Scicite_classification_model
This model is a fine-tuned version of allenai/scibert_scivocab_uncased on the scicite dataset. It achieves the following results on the evaluation set:
- Loss: 0.4704
- Accuracy: 0.9225
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2493 | 1.0 | 513 | 0.2034 | 0.9214 |
| 0.1777 | 2.0 | 1026 | 0.1942 | 0.9247 |
| 0.1385 | 3.0 | 1539 | 0.2552 | 0.9247 |
| 0.1019 | 4.0 | 2052 | 0.2995 | 0.9258 |
| 0.0705 | 5.0 | 2565 | 0.3964 | 0.9181 |
| 0.0444 | 6.0 | 3078 | 0.4243 | 0.9203 |
| 0.0331 | 7.0 | 3591 | 0.4904 | 0.9192 |
| 0.0223 | 8.0 | 4104 | 0.4704 | 0.9225 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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