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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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- jxner |
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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: medicine-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: jxner |
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type: jxner |
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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: 0.0 |
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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: 0.9 |
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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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# medicine-ner |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the jxner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5562 |
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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: 0.9 |
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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: 20 |
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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 | 1 | 1.7398 | 0.0370 | 0.125 | 0.0571 | 0.65 | |
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| No log | 2.0 | 2 | 1.5750 | 0.0 | 0.0 | 0.0 | 0.86 | |
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| No log | 3.0 | 3 | 1.4146 | 0.0 | 0.0 | 0.0 | 0.88 | |
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| No log | 4.0 | 4 | 1.2611 | 0.0 | 0.0 | 0.0 | 0.9 | |
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| No log | 5.0 | 5 | 1.1173 | 0.0 | 0.0 | 0.0 | 0.9 | |
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| No log | 6.0 | 6 | 0.9869 | 0.0 | 0.0 | 0.0 | 0.9 | |
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| No log | 7.0 | 7 | 0.8737 | 0.0 | 0.0 | 0.0 | 0.9 | |
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| No log | 8.0 | 8 | 0.7804 | 0.0 | 0.0 | 0.0 | 0.9 | |
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| No log | 9.0 | 9 | 0.7074 | 0.0 | 0.0 | 0.0 | 0.9 | |
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| No log | 10.0 | 10 | 0.6545 | 0.0 | 0.0 | 0.0 | 0.9 | |
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| No log | 11.0 | 11 | 0.6181 | 0.0 | 0.0 | 0.0 | 0.9 | |
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| No log | 12.0 | 12 | 0.5938 | 0.0 | 0.0 | 0.0 | 0.9 | |
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| No log | 13.0 | 13 | 0.5780 | 0.0 | 0.0 | 0.0 | 0.9 | |
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| No log | 14.0 | 14 | 0.5682 | 0.0 | 0.0 | 0.0 | 0.9 | |
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| No log | 15.0 | 15 | 0.5623 | 0.0 | 0.0 | 0.0 | 0.9 | |
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| No log | 16.0 | 16 | 0.5589 | 0.0 | 0.0 | 0.0 | 0.9 | |
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| No log | 17.0 | 17 | 0.5571 | 0.0 | 0.0 | 0.0 | 0.9 | |
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| No log | 18.0 | 18 | 0.5563 | 0.0 | 0.0 | 0.0 | 0.9 | |
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| No log | 19.0 | 19 | 0.5562 | 0.0 | 0.0 | 0.0 | 0.9 | |
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| No log | 20.0 | 20 | 0.5562 | 0.0 | 0.0 | 0.0 | 0.9 | |
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### Framework versions |
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- Transformers 4.27.3 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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