metadata
library_name: transformers
license: mit
base_model: m3rg-iitd/matscibert
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: VF_MatSciBERT_ST_1000
results: []
VF_MatSciBERT_ST_1000
This model is a fine-tuned version of m3rg-iitd/matscibert on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1512
- Precision: 0.9771
- Recall: 0.9820
- F1: 0.9795
- Accuracy: 0.9743
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1768 | 1.0 | 517 | 0.1073 | 0.9722 | 0.9763 | 0.9742 | 0.9696 |
| 0.0579 | 2.0 | 1034 | 0.1096 | 0.9733 | 0.9781 | 0.9757 | 0.9696 |
| 0.0331 | 3.0 | 1551 | 0.1087 | 0.9763 | 0.9800 | 0.9781 | 0.9725 |
| 0.0205 | 4.0 | 2068 | 0.1311 | 0.9744 | 0.9801 | 0.9772 | 0.9707 |
| 0.0119 | 5.0 | 2585 | 0.1321 | 0.9765 | 0.9824 | 0.9795 | 0.9737 |
| 0.0076 | 6.0 | 3102 | 0.1450 | 0.9760 | 0.9809 | 0.9785 | 0.9725 |
| 0.0043 | 7.0 | 3619 | 0.1511 | 0.9750 | 0.9813 | 0.9782 | 0.9719 |
| 0.003 | 8.0 | 4136 | 0.1480 | 0.9769 | 0.9811 | 0.9790 | 0.9740 |
| 0.0021 | 9.0 | 4653 | 0.1494 | 0.9773 | 0.9824 | 0.9798 | 0.9747 |
| 0.0018 | 10.0 | 5170 | 0.1512 | 0.9771 | 0.9820 | 0.9795 | 0.9743 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1