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README.md
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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.
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- Precision: 0.
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- Recall: 0.
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- F1: 0.
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- Accuracy: 0.
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## Model description
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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:
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- total_eval_batch_size:
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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 Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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### Framework versions
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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: 0.0614
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- Precision: 0.9240
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- Recall: 0.9359
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- F1: 0.9299
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- Accuracy: 0.9837
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## Model description
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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 Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.0759 | 1.0 | 877 | 0.0679 | 0.9090 | 0.9181 | 0.9135 | 0.9809 |
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| 0.0577 | 2.0 | 1754 | 0.0631 | 0.9210 | 0.9316 | 0.9263 | 0.9832 |
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| 0.0176 | 3.0 | 2631 | 0.0614 | 0.9240 | 0.9359 | 0.9299 | 0.9837 |
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### Framework versions
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