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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google-bert/bert-base-uncased |
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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: BERT-full-finetuned-ner-pablo |
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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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# BERT-full-finetuned-ner-pablo |
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This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1008 |
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- Precision: 0.7986 |
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- Recall: 0.7968 |
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- F1: 0.7977 |
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- Accuracy: 0.9750 |
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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: 512 |
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- eval_batch_size: 512 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 2048 |
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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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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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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 | 0.8696 | 5 | 0.0968 | 0.8048 | 0.7943 | 0.7995 | 0.9757 | |
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| No log | 1.9130 | 11 | 0.0984 | 0.8030 | 0.7966 | 0.7998 | 0.9754 | |
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| No log | 2.9565 | 17 | 0.1003 | 0.8008 | 0.7965 | 0.7987 | 0.9751 | |
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| No log | 4.0 | 23 | 0.1008 | 0.7986 | 0.7968 | 0.7977 | 0.9750 | |
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| No log | 4.3478 | 25 | 0.1008 | 0.7986 | 0.7968 | 0.7977 | 0.9750 | |
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### Framework versions |
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- Transformers 4.44.1 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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