adriansanz
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clasificador_xml_5ep_v2
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README.md
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base_model: xlm-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: intent_analysis
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results: []
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# intent_analysis
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate:
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- train_batch_size:
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- eval_batch_size:
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- seed: 42
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- optimizer: Use 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:
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-
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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| No log | 1.0 |
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### Framework versions
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base_model: xlm-roberta-base
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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- precision
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- recall
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- f1
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model-index:
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- name: intent_analysis
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results: []
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# intent_analysis
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0750
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- Accuracy: 0.9781
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- Precision: 0.9782
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- Recall: 0.9781
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- F1: 0.9781
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## Model description
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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: 32
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- eval_batch_size: 32
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- seed: 42
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- optimizer: Use 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: cosine
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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| No log | 1.0 | 114 | 0.1457 | 0.9459 | 0.9498 | 0.9459 | 0.9456 |
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| No log | 2.0 | 228 | 0.0754 | 0.9774 | 0.9777 | 0.9774 | 0.9775 |
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| No log | 3.0 | 342 | 0.0750 | 0.9781 | 0.9782 | 0.9781 | 0.9781 |
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### Framework versions
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model.safetensors
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