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bert-to-distilbert-NER

Token Classification
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  1. README.md +104 -0
  2. logs/1674367746.1589544/events.out.tfevents.1674367746.6e8f83ae5915.190.3 +3 -0
  3. logs/events.out.tfevents.1674366449.6e8f83ae5915.190.0 +2 -2
  4. logs/events.out.tfevents.1674367746.6e8f83ae5915.190.2 +3 -0
  5. pytorch_model.bin +1 -1
  6. training_args.bin +1 -1
README.md ADDED
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+ ---
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+ license: mit
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - conll2003
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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: bert-to-distilbert-NER
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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: conll2003
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+ type: conll2003
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+ config: conll2003
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+ split: train
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+ args: conll2003
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.014729299363057325
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+ - name: Recall
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+ type: recall
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+ value: 0.018680578929653316
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+ - name: F1
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+ type: f1
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+ value: 0.016471286541029827
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.7599340672278802
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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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+ # bert-to-distilbert-NER
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+
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+ This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the conll2003 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 43.2398
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+ - Precision: 0.0147
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+ - Recall: 0.0187
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+ - F1: 0.0165
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+ - Accuracy: 0.7599
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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: 6e-05
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+ - train_batch_size: 128
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+ - eval_batch_size: 128
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+ - seed: 33
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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: 15
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+ - mixed_precision_training: Native AMP
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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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+ | 190.2685 | 1.0 | 110 | 127.2351 | 0.0157 | 0.0098 | 0.0120 | 0.7569 |
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+ | 105.4389 | 2.0 | 220 | 97.1100 | 0.0281 | 0.0298 | 0.0289 | 0.7587 |
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+ | 77.0337 | 3.0 | 330 | 76.9433 | 0.0136 | 0.0173 | 0.0152 | 0.7615 |
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+ | 60.3477 | 4.0 | 440 | 65.9181 | 0.0130 | 0.0158 | 0.0143 | 0.7603 |
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+ | 50.4086 | 5.0 | 550 | 58.5255 | 0.0170 | 0.0220 | 0.0192 | 0.7603 |
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+ | 43.298 | 6.0 | 660 | 54.5405 | 0.0144 | 0.0187 | 0.0163 | 0.7594 |
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+ | 39.0911 | 7.0 | 770 | 52.4767 | 0.0155 | 0.0195 | 0.0172 | 0.7613 |
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+ | 35.07 | 8.0 | 880 | 49.1975 | 0.0170 | 0.0219 | 0.0192 | 0.7602 |
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+ | 32.215 | 9.0 | 990 | 47.4422 | 0.0144 | 0.0187 | 0.0163 | 0.7599 |
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+ | 29.9923 | 10.0 | 1100 | 46.5558 | 0.0167 | 0.0212 | 0.0187 | 0.7606 |
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+ | 28.3599 | 11.0 | 1210 | 45.6301 | 0.0171 | 0.0214 | 0.0190 | 0.7613 |
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+ | 26.8163 | 12.0 | 1320 | 45.0483 | 0.0141 | 0.0177 | 0.0157 | 0.7606 |
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+ | 25.7434 | 13.0 | 1430 | 44.0639 | 0.0176 | 0.0222 | 0.0196 | 0.7605 |
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+ | 24.9853 | 14.0 | 1540 | 43.6618 | 0.0148 | 0.0187 | 0.0165 | 0.7606 |
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+ | 24.3179 | 15.0 | 1650 | 43.2398 | 0.0147 | 0.0187 | 0.0165 | 0.7599 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.25.1
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+ - Pytorch 1.13.1+cu116
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+ - Datasets 2.8.0
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+ - Tokenizers 0.13.2
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