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Training complete with metrics

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README.md CHANGED
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- # BETO Base Cased finetuned on CONLL2002
 
 
 
 
 
 
 
 
 
 
 
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  ## Model description
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- This is a BETO base cased model fine-tuned on the CONLL2002 dataset for Named Entity Recognition (NER).
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- ## Metrics
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- - Precision: 0.8597014925373134
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- - Recall: 0.8602941176470589
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- - F1-score: 0.8599977029975882
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- - Accuracy: 0.978761597037205
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: cc-by-4.0
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+ base_model: NazaGara/NER-fine-tuned-BETO
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - conll2002
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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: beto-base-cased-finetuned-conll2002
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+ results:
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+ - task:
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+ name: Token Classification
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+ type: token-classification
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+ dataset:
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+ name: conll2002
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+ type: conll2002
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+ config: es
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+ split: validation
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+ args: es
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.8597014925373134
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+ - name: Recall
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+ type: recall
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+ value: 0.8602941176470589
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+ - name: F1
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+ type: f1
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+ value: 0.8599977029975882
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.978761597037205
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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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+ # beto-base-cased-finetuned-conll2002
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+
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+ This model is a fine-tuned version of [NazaGara/NER-fine-tuned-BETO](https://huggingface.co/NazaGara/NER-fine-tuned-BETO) on the conll2002 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1259
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+ - Precision: 0.8597
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+ - Recall: 0.8603
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+ - F1: 0.8600
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+ - Accuracy: 0.9788
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  ## Model description
 
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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: 2e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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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: linear
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+ - num_epochs: 3
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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 | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.0154 | 1.0 | 1041 | 0.1064 | 0.8523 | 0.8603 | 0.8563 | 0.9782 |
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+ | 0.0162 | 2.0 | 2082 | 0.1060 | 0.8556 | 0.8635 | 0.8596 | 0.9790 |
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+ | 0.0161 | 3.0 | 3123 | 0.1259 | 0.8597 | 0.8603 | 0.8600 | 0.9788 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.2
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+ - Pytorch 2.3.0+cu121
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+ - Datasets 2.19.2
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+ - Tokenizers 0.19.1
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