---
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_1800
  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. -->

# VF_MatSciBERT_ST_1800

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: 0.1571
- Precision: 0.9763
- Recall: 0.9819
- F1: 0.9791
- Accuracy: 0.9755

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1492        | 1.0   | 569  | 0.0954          | 0.9709    | 0.9754 | 0.9732 | 0.9704   |
| 0.0548        | 2.0   | 1138 | 0.0934          | 0.9726    | 0.9785 | 0.9756 | 0.9726   |
| 0.0348        | 3.0   | 1707 | 0.1098          | 0.9749    | 0.9801 | 0.9775 | 0.9738   |
| 0.0213        | 4.0   | 2276 | 0.1268          | 0.9739    | 0.9813 | 0.9776 | 0.9735   |
| 0.0141        | 5.0   | 2845 | 0.1326          | 0.9748    | 0.9806 | 0.9777 | 0.9740   |
| 0.0093        | 6.0   | 3414 | 0.1402          | 0.9750    | 0.9808 | 0.9779 | 0.9743   |
| 0.0062        | 7.0   | 3983 | 0.1541          | 0.9741    | 0.9805 | 0.9773 | 0.9733   |
| 0.0033        | 8.0   | 4552 | 0.1682          | 0.9741    | 0.9814 | 0.9777 | 0.9732   |
| 0.0026        | 9.0   | 5121 | 0.1638          | 0.9749    | 0.9821 | 0.9785 | 0.9743   |
| 0.0021        | 10.0  | 5690 | 0.1571          | 0.9763    | 0.9819 | 0.9791 | 0.9755   |


### Framework versions

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