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--- |
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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: twitter-roberta-base-dec2021-WNUT |
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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: 0.7111716621253406 |
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- name: Recall |
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type: recall |
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value: 0.6244019138755981 |
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- name: F1 |
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type: f1 |
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value: 0.664968152866242 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9642789042140724 |
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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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# twitter-roberta-base-dec2021-WNUT |
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This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the wnut_17 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2152 |
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- Precision: 0.7112 |
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- Recall: 0.6244 |
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- F1: 0.6650 |
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- Accuracy: 0.9643 |
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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-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 1024 |
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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: 10 |
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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 | 0.46 | 25 | 0.2818 | 0.0982 | 0.0383 | 0.0551 | 0.9241 | |
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| No log | 0.93 | 50 | 0.2158 | 0.6181 | 0.4569 | 0.5254 | 0.9480 | |
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| No log | 1.39 | 75 | 0.1930 | 0.6682 | 0.5347 | 0.5940 | 0.9555 | |
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| No log | 1.85 | 100 | 0.1728 | 0.6583 | 0.5646 | 0.6079 | 0.9594 | |
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| No log | 2.31 | 125 | 0.1787 | 0.7050 | 0.5718 | 0.6314 | 0.9619 | |
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| No log | 2.78 | 150 | 0.2051 | 0.6979 | 0.5251 | 0.5993 | 0.9587 | |
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| No log | 3.24 | 175 | 0.1755 | 0.7172 | 0.5945 | 0.6501 | 0.9621 | |
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| No log | 3.7 | 200 | 0.1720 | 0.6943 | 0.6304 | 0.6608 | 0.9645 | |
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| No log | 4.17 | 225 | 0.1873 | 0.7203 | 0.6316 | 0.6730 | 0.9646 | |
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| No log | 4.63 | 250 | 0.1781 | 0.6934 | 0.6196 | 0.6545 | 0.9638 | |
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| No log | 5.09 | 275 | 0.1953 | 0.7040 | 0.6172 | 0.6577 | 0.9631 | |
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| No log | 5.56 | 300 | 0.1953 | 0.7223 | 0.6316 | 0.6739 | 0.9642 | |
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| No log | 6.02 | 325 | 0.1839 | 0.7008 | 0.6471 | 0.6729 | 0.9648 | |
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| No log | 6.48 | 350 | 0.1995 | 0.716 | 0.6423 | 0.6772 | 0.9650 | |
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| No log | 6.94 | 375 | 0.2056 | 0.7251 | 0.6184 | 0.6675 | 0.9640 | |
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| No log | 7.41 | 400 | 0.2044 | 0.7065 | 0.6220 | 0.6616 | 0.9640 | |
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| No log | 7.87 | 425 | 0.2042 | 0.7201 | 0.6400 | 0.6776 | 0.9650 | |
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| No log | 8.33 | 450 | 0.2247 | 0.7280 | 0.6244 | 0.6722 | 0.9638 | |
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| No log | 8.8 | 475 | 0.2060 | 0.7064 | 0.6447 | 0.6742 | 0.9649 | |
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| 0.0675 | 9.26 | 500 | 0.2152 | 0.7112 | 0.6244 | 0.6650 | 0.9643 | |
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| 0.0675 | 9.72 | 525 | 0.2086 | 0.7070 | 0.6495 | 0.6771 | 0.9650 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.12.0 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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