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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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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: test_wnut_model |
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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: 0.5218579234972678 |
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- name: Recall |
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type: recall |
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value: 0.3540315106580167 |
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- name: F1 |
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type: f1 |
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value: 0.4218663721700718 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9427130092770724 |
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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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# test_wnut_model |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2816 |
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- Precision: 0.5219 |
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- Recall: 0.3540 |
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- F1: 0.4219 |
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- Accuracy: 0.9427 |
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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: 5e-06 |
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- train_batch_size: 6 |
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- eval_batch_size: 32 |
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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: 5 |
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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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| 0.3664 | 1.0 | 566 | 0.3082 | 0.4777 | 0.1687 | 0.2493 | 0.9354 | |
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| 0.1672 | 2.0 | 1132 | 0.2867 | 0.5395 | 0.3105 | 0.3941 | 0.9407 | |
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| 0.1265 | 3.0 | 1698 | 0.3171 | 0.5976 | 0.2753 | 0.3769 | 0.9413 | |
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| 0.116 | 4.0 | 2264 | 0.2914 | 0.5712 | 0.3420 | 0.4278 | 0.9431 | |
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| 0.0974 | 5.0 | 2830 | 0.2816 | 0.5219 | 0.3540 | 0.4219 | 0.9427 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.0 |
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- Datasets 2.14.2 |
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- Tokenizers 0.13.3 |
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