Text Classification
Transformers
PyTorch
Safetensors
English
bert
sentence-similarity
text-embeddings-inference
Instructions to use dennlinger/bert-wiki-paragraphs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dennlinger/bert-wiki-paragraphs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dennlinger/bert-wiki-paragraphs")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dennlinger/bert-wiki-paragraphs") model = AutoModelForSequenceClassification.from_pretrained("dennlinger/bert-wiki-paragraphs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model
This is an automated PR create with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. Feel free to ignore this PR.ae82318
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Update README.md
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Update YAML and add link to dataset.
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Adding usage example with pipelines.
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Include Lucy in acknowledgments, extend
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Initial push of bert-wiki-paragraphs.
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initial commit
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