--- 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 --- # 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