---
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: 0.5316
- Precision: 0.9596
- Recall: 0.9442
- F1: 0.9518
- Accuracy: 0.9428

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1601        | 1.0   | 793  | 0.2740          | 0.9603    | 0.9459 | 0.9530 | 0.9444   |
| 0.0276        | 2.0   | 1586 | 0.3631          | 0.9599    | 0.9447 | 0.9522 | 0.9431   |
| 0.0139        | 3.0   | 2379 | 0.3745          | 0.9602    | 0.9445 | 0.9523 | 0.9430   |
| 0.0054        | 4.0   | 3172 | 0.4634          | 0.9601    | 0.9441 | 0.9521 | 0.9429   |
| 0.0039        | 5.0   | 3965 | 0.4709          | 0.9594    | 0.9440 | 0.9517 | 0.9423   |
| 0.0018        | 6.0   | 4758 | 0.5042          | 0.9587    | 0.9440 | 0.9513 | 0.9426   |
| 0.0011        | 7.0   | 5551 | 0.5223          | 0.9598    | 0.9439 | 0.9518 | 0.9425   |
| 0.0009        | 8.0   | 6344 | 0.5241          | 0.9594    | 0.9438 | 0.9515 | 0.9424   |
| 0.0004        | 9.0   | 7137 | 0.5277          | 0.9595    | 0.9441 | 0.9517 | 0.9428   |
| 0.0004        | 10.0  | 7930 | 0.5316          | 0.9596    | 0.9442 | 0.9518 | 0.9428   |


### Framework versions

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