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--- |
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license: apache-2.0 |
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base_model: climatebert/distilroberta-base-climate-f |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: SECTOR-multilabel-climatebert |
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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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# SECTOR-multilabel-climatebert |
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This model is a fine-tuned version of [climatebert/distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6028 |
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- Precision-micro: 0.6395 |
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- Precision-samples: 0.7543 |
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- Precision-weighted: 0.6475 |
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- Recall-micro: 0.7762 |
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- Recall-samples: 0.8583 |
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- Recall-weighted: 0.7762 |
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- F1-micro: 0.7012 |
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- F1-samples: 0.7655 |
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- F1-weighted: 0.7041 |
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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: 9.07e-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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- lr_scheduler_warmup_steps: 300 |
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- num_epochs: 7 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision-micro | Precision-samples | Precision-weighted | Recall-micro | Recall-samples | Recall-weighted | F1-micro | F1-samples | F1-weighted | |
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|:-------------:|:-----:|:----:|:---------------:|:---------------:|:-----------------:|:------------------:|:------------:|:--------------:|:---------------:|:--------:|:----------:|:-----------:| |
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| 0.6978 | 1.0 | 633 | 0.5968 | 0.3948 | 0.5274 | 0.4982 | 0.7873 | 0.8675 | 0.7873 | 0.5259 | 0.5996 | 0.5793 | |
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| 0.485 | 2.0 | 1266 | 0.5255 | 0.5089 | 0.6365 | 0.5469 | 0.7984 | 0.8749 | 0.7984 | 0.6216 | 0.6907 | 0.6384 | |
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| 0.3657 | 3.0 | 1899 | 0.5248 | 0.4984 | 0.6617 | 0.5397 | 0.8141 | 0.8769 | 0.8141 | 0.6183 | 0.7066 | 0.6393 | |
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| 0.2585 | 4.0 | 2532 | 0.5457 | 0.5807 | 0.7148 | 0.5992 | 0.8007 | 0.8752 | 0.8007 | 0.6732 | 0.7449 | 0.6813 | |
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| 0.1841 | 5.0 | 3165 | 0.5551 | 0.6016 | 0.7426 | 0.6192 | 0.7937 | 0.8677 | 0.7937 | 0.6844 | 0.7590 | 0.6917 | |
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| 0.1359 | 6.0 | 3798 | 0.5913 | 0.6349 | 0.7506 | 0.6449 | 0.7844 | 0.8676 | 0.7844 | 0.7018 | 0.7667 | 0.7057 | |
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| 0.1133 | 7.0 | 4431 | 0.6028 | 0.6395 | 0.7543 | 0.6475 | 0.7762 | 0.8583 | 0.7762 | 0.7012 | 0.7655 | 0.7041 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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