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: 0.7579696797959762
- name: Recall
type: recall
value: 0.7590989712664066
- name: F1
type: f1
value: 0.7585339052142781
- name: Accuracy
type: accuracy
value: 0.8206361113872795
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: 0.6169
- Precision: 0.7580
- Recall: 0.7591
- F1: 0.7585
- Accuracy: 0.8206
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 |
|---|---|---|---|---|---|---|---|
| 0.6277 | 1.0 | 22500 | 0.6102 | 0.7441 | 0.7474 | 0.7458 | 0.8109 |
| 0.5143 | 2.0 | 45000 | 0.5998 | 0.7549 | 0.7561 | 0.7555 | 0.8183 |
| 0.4392 | 3.0 | 67500 | 0.6169 | 0.7580 | 0.7591 | 0.7585 | 0.8206 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0