| | --- |
| | license: apache-2.0 |
| | base_model: distilbert-base-uncased |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - wnut_17 |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: test_wnut_model |
| | 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: 0.5218579234972678 |
| | - name: Recall |
| | type: recall |
| | value: 0.3540315106580167 |
| | - name: F1 |
| | type: f1 |
| | value: 0.4218663721700718 |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.9427130092770724 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # test_wnut_model |
| |
|
| | 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: 0.2816 |
| | - Precision: 0.5219 |
| | - Recall: 0.3540 |
| | - F1: 0.4219 |
| | - Accuracy: 0.9427 |
| | |
| | ## 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-06 |
| | - train_batch_size: 6 |
| | - eval_batch_size: 32 |
| | - 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 | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | 0.3664 | 1.0 | 566 | 0.3082 | 0.4777 | 0.1687 | 0.2493 | 0.9354 | |
| | | 0.1672 | 2.0 | 1132 | 0.2867 | 0.5395 | 0.3105 | 0.3941 | 0.9407 | |
| | | 0.1265 | 3.0 | 1698 | 0.3171 | 0.5976 | 0.2753 | 0.3769 | 0.9413 | |
| | | 0.116 | 4.0 | 2264 | 0.2914 | 0.5712 | 0.3420 | 0.4278 | 0.9431 | |
| | | 0.0974 | 5.0 | 2830 | 0.2816 | 0.5219 | 0.3540 | 0.4219 | 0.9427 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.31.0 |
| | - Pytorch 2.0.0 |
| | - Datasets 2.14.2 |
| | - Tokenizers 0.13.3 |
| | |
