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End of training

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README.md ADDED
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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: TAPP-multilabel-climatebert
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+ results: []
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+ ---
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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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+
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+ # TAPP-multilabel-climatebert
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+
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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: 0.6527
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+ - Precision-micro: 0.7368
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+ - Precision-samples: 0.7425
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+ - Precision-weighted: 0.7469
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+ - Recall-micro: 0.8044
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+ - Recall-samples: 0.7744
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+ - Recall-weighted: 0.8044
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+ - F1-micro: 0.7691
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+ - F1-samples: 0.7384
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+ - F1-weighted: 0.7721
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 3.06e-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: 200
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+ - num_epochs: 5
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+
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+ ### Training results
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+
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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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+ | 0.7627 | 0.8 | 500 | 0.6471 | 0.6232 | 0.6727 | 0.6384 | 0.7989 | 0.7741 | 0.7989 | 0.7002 | 0.6929 | 0.7062 |
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+ | 0.5542 | 1.59 | 1000 | 0.6114 | 0.6393 | 0.6754 | 0.6671 | 0.8154 | 0.7833 | 0.8154 | 0.7167 | 0.6999 | 0.7279 |
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+ | 0.4219 | 2.39 | 1500 | 0.6145 | 0.7196 | 0.7236 | 0.7311 | 0.7989 | 0.7645 | 0.7989 | 0.7572 | 0.7231 | 0.7613 |
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+ | 0.3268 | 3.19 | 2000 | 0.6363 | 0.7272 | 0.7383 | 0.7358 | 0.8053 | 0.7738 | 0.8053 | 0.7643 | 0.7374 | 0.7672 |
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+ | 0.2477 | 3.99 | 2500 | 0.6509 | 0.7315 | 0.7351 | 0.7439 | 0.8007 | 0.7689 | 0.8007 | 0.7646 | 0.7319 | 0.7686 |
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+ | 0.1989 | 4.78 | 3000 | 0.6527 | 0.7368 | 0.7425 | 0.7469 | 0.8044 | 0.7744 | 0.8044 | 0.7691 | 0.7384 | 0.7721 |
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+
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+
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+ ### Framework versions
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+
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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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