Instructions to use yidi-huang/bert-finetuned-ner-lieu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yidi-huang/bert-finetuned-ner-lieu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="yidi-huang/bert-finetuned-ner-lieu")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("yidi-huang/bert-finetuned-ner-lieu") model = AutoModelForTokenClassification.from_pretrained("yidi-huang/bert-finetuned-ner-lieu", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): bb4945f
Training in progress epoch 16
Browse files- README.md +3 -2
- tf_model.h5 +1 -1
README.md
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This model is a fine-tuned version of [dbmdz/bert-base-french-europeana-cased](https://huggingface.co/dbmdz/bert-base-french-europeana-cased) 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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- Epoch:
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## Model description
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### Framework versions
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This model is a fine-tuned version of [dbmdz/bert-base-french-europeana-cased](https://huggingface.co/dbmdz/bert-base-french-europeana-cased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.0023
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- Epoch: 16
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## Model description
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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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