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NER_harem_bert-base-portuguese-cased

Token Classification
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Safetensors
bert
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NER_harem_bert-base-portuguese-cased / README.md
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metadata
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
  - generated_from_trainer
datasets:
  - harem
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: harem-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: harem
          type: harem
          config: default
          split: test
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.6869415807560137
          - name: Recall
            type: recall
            value: 0.7467314157639149
          - name: F1
            type: f1
            value: 0.7155897619473779
          - name: Accuracy
            type: accuracy
            value: 0.9527588964414234

harem-ner

This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the harem dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2411
  • Precision: 0.6869
  • Recall: 0.7467
  • F1: 0.7156
  • Accuracy: 0.9528

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 3e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 300

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 16 0.7683 0.0 0.0 0.0 0.8358
No log 2.0 32 0.4727 0.3375 0.2955 0.3151 0.8803
No log 3.0 48 0.3498 0.4859 0.4838 0.4848 0.9090
No log 4.0 64 0.2771 0.5651 0.6223 0.5924 0.9354
No log 5.0 80 0.2309 0.5901 0.6743 0.6294 0.9424
No log 6.0 96 0.2195 0.6229 0.6997 0.6590 0.9469
No log 7.0 112 0.2151 0.6239 0.6903 0.6554 0.9480
No log 8.0 128 0.2178 0.6682 0.7236 0.6948 0.9504
No log 9.0 144 0.2210 0.6808 0.7426 0.7104 0.9514
No log 10.0 160 0.2292 0.6863 0.7348 0.7097 0.9512
No log 11.0 176 0.2312 0.6932 0.7452 0.7183 0.9522
No log 12.0 192 0.2258 0.6966 0.7523 0.7234 0.9535
No log 13.0 208 0.2337 0.7076 0.7557 0.7309 0.9537
No log 14.0 224 0.2299 0.6907 0.7549 0.7214 0.9533
No log 15.0 240 0.2381 0.6980 0.7553 0.7255 0.9524
No log 16.0 256 0.2411 0.6869 0.7467 0.7156 0.9528

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2