metadata
base_model: mHossain/bengali_pos_v1_400000
tags:
- generated_from_trainer
datasets:
- pos_tag_100k
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bengali_pos_v1_500000
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: 0.7517491791092168
- name: Recall
type: recall
value: 0.7532968740056972
- name: F1
type: f1
value: 0.752522230781312
- name: Accuracy
type: accuracy
value: 0.8148741068930406
bengali_pos_v1_500000
This model is a fine-tuned version of mHossain/bengali_pos_v1_400000 on the pos_tag_100k dataset. It achieves the following results on the evaluation set:
- Loss: 0.6185
- Precision: 0.7517
- Recall: 0.7533
- F1: 0.7525
- Accuracy: 0.8149
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 |
|---|---|---|---|---|---|---|---|
| 0.6508 | 1.0 | 67500 | 0.6325 | 0.7386 | 0.7396 | 0.7391 | 0.8041 |
| 0.5713 | 2.0 | 135000 | 0.6093 | 0.7487 | 0.7500 | 0.7493 | 0.8123 |
| 0.4789 | 3.0 | 202500 | 0.6185 | 0.7517 | 0.7533 | 0.7525 | 0.8149 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0