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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: Babelscape/wikineural-multilingual-ner |
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tags: |
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- generated_from_trainer |
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datasets: |
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- wnut_17 |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: bert-finetuned-ner-Colab |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: wnut_17 |
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type: wnut_17 |
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config: wnut_17 |
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split: validation |
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args: wnut_17 |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.6477093206951027 |
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- name: Recall |
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type: recall |
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value: 0.4904306220095694 |
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- name: F1 |
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type: f1 |
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value: 0.5582028590878148 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9344202521095948 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-finetuned-ner-Colab |
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This model is a fine-tuned version of [Babelscape/wikineural-multilingual-ner](https://huggingface.co/Babelscape/wikineural-multilingual-ner) on the wnut_17 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4102 |
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- Precision: 0.6477 |
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- Recall: 0.4904 |
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- F1: 0.5582 |
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- Accuracy: 0.9344 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 425 | 0.3037 | 0.5963 | 0.5072 | 0.5482 | 0.9321 | |
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| 0.0672 | 2.0 | 850 | 0.3751 | 0.6604 | 0.4653 | 0.5460 | 0.9316 | |
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| 0.0451 | 3.0 | 1275 | 0.4102 | 0.6477 | 0.4904 | 0.5582 | 0.9344 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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