---
license: mit
base_model: m3rg-iitd/matscibert
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: MatSciBERT_BIOMAT_NER2
  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_NER2

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: 0.5936
- Precision: 0.9555
- Recall: 0.9490
- F1: 0.9523
- Accuracy: 0.9426

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0537        | 1.0   | 1171  | 0.3242          | 0.9568    | 0.9456 | 0.9512 | 0.9406   |
| 0.0163        | 2.0   | 2342  | 0.3726          | 0.9578    | 0.9499 | 0.9538 | 0.9447   |
| 0.0054        | 3.0   | 3513  | 0.4411          | 0.9565    | 0.9496 | 0.9530 | 0.9432   |
| 0.0029        | 4.0   | 4684  | 0.4982          | 0.9562    | 0.9480 | 0.9521 | 0.9420   |
| 0.0017        | 5.0   | 5855  | 0.5276          | 0.9548    | 0.9472 | 0.9510 | 0.9404   |
| 0.001         | 6.0   | 7026  | 0.5463          | 0.9562    | 0.9494 | 0.9528 | 0.9429   |
| 0.0005        | 7.0   | 8197  | 0.5741          | 0.9567    | 0.9500 | 0.9533 | 0.9437   |
| 0.0004        | 8.0   | 9368  | 0.5979          | 0.9557    | 0.9494 | 0.9526 | 0.9428   |
| 0.0003        | 9.0   | 10539 | 0.6028          | 0.9558    | 0.9496 | 0.9527 | 0.9428   |
| 0.0004        | 10.0  | 11710 | 0.5936          | 0.9555    | 0.9490 | 0.9523 | 0.9426   |


### Framework versions

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