---
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_1000
  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_1000

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: 0.1669
- Precision: 0.8484
- Recall: 0.8572
- F1: 0.8528
- Accuracy: 0.9724

## 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   | 495  | 0.1097          | 0.8373    | 0.8310 | 0.8341 | 0.9692   |
| 0.1746        | 2.0   | 990  | 0.0968          | 0.8355    | 0.8550 | 0.8452 | 0.9720   |
| 0.0592        | 3.0   | 1485 | 0.1072          | 0.8405    | 0.8497 | 0.8451 | 0.9711   |
| 0.0316        | 4.0   | 1980 | 0.1302          | 0.8451    | 0.8468 | 0.8459 | 0.9709   |
| 0.017         | 5.0   | 2475 | 0.1426          | 0.8381    | 0.8448 | 0.8415 | 0.9702   |
| 0.0102        | 6.0   | 2970 | 0.1503          | 0.8456    | 0.8470 | 0.8463 | 0.9711   |
| 0.0058        | 7.0   | 3465 | 0.1528          | 0.8466    | 0.8509 | 0.8487 | 0.9721   |
| 0.0035        | 8.0   | 3960 | 0.1565          | 0.8459    | 0.8521 | 0.8490 | 0.9719   |
| 0.0027        | 9.0   | 4455 | 0.1592          | 0.8531    | 0.8562 | 0.8547 | 0.9728   |
| 0.0017        | 10.0  | 4950 | 0.1669          | 0.8484    | 0.8572 | 0.8528 | 0.9724   |


### Framework versions

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