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ner-bert-large-cased-pt-contratos_tceal

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contratos_tceal

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  1. README.md +108 -0
  2. config.json +234 -0
  3. model.safetensors +3 -0
  4. runs/Dec15_21-35-15_7ccf83c50d65/events.out.tfevents.1702676116.7ccf83c50d65.1601.0 +3 -0
  5. runs/Dec15_21-35-15_7ccf83c50d65/events.out.tfevents.1702677760.7ccf83c50d65.1601.1 +3 -0
  6. special_tokens_map.json +37 -0
  7. tokenizer.json +0 -0
  8. tokenizer_config.json +61 -0
  9. training_args.bin +3 -0
  10. vocab.txt +0 -0
README.md ADDED
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+ ---
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+ base_model: pierreguillou/ner-bert-large-cased-pt-lenerbr
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - contratos_tceal
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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: ner-bert-large-cased-pt-contratos_tceal
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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: contratos_tceal
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+ type: contratos_tceal
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+ config: contratos_tceal
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+ split: validation
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+ args: contratos_tceal
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.863676600767188
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+ - name: Recall
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+ type: recall
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+ value: 0.8834892846362813
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+ - name: F1
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+ type: f1
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+ value: 0.8734706057893166
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9145210809496307
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+ ---
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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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+ # ner-bert-large-cased-pt-contratos_tceal
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+
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+ This model is a fine-tuned version of [pierreguillou/ner-bert-large-cased-pt-lenerbr](https://huggingface.co/pierreguillou/ner-bert-large-cased-pt-lenerbr) on the contratos_tceal dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: nan
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+ - Precision: 0.8637
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+ - Recall: 0.8835
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+ - F1: 0.8735
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+ - Accuracy: 0.9145
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+
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+ ## Model description
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+
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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: 4
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+ - eval_batch_size: 4
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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: 20
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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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+ | No log | 1.0 | 191 | nan | 0.3715 | 0.3350 | 0.3523 | 0.5279 |
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+ | No log | 2.0 | 382 | nan | 0.4724 | 0.5889 | 0.5243 | 0.6579 |
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+ | 2.0083 | 3.0 | 573 | nan | 0.6952 | 0.7483 | 0.7207 | 0.8521 |
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+ | 2.0083 | 4.0 | 764 | nan | 0.7303 | 0.7911 | 0.7595 | 0.8775 |
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+ | 2.0083 | 5.0 | 955 | nan | 0.7914 | 0.8005 | 0.7959 | 0.8917 |
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+ | 0.4952 | 6.0 | 1146 | nan | 0.8545 | 0.8702 | 0.8623 | 0.9099 |
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+ | 0.4952 | 7.0 | 1337 | nan | 0.8507 | 0.8775 | 0.8639 | 0.9086 |
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+ | 0.2482 | 8.0 | 1528 | nan | 0.8530 | 0.8708 | 0.8618 | 0.9085 |
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+ | 0.2482 | 9.0 | 1719 | nan | 0.8546 | 0.8744 | 0.8644 | 0.9108 |
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+ | 0.2482 | 10.0 | 1910 | nan | 0.8563 | 0.8720 | 0.8641 | 0.9105 |
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+ | 0.169 | 11.0 | 2101 | nan | 0.8632 | 0.8741 | 0.8686 | 0.9092 |
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+ | 0.169 | 12.0 | 2292 | nan | 0.8640 | 0.8805 | 0.8722 | 0.9089 |
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+ | 0.169 | 13.0 | 2483 | nan | 0.8598 | 0.8756 | 0.8677 | 0.9096 |
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+ | 0.1255 | 14.0 | 2674 | nan | 0.8622 | 0.8799 | 0.8709 | 0.9121 |
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+ | 0.1255 | 15.0 | 2865 | nan | 0.8603 | 0.8814 | 0.8707 | 0.9113 |
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+ | 0.0942 | 16.0 | 3056 | nan | 0.8612 | 0.8787 | 0.8699 | 0.9114 |
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+ | 0.0942 | 17.0 | 3247 | nan | 0.8626 | 0.8793 | 0.8709 | 0.9133 |
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+ | 0.0942 | 18.0 | 3438 | nan | 0.8640 | 0.8823 | 0.8731 | 0.9132 |
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+ | 0.0795 | 19.0 | 3629 | nan | 0.8608 | 0.8808 | 0.8707 | 0.9139 |
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+ | 0.0795 | 20.0 | 3820 | nan | 0.8637 | 0.8835 | 0.8735 | 0.9145 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.36.1
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.15.0
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+ - Tokenizers 0.15.0
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