--- license: apache-2.0 tags: - generated_from_trainer datasets: - wnut_17 metrics: - precision - recall - f1 - accuracy model-index: - name: token_classification_finetune 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: 0.5759878419452887 - name: Recall type: recall value: 0.35125115848007415 - name: F1 type: f1 value: 0.436384571099597 - name: Accuracy type: accuracy value: 0.9444206926036768 --- # token_classification_finetune This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.2489 - Precision: 0.5760 - Recall: 0.3513 - F1: 0.4364 - Accuracy: 0.9444 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 107 | 0.2573 | 0.6011 | 0.3003 | 0.4005 | 0.9409 | | No log | 2.0 | 214 | 0.2489 | 0.5760 | 0.3513 | 0.4364 | 0.9444 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3