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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: stance_class_l |
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results: [] |
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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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# stance_class_l |
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This model is a fine-tuned version of vinai/bertweet-base on the dataset of 804 labeled tweets on the cancer risk controversy of Roundup Weedkiller \. |
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It classified the stance of an individual's tweet toward Bayer, Monsanto, or other relevant organizations in the crisis. |
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Two stances are classified: (0) Aggressive, (1) Non-Aggressive (neutral and accommodative). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6084 |
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- Accuracy: 0.8447 |
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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: 2.924e-05 |
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- train_batch_size: 30 |
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- eval_batch_size: 30 |
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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 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.3566 | 1.0 | 17 | 0.4855 | 0.7578 | |
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| 0.2532 | 2.0 | 34 | 0.3632 | 0.8509 | |
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| 0.2351 | 3.0 | 51 | 0.3773 | 0.8509 | |
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| 0.043 | 4.0 | 68 | 0.3553 | 0.8571 | |
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| 0.08 | 5.0 | 85 | 0.4682 | 0.8447 | |
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| 0.3089 | 6.0 | 102 | 0.4686 | 0.8509 | |
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| 0.035 | 7.0 | 119 | 0.5876 | 0.8323 | |
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| 0.0188 | 8.0 | 136 | 0.5469 | 0.8571 | |
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| 0.021 | 9.0 | 153 | 0.5022 | 0.8447 | |
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| 0.0533 | 10.0 | 170 | 0.5240 | 0.8385 | |
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| 0.0175 | 11.0 | 187 | 0.6352 | 0.8447 | |
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| 0.0106 | 12.0 | 204 | 0.5856 | 0.8447 | |
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| 1.9534 | 13.0 | 221 | 0.5938 | 0.8509 | |
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| 0.0143 | 14.0 | 238 | 0.6074 | 0.8447 | |
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| 0.0079 | 15.0 | 255 | 0.6084 | 0.8447 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.2 |
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