| --- |
| license: apache-2.0 |
| base_model: distilbert-base-uncased |
| tags: |
| - generated_from_trainer |
| datasets: |
| - indian_names |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: my_awesome_wnut_model |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: indian_names |
| type: indian_names |
| config: indian_names |
| split: train |
| args: indian_names |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.9939821779886587 |
| - name: Recall |
| type: recall |
| value: 0.9958260869565217 |
| - name: F1 |
| type: f1 |
| value: 0.9949032781188464 |
| - name: Accuracy |
| type: accuracy |
| value: 0.999003984063745 |
| --- |
| |
| <!-- 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. --> |
|
|
| # my_awesome_wnut_model |
| |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the indian_names dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0050 |
| - Precision: 0.9940 |
| - Recall: 0.9958 |
| - F1: 0.9949 |
| - Accuracy: 0.9990 |
|
|
| ## 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: 5e-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: 5 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 1.0 | 66 | 0.0440 | 0.9579 | 0.9650 | 0.9614 | 0.9906 | |
| | No log | 2.0 | 132 | 0.0191 | 0.9870 | 0.9821 | 0.9845 | 0.9959 | |
| | No log | 3.0 | 198 | 0.0098 | 0.9919 | 0.9899 | 0.9909 | 0.9980 | |
| | No log | 4.0 | 264 | 0.0061 | 0.9927 | 0.9935 | 0.9931 | 0.9987 | |
| | No log | 5.0 | 330 | 0.0050 | 0.9940 | 0.9958 | 0.9949 | 0.9990 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.33.1 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.14.5 |
| - Tokenizers 0.13.3 |
|
|
