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---
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library_name: transformers
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license: mit
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base_model: FacebookAI/roberta-base
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tags:
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- generated_from_trainer
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model-index:
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- name: melodic-bee-938
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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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# melodic-bee-938
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This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3387
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- Hamming Loss: 0.1123
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- Zero One Loss: 1.0
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- Jaccard Score: 1.0
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- Hamming Loss Optimised: 0.1123
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- Hamming Loss Threshold: 0.9000
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- Zero One Loss Optimised: 1.0
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- Zero One Loss Threshold: 0.9000
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- Jaccard Score Optimised: 1.0
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- Jaccard Score Threshold: 0.9000
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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: 0.0011128424281972827
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 2024
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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: 9
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Hamming Loss | Zero One Loss | Jaccard Score | Hamming Loss Optimised | Hamming Loss Threshold | Zero One Loss Optimised | Zero One Loss Threshold | Jaccard Score Optimised | Jaccard Score Threshold |
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|:-------------:|:-----:|:----:|:---------------:|:------------:|:-------------:|:-------------:|:----------------------:|:----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|
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| 0.3511 | 1.0 | 100 | 0.3435 | 0.1123 | 1.0 | 1.0 | 0.1123 | 0.9000 | 1.0 | 0.9000 | 1.0 | 0.9000 |
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| 0.3408 | 2.0 | 200 | 0.3418 | 0.1123 | 1.0 | 1.0 | 0.1123 | 0.9000 | 1.0 | 0.9000 | 1.0 | 0.9000 |
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| 0.3393 | 3.0 | 300 | 0.3440 | 0.1123 | 1.0 | 1.0 | 0.1123 | 0.9000 | 1.0 | 0.9000 | 1.0 | 0.9000 |
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| 0.3377 | 4.0 | 400 | 0.3395 | 0.1123 | 1.0 | 1.0 | 0.1123 | 0.9000 | 1.0 | 0.9000 | 1.0 | 0.9000 |
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| 0.3363 | 5.0 | 500 | 0.3408 | 0.1123 | 1.0 | 1.0 | 0.1123 | 0.9000 | 1.0 | 0.9000 | 1.0 | 0.9000 |
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| 0.3362 | 6.0 | 600 | 0.3397 | 0.1123 | 1.0 | 1.0 | 0.1123 | 0.9000 | 1.0 | 0.9000 | 1.0 | 0.9000 |
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| 0.3349 | 7.0 | 700 | 0.3420 | 0.1123 | 1.0 | 1.0 | 0.1123 | 0.9000 | 1.0 | 0.9000 | 1.0 | 0.9000 |
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| 0.334 | 8.0 | 800 | 0.3397 | 0.1123 | 1.0 | 1.0 | 0.1123 | 0.9000 | 1.0 | 0.9000 | 1.0 | 0.9000 |
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| 0.3337 | 9.0 | 900 | 0.3387 | 0.1123 | 1.0 | 1.0 | 0.1123 | 0.9000 | 1.0 | 0.9000 | 1.0 | 0.9000 |
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### Framework versions
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- Transformers 4.45.1
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- Pytorch 2.5.1+cu124
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- Datasets 3.1.0
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- Tokenizers 0.20.3
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