| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - wnut_17 |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: ner |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: wnut_17 |
| type: wnut_17 |
| config: wnut_17 |
| split: test |
| args: wnut_17 |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.5552523874488404 |
| - name: Recall |
| type: recall |
| value: 0.37720111214087115 |
| - name: F1 |
| type: f1 |
| value: 0.44922737306843263 |
| - name: Accuracy |
| type: accuracy |
| value: 0.9469454063528707 |
| --- |
| |
| <!-- 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 |
|
|
| 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: 0.2942 |
| - Precision: 0.5553 |
| - Recall: 0.3772 |
| - F1: 0.4492 |
| - Accuracy: 0.9469 |
| |
| ## 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: 5 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 1.0 | 213 | 0.2666 | 0.6024 | 0.2808 | 0.3831 | 0.9405 | |
| | No log | 2.0 | 426 | 0.2605 | 0.5708 | 0.3364 | 0.4233 | 0.9456 | |
| | 0.1299 | 3.0 | 639 | 0.2827 | 0.5658 | 0.3346 | 0.4205 | 0.9452 | |
| | 0.1299 | 4.0 | 852 | 0.2836 | 0.5503 | 0.3753 | 0.4463 | 0.9469 | |
| | 0.051 | 5.0 | 1065 | 0.2942 | 0.5553 | 0.3772 | 0.4492 | 0.9469 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.27.4 |
| - Pytorch 2.0.0+cu118 |
| - Datasets 2.11.0 |
| - Tokenizers 0.13.3 |
| |
