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license: apache-2.0 |
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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-base-uncased-finetuned-ner |
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results: [] |
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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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# bert-base-uncased-finetuned-ner |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0615 |
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- Precision: 0.9222 |
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- Recall: 0.9372 |
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- F1: 0.9297 |
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- Accuracy: 0.9838 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: IPU |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 16 |
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- total_eval_batch_size: 5 |
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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: 3 |
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- training precision: Mixed Precision |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.051 | 1.0 | 877 | 0.0667 | 0.9090 | 0.9190 | 0.9139 | 0.9811 | |
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| 0.2483 | 2.0 | 1754 | 0.0600 | 0.9295 | 0.9344 | 0.9320 | 0.9839 | |
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| 0.0153 | 3.0 | 2631 | 0.0615 | 0.9222 | 0.9372 | 0.9297 | 0.9838 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.10.0+cpu |
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- Datasets 2.7.1 |
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- Tokenizers 0.12.1 |
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