---
library_name: transformers
license: mit
base_model: m3rg-iitd/matscibert
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ST_CEMS
  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_CEMS

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: 0.0598
- Precision: 0.9368
- Recall: 0.9226
- F1: 0.9296
- Accuracy: 0.9898

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0492        | 1.0   | 569  | 0.0347          | 0.9181    | 0.9091 | 0.9136 | 0.9881   |
| 0.0177        | 2.0   | 1138 | 0.0331          | 0.9406    | 0.9177 | 0.9290 | 0.9905   |
| 0.0109        | 3.0   | 1707 | 0.0454          | 0.9116    | 0.9122 | 0.9119 | 0.9876   |
| 0.0066        | 4.0   | 2276 | 0.0454          | 0.9596    | 0.8970 | 0.9272 | 0.9896   |
| 0.0042        | 5.0   | 2845 | 0.0477          | 0.9352    | 0.9061 | 0.9204 | 0.9889   |
| 0.0027        | 6.0   | 3414 | 0.0525          | 0.9352    | 0.9146 | 0.9248 | 0.9896   |
| 0.0018        | 7.0   | 3983 | 0.0498          | 0.9405    | 0.9159 | 0.9280 | 0.9899   |
| 0.0008        | 8.0   | 4552 | 0.0555          | 0.9312    | 0.9238 | 0.9275 | 0.9896   |
| 0.0007        | 9.0   | 5121 | 0.0602          | 0.9406    | 0.9165 | 0.9284 | 0.9897   |
| 0.0006        | 10.0  | 5690 | 0.0598          | 0.9368    | 0.9226 | 0.9296 | 0.9898   |


### Framework versions

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