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language: |
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- es |
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license: apache-2.0 |
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datasets: |
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- eriktks/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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pipeline_tag: token-classification |
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--- |
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# Model Name: bert-finetuned-ner-1 |
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This is a BERT model fine-tuned for Named Entity Recognition (NER). |
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# Model Description |
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This is a fine-tuned BERT model for Named Entity Recognition (NER) task using CONLL2002 dataset. |
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In the first part, the dataset must be pre-processed in order to give it to the model. This is done using the 🤗 Transformers and BERT tokenizers. Once this is done, finetuning is applied from *[bert-base-cased](https://huggingface.co/google-bert/bert-base-cased)* and using the 🤗 *AutoModelForTokenClassification*. |
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Finally, the model is trained obtaining the neccesary metrics for evaluating its performance (Precision, Recall, F1 and Accuracy) |
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# Training |
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## Training Details |
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- Epochs: 5 |
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- Learning Rate: 2e-05 |
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- Weight Decay: 0.01 |
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- Batch Size (Train): 16 |
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- Batch Size (Eval): 8 |
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## Training Metrics |
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| Epoch | Training Loss | Validation Loss | Precision | Recall | F1 Score | Accuracy | |
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|:----:|:-------------:|:---------------:|:---------:|:------:|:--------:|:--------:| |
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| 1 | 0.1735| 0.1508 | 0.6577 | 0.7323 | 0.6930 | 0.9586 | |
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| 2 | 0.0770| 0.1421 | 0.6876 | 0.7702 | 0.7266 | 0.9629 | |
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| 3 | 0.0504| 0.1373 | 0.7353 | 0.7845 | 0.7591 | 0.9663 | |
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| 4 | 0.0358| 0.1442 | 0.7453 | 0.7902 | 0.7671 | 0.9664 | |
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| 5 | 0.0272| 0.1536 | 0.7527 | 0.7946 | 0.7731 | 0.9667 | |
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# Authors |
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Made by: |
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- Paul Rodrigo Rojas Guerrero |
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- Jose Luis Hincapie Bucheli |
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- Sebastián Idrobo Avirama |
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With help from: |
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- [Raúl Ernesto Gutiérrez](https://huggingface.co/raulgdp) |
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