Instructions to use Patrick2000/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Patrick2000/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Patrick2000/bert-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Patrick2000/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("Patrick2000/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): f58a044
Training in progress epoch 2
Browse files- README.md +4 -3
- tf_model.h5 +1 -1
README.md
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This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) 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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| 0.1132 | 0.0610 | 0 |
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### Framework versions
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This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.0211
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- Validation Loss: 0.0667
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- Epoch: 2
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## Model description
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|:----------:|:---------------:|:-----:|
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| 0.1132 | 0.0610 | 0 |
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| 0.0384 | 0.0584 | 1 |
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| 0.0211 | 0.0667 | 2 |
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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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size 431173588
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