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--- |
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license: mit |
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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: roberta-large_ner_wnut_17 |
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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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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: 0.7345505617977528 |
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- name: Recall |
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type: recall |
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value: 0.6255980861244019 |
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- name: F1 |
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type: f1 |
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value: 0.6757105943152455 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9650416322379711 |
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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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# roberta-large_ner_wnut_17 |
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This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the wnut_17 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2288 |
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- Precision: 0.7346 |
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- Recall: 0.6256 |
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- F1: 0.6757 |
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- Accuracy: 0.9650 |
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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: cosine |
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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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| No log | 1.0 | 213 | 0.1805 | 0.6403 | 0.6089 | 0.6242 | 0.9598 | |
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| No log | 2.0 | 426 | 0.1925 | 0.7314 | 0.5993 | 0.6588 | 0.9624 | |
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| 0.1192 | 3.0 | 639 | 0.1883 | 0.7088 | 0.6172 | 0.6598 | 0.9637 | |
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| 0.1192 | 4.0 | 852 | 0.2144 | 0.7289 | 0.6400 | 0.6815 | 0.9655 | |
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| 0.0301 | 5.0 | 1065 | 0.2288 | 0.7346 | 0.6256 | 0.6757 | 0.9650 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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