Instructions to use Shaopeng/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shaopeng/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Shaopeng/bert-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Shaopeng/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("Shaopeng/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress epoch 0
Browse files- README.md +4 -6
- tf_model.h5 +1 -1
README.md
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.
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- Validation Loss: 0.
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- Epoch:
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## Model description
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| Train Loss | Validation Loss | Epoch |
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| 0.0405 | 0.0577 | 1 |
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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 an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.3505
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- Validation Loss: 0.2177
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- Epoch: 0
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## Model description
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| Train Loss | Validation Loss | Epoch |
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| 0.3505 | 0.2177 | 0 |
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### Framework versions
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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