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--- |
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license: apache-2.0 |
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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-tiny-finetuned-wnut17-ner |
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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: train |
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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: 0.0 |
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- name: Recall |
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type: recall |
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value: 0.0 |
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- name: F1 |
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type: f1 |
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value: 0.0 |
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- name: Accuracy |
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type: accuracy |
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value: 0.8960890010322284 |
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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-tiny-finetuned-wnut17-ner |
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This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the wnut_17 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6054 |
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- Precision: 0.0 |
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- Recall: 0.0 |
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- F1: 0.0 |
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- Accuracy: 0.8961 |
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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: 128 |
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- eval_batch_size: 128 |
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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: 15 |
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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 | 27 | 1.1060 | 0.0 | 0.0 | 0.0 | 0.8961 | |
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| No log | 2.0 | 54 | 0.9075 | 0.0 | 0.0 | 0.0 | 0.8961 | |
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| No log | 3.0 | 81 | 0.7978 | 0.0 | 0.0 | 0.0 | 0.8961 | |
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| No log | 4.0 | 108 | 0.7333 | 0.0 | 0.0 | 0.0 | 0.8961 | |
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| No log | 5.0 | 135 | 0.6929 | 0.0 | 0.0 | 0.0 | 0.8961 | |
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| No log | 6.0 | 162 | 0.6661 | 0.0 | 0.0 | 0.0 | 0.8961 | |
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| No log | 7.0 | 189 | 0.6477 | 0.0 | 0.0 | 0.0 | 0.8961 | |
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| No log | 8.0 | 216 | 0.6346 | 0.0 | 0.0 | 0.0 | 0.8961 | |
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| No log | 9.0 | 243 | 0.6251 | 0.0 | 0.0 | 0.0 | 0.8961 | |
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| No log | 10.0 | 270 | 0.6182 | 0.0 | 0.0 | 0.0 | 0.8961 | |
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| No log | 11.0 | 297 | 0.6132 | 0.0 | 0.0 | 0.0 | 0.8961 | |
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| No log | 12.0 | 324 | 0.6097 | 0.0 | 0.0 | 0.0 | 0.8961 | |
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| No log | 13.0 | 351 | 0.6073 | 0.0 | 0.0 | 0.0 | 0.8961 | |
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| No log | 14.0 | 378 | 0.6059 | 0.0 | 0.0 | 0.0 | 0.8961 | |
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| No log | 15.0 | 405 | 0.6054 | 0.0 | 0.0 | 0.0 | 0.8961 | |
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### Framework versions |
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- Transformers 4.21.0 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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