|
--- |
|
license: mit |
|
base_model: urduhack/roberta-urdu-small |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- wikiann |
|
model-index: |
|
- name: UrduNER |
|
results: [] |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# UrduNER |
|
|
|
This model is a fine-tuned version of [urduhack/roberta-urdu-small](https://huggingface.co./urduhack/roberta-urdu-small) on the wikiann dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1163 |
|
- Overall Precision: 0.9540 |
|
- Overall Recall: 0.9553 |
|
- Overall F1: 0.9546 |
|
- Overall Accuracy: 0.9836 |
|
- Loc F1: 0.9643 |
|
- Org F1: 0.9448 |
|
- Per F1: 0.9491 |
|
|
|
## 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: 16 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 7 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Loc F1 | Org F1 | Per F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:------:|:------:| |
|
| 0.248 | 1.0 | 1250 | 0.0920 | 0.8906 | 0.8991 | 0.8948 | 0.9687 | 0.9086 | 0.8686 | 0.8995 | |
|
| 0.1169 | 2.0 | 2500 | 0.0761 | 0.9302 | 0.9390 | 0.9346 | 0.9791 | 0.9501 | 0.9045 | 0.9400 | |
|
| 0.07 | 3.0 | 3750 | 0.0831 | 0.9394 | 0.9451 | 0.9422 | 0.9805 | 0.9505 | 0.9348 | 0.9361 | |
|
| 0.029 | 4.0 | 5000 | 0.1102 | 0.9311 | 0.9431 | 0.9371 | 0.9784 | 0.9469 | 0.9305 | 0.9279 | |
|
| 0.0134 | 5.0 | 6250 | 0.1225 | 0.9442 | 0.9519 | 0.9480 | 0.9820 | 0.9593 | 0.9438 | 0.9337 | |
|
| 0.0107 | 6.0 | 7500 | 0.1087 | 0.9515 | 0.9566 | 0.9541 | 0.9837 | 0.9660 | 0.9423 | 0.9466 | |
|
| 0.005 | 7.0 | 8750 | 0.1163 | 0.9540 | 0.9553 | 0.9546 | 0.9836 | 0.9643 | 0.9448 | 0.9491 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.32.0 |
|
- Pytorch 2.0.1+cu118 |
|
- Datasets 2.14.4 |
|
- Tokenizers 0.13.3 |
|
|
