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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: ADAPMIT-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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+ # ADAPMIT-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: 0.3535
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+ - Precision-micro: 0.8999
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+ - Precision-samples: 0.8559
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+ - Precision-weighted: 0.9001
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+ - Recall-micro: 0.9173
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+ - Recall-samples: 0.8592
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+ - Recall-weighted: 0.9173
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+ - F1-micro: 0.9085
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+ - F1-samples: 0.8521
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+ - F1-weighted: 0.9085
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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: 6.03e-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: 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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+ | 0.3512 | 1.0 | 784 | 0.3253 | 0.8530 | 0.8273 | 0.8572 | 0.8883 | 0.8311 | 0.8883 | 0.8703 | 0.8238 | 0.8703 |
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+ | 0.2152 | 2.0 | 1568 | 0.2604 | 0.8999 | 0.8580 | 0.9002 | 0.9094 | 0.8521 | 0.9094 | 0.9046 | 0.8510 | 0.9046 |
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+ | 0.1348 | 3.0 | 2352 | 0.2908 | 0.9038 | 0.8626 | 0.9059 | 0.9173 | 0.8588 | 0.9173 | 0.9105 | 0.8566 | 0.9107 |
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+ | 0.0767 | 4.0 | 3136 | 0.3367 | 0.8999 | 0.8563 | 0.9000 | 0.9173 | 0.8588 | 0.9173 | 0.9085 | 0.8524 | 0.9085 |
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+ | 0.0475 | 5.0 | 3920 | 0.3535 | 0.8999 | 0.8559 | 0.9001 | 0.9173 | 0.8592 | 0.9173 | 0.9085 | 0.8521 | 0.9085 |
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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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