| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - conll2003 |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | base_model: bert-base-cased |
| | model-index: |
| | - name: dark-bert-finetuned-ner1 |
| | results: |
| | - task: |
| | type: token-classification |
| | name: Token Classification |
| | dataset: |
| | name: conll2003 |
| | type: conll2003 |
| | config: conll2003 |
| | split: train |
| | args: conll2003 |
| | metrics: |
| | - type: precision |
| | value: 0.9337419247970846 |
| | name: Precision |
| | - type: recall |
| | value: 0.9486704813194211 |
| | name: Recall |
| | - type: f1 |
| | value: 0.9411470072627097 |
| | name: F1 |
| | - type: accuracy |
| | value: 0.9861364572908695 |
| | name: Accuracy |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # dark-bert-finetuned-ner1 |
| |
|
| | This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0833 |
| | - Precision: 0.9337 |
| | - Recall: 0.9487 |
| | - F1: 0.9411 |
| | - Accuracy: 0.9861 |
| |
|
| | ## 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: 8 |
| | - eval_batch_size: 8 |
| | - 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 | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | 0.0358 | 1.0 | 1756 | 0.0780 | 0.9283 | 0.9409 | 0.9346 | 0.9844 | |
| | | 0.0172 | 2.0 | 3512 | 0.0708 | 0.9375 | 0.9488 | 0.9431 | 0.9860 | |
| | | 0.0056 | 3.0 | 5268 | 0.0833 | 0.9337 | 0.9487 | 0.9411 | 0.9861 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.22.1 |
| | - Pytorch 1.10.0 |
| | - Datasets 2.5.1 |
| | - Tokenizers 0.12.1 |
| |
|
