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bengali_pos_v1_200000

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bengali_pos_v1_200000
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---
base_model: mHossain/bengali_pos_v1
tags:
- generated_from_trainer
datasets:
- pos_tag_100k
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bengali_pos_v1_200000
  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: 0.7727710210867093
    - name: Recall
      type: recall
      value: 0.7751739702839947
    - name: F1
      type: f1
      value: 0.7739706305787504
    - name: Accuracy
      type: accuracy
      value: 0.8325531771739444
---

<!-- 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_200000

This model is a fine-tuned version of [mHossain/bengali_pos_v1](https://huggingface.co/mHossain/bengali_pos_v1) on the pos_tag_100k dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5802
- Precision: 0.7728
- Recall: 0.7752
- F1: 0.7740
- Accuracy: 0.8326

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6183        | 1.0   | 22500 | 0.6032          | 0.7546    | 0.7570 | 0.7558 | 0.8193   |
| 0.5138        | 2.0   | 45000 | 0.5763          | 0.7691    | 0.7694 | 0.7692 | 0.8292   |
| 0.4448        | 3.0   | 67500 | 0.5802          | 0.7728    | 0.7752 | 0.7740 | 0.8326   |


### Framework versions

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