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---
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library_name: transformers
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license: mit
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base_model: almanach/camembert-base
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: my_awesome_wnut_model
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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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# my_awesome_wnut_model
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This model is a fine-tuned version of [almanach/camembert-base](https://huggingface.co/almanach/camembert-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0159
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- Precision: 0.0
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- Recall: 0.0
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- F1: 0.0
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- Accuracy: 0.9970
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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: 2e-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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
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| No log | 1.0 | 160 | 0.1652 | 0.0 | 0.0 | 0.0 | 0.9528 |
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| No log | 2.0 | 320 | 0.0499 | 0.0 | 0.0 | 0.0 | 0.9943 |
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| No log | 3.0 | 480 | 0.0303 | 0.0 | 0.0 | 0.0 | 0.9960 |
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| 0.1412 | 4.0 | 640 | 0.0239 | 0.0 | 0.0 | 0.0 | 0.9967 |
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| 0.1412 | 5.0 | 800 | 0.0206 | 0.0 | 0.0 | 0.0 | 0.9968 |
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| 0.1412 | 6.0 | 960 | 0.0186 | 0.0 | 0.0 | 0.0 | 0.9969 |
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| 0.0254 | 7.0 | 1120 | 0.0173 | 0.0 | 0.0 | 0.0 | 0.9970 |
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| 0.0254 | 8.0 | 1280 | 0.0165 | 0.0 | 0.0 | 0.0 | 0.9970 |
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| 0.0254 | 9.0 | 1440 | 0.0161 | 0.0 | 0.0 | 0.0 | 0.9970 |
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| 0.0184 | 10.0 | 1600 | 0.0159 | 0.0 | 0.0 | 0.0 | 0.9970 |
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### Framework versions
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- Transformers 4.46.2
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- Pytorch 2.5.1+cu118
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- Datasets 3.1.0
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- Tokenizers 0.20.3
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