---
base_model: mHossain/bengali_pos_v1_200000
tags:
- generated_from_trainer
datasets:
- pos_tag_100k
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bengali_pos_v1_300000
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: pos_tag_100k
      type: pos_tag_100k
      config: conll2003
      split: validation
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.7824197234815842
    - name: Recall
      type: recall
      value: 0.7854805341357909
    - name: F1
      type: f1
      value: 0.783947141194579
    - name: Accuracy
      type: accuracy
      value: 0.839060352367862
---

<!-- 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. -->

# bengali_pos_v1_300000

This model is a fine-tuned version of [mHossain/bengali_pos_v1_200000](https://huggingface.co/mHossain/bengali_pos_v1_200000) on the pos_tag_100k dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6558
- Precision: 0.7824
- Recall: 0.7855
- F1: 0.7839
- Accuracy: 0.8391

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3708        | 1.0   | 22500 | 0.6313          | 0.7688    | 0.7733 | 0.7711 | 0.8304   |
| 0.3099        | 2.0   | 45000 | 0.6491          | 0.7770    | 0.7789 | 0.7780 | 0.8353   |
| 0.3127        | 3.0   | 67500 | 0.6558          | 0.7824    | 0.7855 | 0.7839 | 0.8391   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0