---
license: mit
base_model: m3rg-iitd/matscibert
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: MatSciBERT_BIOMAT_NER1800
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# MatSciBERT_BIOMAT_NER1800

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: 0.1788
- Precision: 0.9841
- Recall: 0.9758
- F1: 0.9799
- Accuracy: 0.9728

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.186         | 1.0   | 869  | 0.0890          | 0.9831    | 0.9762 | 0.9796 | 0.9730   |
| 0.0519        | 2.0   | 1738 | 0.0944          | 0.9834    | 0.9773 | 0.9803 | 0.9744   |
| 0.0293        | 3.0   | 2607 | 0.1101          | 0.9832    | 0.9748 | 0.9790 | 0.9721   |
| 0.0185        | 4.0   | 3476 | 0.1348          | 0.9823    | 0.9752 | 0.9788 | 0.9721   |
| 0.0086        | 5.0   | 4345 | 0.1421          | 0.9823    | 0.9746 | 0.9785 | 0.9715   |
| 0.0054        | 6.0   | 5214 | 0.1755          | 0.9835    | 0.9719 | 0.9777 | 0.9693   |
| 0.0032        | 7.0   | 6083 | 0.1706          | 0.9831    | 0.9735 | 0.9783 | 0.9709   |
| 0.0027        | 8.0   | 6952 | 0.1774          | 0.9840    | 0.9756 | 0.9798 | 0.9729   |
| 0.0017        | 9.0   | 7821 | 0.1825          | 0.9841    | 0.9749 | 0.9795 | 0.9717   |
| 0.001         | 10.0  | 8690 | 0.1788          | 0.9841    | 0.9758 | 0.9799 | 0.9728   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1