---
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: 0.1894
- Precision: 0.7360
- Recall: 0.7770
- F1: 0.7560
- Accuracy: 0.9556

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 30   | 0.3205          | 0.4448    | 0.2093 | 0.2847 | 0.9095   |
| No log        | 2.0   | 60   | 0.2400          | 0.6346    | 0.6075 | 0.6208 | 0.9376   |
| No log        | 3.0   | 90   | 0.2236          | 0.6764    | 0.7010 | 0.6885 | 0.9450   |
| No log        | 4.0   | 120  | 0.1959          | 0.6664    | 0.7127 | 0.6888 | 0.9453   |
| No log        | 5.0   | 150  | 0.1958          | 0.7177    | 0.7514 | 0.7342 | 0.9519   |
| No log        | 6.0   | 180  | 0.1802          | 0.7180    | 0.7666 | 0.7415 | 0.9541   |
| No log        | 7.0   | 210  | 0.1911          | 0.7316    | 0.7668 | 0.7488 | 0.9546   |
| No log        | 8.0   | 240  | 0.1914          | 0.7384    | 0.7711 | 0.7544 | 0.9554   |
| No log        | 9.0   | 270  | 0.1873          | 0.7366    | 0.7745 | 0.7551 | 0.9556   |
| No log        | 10.0  | 300  | 0.1894          | 0.7360    | 0.7770 | 0.7560 | 0.9556   |


### Framework versions

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