metadata
base_model: mHossain/bengali_pos_v1_300000
tags:
- generated_from_trainer
datasets:
- pos_tag_100k
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bengali_pos_v1_400000
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: 0.7830405270513077
- name: Recall
type: recall
value: 0.7856186076789224
- name: F1
type: f1
value: 0.7843274488361194
- name: Accuracy
type: accuracy
value: 0.8402036064122549
bengali_pos_v1_400000
This model is a fine-tuned version of mHossain/bengali_pos_v1_300000 on the pos_tag_100k dataset. It achieves the following results on the evaluation set:
- Loss: 0.5609
- Precision: 0.7830
- Recall: 0.7856
- F1: 0.7843
- Accuracy: 0.8402
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 |
|---|---|---|---|---|---|---|---|
| 0.5908 | 1.0 | 22500 | 0.5513 | 0.7688 | 0.7698 | 0.7693 | 0.8289 |
| 0.4642 | 2.0 | 45000 | 0.5415 | 0.7799 | 0.7822 | 0.7810 | 0.8382 |
| 0.3773 | 3.0 | 67500 | 0.5609 | 0.7830 | 0.7856 | 0.7843 | 0.8402 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0