---
library_name: transformers
license: mit
base_model: m3rg-iitd/matscibert
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: MatSciBERT_ST_DA_100
  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. -->

# MatSciBERT_ST_DA_100

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: 0.2043
- Precision: 0.9627
- Recall: 0.9693
- F1: 0.9660
- Accuracy: 0.9561

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 59   | 0.2685          | 0.9263    | 0.9420 | 0.9341 | 0.9213   |
| No log        | 2.0   | 118  | 0.1935          | 0.9477    | 0.9573 | 0.9524 | 0.9429   |
| No log        | 3.0   | 177  | 0.2043          | 0.9558    | 0.9669 | 0.9613 | 0.9506   |
| No log        | 4.0   | 236  | 0.1769          | 0.9596    | 0.9701 | 0.9648 | 0.9554   |
| No log        | 5.0   | 295  | 0.1789          | 0.9619    | 0.9686 | 0.9652 | 0.9561   |
| No log        | 6.0   | 354  | 0.1916          | 0.9620    | 0.9683 | 0.9651 | 0.9557   |
| No log        | 7.0   | 413  | 0.1955          | 0.9623    | 0.9685 | 0.9654 | 0.9559   |
| No log        | 8.0   | 472  | 0.2002          | 0.9627    | 0.9713 | 0.9670 | 0.9575   |
| 0.1044        | 9.0   | 531  | 0.2033          | 0.9632    | 0.9698 | 0.9665 | 0.9566   |
| 0.1044        | 10.0  | 590  | 0.2043          | 0.9627    | 0.9693 | 0.9660 | 0.9561   |


### Framework versions

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