2.17 kB
| --- | |
| license: gpl-3.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - mim_gold_ner | |
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| - accuracy | |
| model-index: | |
| - name: IceBERT-finetuned-ner | |
| results: | |
| - task: | |
| name: Token Classification | |
| type: token-classification | |
| dataset: | |
| name: mim_gold_ner | |
| type: mim_gold_ner | |
| args: mim-gold-ner | |
| metrics: | |
| - name: Precision | |
| type: precision | |
| value: 0.8927335640138409 | |
| - name: Recall | |
| type: recall | |
| value: 0.8631855657784682 | |
| - name: F1 | |
| type: f1 | |
| value: 0.8777109531620194 | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.9849836396073506 | |
| --- | |
| <!-- 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. --> | |
| # IceBERT-finetuned-ner | |
| This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the mim_gold_ner dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0807 | |
| - Precision: 0.8927 | |
| - Recall: 0.8632 | |
| - F1: 0.8777 | |
| - Accuracy: 0.9850 | |
| ## 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: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | |
| | 0.0544 | 1.0 | 2904 | 0.0774 | 0.8859 | 0.8490 | 0.8670 | 0.9833 | | |
| | 0.0284 | 2.0 | 5808 | 0.0781 | 0.8709 | 0.8590 | 0.8649 | 0.9840 | | |
| | 0.0166 | 3.0 | 8712 | 0.0807 | 0.8927 | 0.8632 | 0.8777 | 0.9850 | | |
| ### Framework versions | |
| - Transformers 4.11.2 | |
| - Pytorch 1.9.0+cu102 | |
| - Datasets 1.12.1 | |
| - Tokenizers 0.10.3 | |