---
library_name: transformers
license: mit
base_model: m3rg-iitd/matscibert
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ST_MAT
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# ST_MAT

This model is a fine-tuned version of [m3rg-iitd/matscibert](https://huggingface.co/m3rg-iitd/matscibert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1551
- Precision: 0.8250
- Recall: 0.8333
- F1: 0.8291
- Accuracy: 0.9766

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1259        | 1.0   | 569  | 0.0862          | 0.8117    | 0.7998 | 0.8057 | 0.9742   |
| 0.0476        | 2.0   | 1138 | 0.0909          | 0.8065    | 0.8154 | 0.8109 | 0.9741   |
| 0.0296        | 3.0   | 1707 | 0.1032          | 0.8039    | 0.8232 | 0.8134 | 0.9739   |
| 0.0196        | 4.0   | 2276 | 0.1157          | 0.8054    | 0.8203 | 0.8128 | 0.9745   |
| 0.0118        | 5.0   | 2845 | 0.1182          | 0.8300    | 0.8311 | 0.8305 | 0.9768   |
| 0.0074        | 6.0   | 3414 | 0.1399          | 0.8204    | 0.8151 | 0.8178 | 0.9753   |
| 0.0053        | 7.0   | 3983 | 0.1445          | 0.8334    | 0.8223 | 0.8278 | 0.9765   |
| 0.0025        | 8.0   | 4552 | 0.1521          | 0.8218    | 0.8288 | 0.8253 | 0.9758   |
| 0.0023        | 9.0   | 5121 | 0.1555          | 0.8215    | 0.8255 | 0.8235 | 0.9759   |
| 0.0016        | 10.0  | 5690 | 0.1551          | 0.8250    | 0.8333 | 0.8291 | 0.9766   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1