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--- |
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license: mit |
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base_model: dslim/bert-base-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: my_finetuned_wnut_model_1012 |
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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: test |
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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: 0.5479274611398963 |
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- name: Recall |
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type: recall |
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value: 0.39202965708989806 |
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- name: F1 |
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type: f1 |
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value: 0.45705024311183146 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9487047961015646 |
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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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# my_finetuned_wnut_model_1012 |
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This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the wnut_17 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2940 |
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- Precision: 0.5479 |
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- Recall: 0.3920 |
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- F1: 0.4571 |
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- Accuracy: 0.9487 |
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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: 16 |
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- eval_batch_size: 16 |
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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: 2 |
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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 | 213 | 0.2657 | 0.5157 | 0.3967 | 0.4484 | 0.9468 | |
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| No log | 2.0 | 426 | 0.2940 | 0.5479 | 0.3920 | 0.4571 | 0.9487 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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