---
license: mit
base_model: m3rg-iitd/matscibert
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: MatSciBERT_BIOMAT_NER3600
  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_BIOMAT_NER3600

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: 0.4022
- Precision: 0.9708
- Recall: 0.9629
- F1: 0.9669
- Accuracy: 0.9638

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1486        | 1.0   | 601  | 0.2452          | 0.9584    | 0.9499 | 0.9541 | 0.9494   |
| 0.0464        | 2.0   | 1202 | 0.2348          | 0.9658    | 0.9590 | 0.9624 | 0.9589   |
| 0.0265        | 3.0   | 1803 | 0.2845          | 0.9659    | 0.9599 | 0.9629 | 0.9592   |
| 0.0164        | 4.0   | 2404 | 0.3016          | 0.9689    | 0.9613 | 0.9650 | 0.9619   |
| 0.0063        | 5.0   | 3005 | 0.3531          | 0.9699    | 0.9623 | 0.9661 | 0.9631   |
| 0.0043        | 6.0   | 3606 | 0.3540          | 0.9701    | 0.9620 | 0.9660 | 0.9628   |
| 0.0033        | 7.0   | 4207 | 0.3730          | 0.9708    | 0.9630 | 0.9669 | 0.9638   |
| 0.0023        | 8.0   | 4808 | 0.3796          | 0.9710    | 0.9631 | 0.9670 | 0.9640   |
| 0.0019        | 9.0   | 5409 | 0.3892          | 0.9712    | 0.9634 | 0.9673 | 0.9642   |
| 0.0011        | 10.0  | 6010 | 0.4022          | 0.9708    | 0.9629 | 0.9669 | 0.9638   |


### Framework versions

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