| | --- |
| | license: apache-2.0 |
| | base_model: distilbert-base-uncased |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - wnut_17 |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: copilot_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: 0.5803921568627451 |
| | - name: Recall |
| | type: recall |
| | value: 0.4114921223354958 |
| | - name: F1 |
| | type: f1 |
| | value: 0.48156182212581344 |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.9483562053781369 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # copilot_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: 0.3448 |
| | - Precision: 0.5804 |
| | - Recall: 0.4115 |
| | - F1: 0.4816 |
| | - Accuracy: 0.9484 |
| | |
| | ## 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: 10 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | No log | 1.0 | 213 | 0.2743 | 0.6364 | 0.2725 | 0.3816 | 0.9401 | |
| | | No log | 2.0 | 426 | 0.2598 | 0.5977 | 0.3346 | 0.4290 | 0.9445 | |
| | | 0.1759 | 3.0 | 639 | 0.3063 | 0.6741 | 0.3086 | 0.4234 | 0.9445 | |
| | | 0.1759 | 4.0 | 852 | 0.3097 | 0.5930 | 0.3605 | 0.4484 | 0.9463 | |
| | | 0.0477 | 5.0 | 1065 | 0.2962 | 0.5558 | 0.4106 | 0.4723 | 0.9474 | |
| | | 0.0477 | 6.0 | 1278 | 0.3218 | 0.5792 | 0.3967 | 0.4708 | 0.9474 | |
| | | 0.0477 | 7.0 | 1491 | 0.3199 | 0.5595 | 0.4096 | 0.4730 | 0.9477 | |
| | | 0.022 | 8.0 | 1704 | 0.3385 | 0.5938 | 0.4106 | 0.4855 | 0.9481 | |
| | | 0.022 | 9.0 | 1917 | 0.3311 | 0.5687 | 0.4217 | 0.4843 | 0.9478 | |
| | | 0.0123 | 10.0 | 2130 | 0.3448 | 0.5804 | 0.4115 | 0.4816 | 0.9484 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.34.1 |
| | - Pytorch 2.1.0+cu118 |
| | - Datasets 2.14.6 |
| | - Tokenizers 0.14.1 |
| | |
