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--- |
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license: mit |
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base_model: roberta-base |
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datasets: climate_fever |
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tags: |
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- fact-checking |
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- climate |
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- text entailment |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: results |
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results: [] |
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widget: |
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- text: "The Great Barrier Reef is experiencing the most widespread bleaching ever recorded [SEP] A March 2016 report stated that coral bleaching was more widespread than previously thought, seriously affecting the northern parts of the reef as a result of warming ocean temperatures." |
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example_title: "Claim Verification" |
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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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# results |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on Jasontth/climate_fever_plus (https://huggingface.co/datasets/Jasontth/climate_fever_plus), enlarged CLIMATE-FEVER dataset (The dataset provided combines the Climate-Fever dataset and claim reviews from the website Climate Feedback (https://climatefeedback.org/) |
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It achieves the following results on the test set: |
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- Loss: 0.6970 |
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- Accuracy: 0.7288 |
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- F1: 0.7229 |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 16 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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