Instructions to use nthngdy/headless-bert-bs64-owt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nthngdy/headless-bert-bs64-owt2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="nthngdy/headless-bert-bs64-owt2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nthngdy/headless-bert-bs64-owt2") model = AutoModelForMaskedLM.from_pretrained("nthngdy/headless-bert-bs64-owt2") - Notebooks
- Google Colab
- Kaggle
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- **Repository:**
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- **Paper:** https://arxiv.org/abs/2309.08351
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This model is a bert-base architecture trained on OpenWebText-2 using the Contrastive Weight Tying objective.
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#### Software
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[More Information Needed]
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## Citation
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**BibTeX:**
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## Model Card Authors
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Nathan Godey
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Eric de la Clergerie
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Benoît Sagot
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nathan.godey@inria.fr
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- **Repository:** https://github.com/NathanGodey/headless-lm
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- **Paper:** https://arxiv.org/abs/2309.08351
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This model is a bert-base architecture trained on OpenWebText-2 using the Contrastive Weight Tying objective.
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## Citation
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**BibTeX:**
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## Contact
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nathan.godey@inria.fr
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