metadata
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: SciBERT_TwoWayLoss_25K_bs64_P10_N5
results: []
SciBERT_TwoWayLoss_25K_bs64_P10_N5
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: 15.1250
- Accuracy: 0.7066
- Precision: 0.0321
- Recall: 0.9982
- F1: 0.0622
- Hamming: 0.2934
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 25000
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Hamming |
|---|---|---|---|---|---|---|---|---|
| 28.5732 | 0.16 | 5000 | 26.4288 | 0.6945 | 0.0307 | 0.9910 | 0.0595 | 0.3055 |
| 19.8755 | 0.32 | 10000 | 18.9620 | 0.7010 | 0.0315 | 0.9959 | 0.0610 | 0.2990 |
| 17.1294 | 0.47 | 15000 | 16.5587 | 0.7021 | 0.0316 | 0.9970 | 0.0613 | 0.2979 |
| 15.8209 | 0.63 | 20000 | 15.4919 | 0.7053 | 0.0320 | 0.9982 | 0.0620 | 0.2947 |
| 15.4304 | 0.79 | 25000 | 15.1250 | 0.7066 | 0.0321 | 0.9982 | 0.0622 | 0.2934 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3