| --- |
| license: apache-2.0 |
| base_model: distilbert-base-uncased |
| tags: |
| - generated_from_trainer |
| datasets: |
| - ner |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: my_awesome_wnut_model |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: ner |
| type: ner |
| config: indian_names |
| split: train |
| args: indian_names |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.9999768614928964 |
| - name: Recall |
| type: recall |
| value: 0.9999305876908838 |
| - name: F1 |
| type: f1 |
| value: 0.9999537240565493 |
| - name: Accuracy |
| type: accuracy |
| value: 0.9999695484028137 |
| --- |
| |
| <!-- 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 ner dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0001 |
| - Precision: 1.0000 |
| - Recall: 0.9999 |
| - F1: 1.0000 |
| - Accuracy: 1.0000 |
| |
| ## 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.0405 | 1.0 | 688 | 0.0024 | 0.9962 | 0.9969 | 0.9965 | 0.9980 | |
| | 0.0078 | 2.0 | 1376 | 0.0017 | 0.9972 | 0.9989 | 0.9981 | 0.9990 | |
| | 0.0024 | 3.0 | 2064 | 0.0004 | 0.9995 | 0.9998 | 0.9997 | 0.9998 | |
| | 0.0008 | 4.0 | 2752 | 0.0002 | 0.9999 | 0.9999 | 0.9999 | 0.9999 | |
| | 0.001 | 5.0 | 3440 | 0.0001 | 1.0000 | 0.9999 | 1.0000 | 1.0000 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.33.2 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.14.5 |
| - Tokenizers 0.13.3 |
| |
