| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - wnut_17 |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: ner-classification |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: wnut_17 |
| type: wnut_17 |
| args: wnut_17 |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.5421686746987951 |
| - name: Recall |
| type: recall |
| value: 0.3336422613531047 |
| - name: F1 |
| type: f1 |
| value: 0.41308089500860584 |
| - name: Accuracy |
| type: accuracy |
| value: 0.9439100508742679 |
| --- |
| |
| <!-- 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. --> |
|
|
| # ner-classification |
|
|
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2699 |
| - Precision: 0.5422 |
| - Recall: 0.3336 |
| - F1: 0.4131 |
| - Accuracy: 0.9439 |
| |
| ## 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: 3 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 1.0 | 213 | 0.2797 | 0.5105 | 0.2484 | 0.3342 | 0.9386 | |
| | No log | 2.0 | 426 | 0.2636 | 0.5493 | 0.3151 | 0.4005 | 0.9430 | |
| | 0.1938 | 3.0 | 639 | 0.2699 | 0.5422 | 0.3336 | 0.4131 | 0.9439 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.20.1 |
| - Pytorch 1.12.0+cu102 |
| - Datasets 2.4.0 |
| - Tokenizers 0.12.1 |
| |
