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pardrib1998
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ner_bert_model

Token Classification
Transformers
Safetensors
distilbert
Generated from Trainer
Eval Results (legacy)
Model card Files
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README.md ADDED
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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: distilbert-base-uncased
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - lener_br
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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_model
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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: lener_br
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+ type: lener_br
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+ config: lener_br
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+ split: test
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+ args: lener_br
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.828094932649134
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+ - name: Recall
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+ type: recall
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+ value: 0.8532716457369465
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+ - name: F1
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+ type: f1
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+ value: 0.8404947916666666
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9840912469998513
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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_model
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+
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+ This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the lener_br dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0922
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+ - Precision: 0.8281
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+ - Recall: 0.8533
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+ - F1: 0.8405
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+ - Accuracy: 0.9841
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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: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - optimizer: Use OptimizerNames.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: 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 | 490 | 0.0971 | 0.6373 | 0.7607 | 0.6936 | 0.9706 |
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+ | 0.2449 | 2.0 | 980 | 0.0820 | 0.6916 | 0.8063 | 0.7446 | 0.9760 |
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+ | 0.0634 | 3.0 | 1470 | 0.0750 | 0.7106 | 0.8473 | 0.7730 | 0.9778 |
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+ | 0.0352 | 4.0 | 1960 | 0.0707 | 0.7690 | 0.8361 | 0.8011 | 0.9799 |
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+ | 0.0226 | 5.0 | 2450 | 0.0812 | 0.8063 | 0.8394 | 0.8225 | 0.9821 |
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+ | 0.0157 | 6.0 | 2940 | 0.0779 | 0.7931 | 0.8486 | 0.8199 | 0.9826 |
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+ | 0.0105 | 7.0 | 3430 | 0.0958 | 0.7314 | 0.8586 | 0.7899 | 0.9779 |
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+ | 0.0082 | 8.0 | 3920 | 0.0810 | 0.8158 | 0.8460 | 0.8306 | 0.9829 |
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+ | 0.0067 | 9.0 | 4410 | 0.0830 | 0.8190 | 0.8526 | 0.8355 | 0.9832 |
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+ | 0.0054 | 10.0 | 4900 | 0.0810 | 0.8165 | 0.8500 | 0.8329 | 0.9833 |
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+ | 0.0051 | 11.0 | 5390 | 0.0855 | 0.8180 | 0.8493 | 0.8333 | 0.9832 |
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+ | 0.0037 | 12.0 | 5880 | 0.0862 | 0.8195 | 0.8519 | 0.8354 | 0.9841 |
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+ | 0.0034 | 13.0 | 6370 | 0.0867 | 0.8165 | 0.8586 | 0.8370 | 0.9833 |
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+ | 0.0027 | 14.0 | 6860 | 0.0922 | 0.8214 | 0.8420 | 0.8316 | 0.9832 |
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+ | 0.0024 | 15.0 | 7350 | 0.0910 | 0.8147 | 0.8486 | 0.8313 | 0.9836 |
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+ | 0.002 | 16.0 | 7840 | 0.0928 | 0.8191 | 0.8559 | 0.8371 | 0.9840 |
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+ | 0.0018 | 17.0 | 8330 | 0.0928 | 0.8119 | 0.8559 | 0.8333 | 0.9834 |
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+ | 0.0017 | 18.0 | 8820 | 0.0920 | 0.8228 | 0.8592 | 0.8406 | 0.9838 |
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+ | 0.0015 | 19.0 | 9310 | 0.0919 | 0.8242 | 0.8553 | 0.8394 | 0.9837 |
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+ | 0.0011 | 20.0 | 9800 | 0.0922 | 0.8281 | 0.8533 | 0.8405 | 0.9841 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.52.4
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+ - Pytorch 2.6.0+cu124
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+ - Datasets 3.6.0
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+ - Tokenizers 0.21.1
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