-tuned version of allenai/scibert_scivocab_uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0162
- Accuracy: 0.9962
- F1: 0.9880
- Precision: 0.9889
- Recall: 0.9870
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.3131 | 1.0 | 666 | 0.2551 | 0.8880 | 0.5518 | 0.7419 | 0.4392 |
| 0.267 | 2.0 | 1332 | 0.1821 | 0.9280 | 0.7636 | 0.7875 | 0.7410 |
| 0.2245 | 3.0 | 1998 | 0.0942 | 0.9695 | 0.9034 | 0.8968 | 0.9101 |
| 0.1135 | 4.0 | 2664 | 0.0514 | 0.9845 | 0.9517 | 0.9339 | 0.9702 |
| 0.0821 | 5.0 | 3330 | 0.0223 | 0.9944 | 0.9822 | 0.9808 | 0.9837 |
| 0.0618 | 6.0 | 3996 | 0.0162 | 0.9962 | 0.9880 | 0.9889 | 0.9870 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3