Instructions to use entropy/roberta_zinc_480m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use entropy/roberta_zinc_480m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="entropy/roberta_zinc_480m")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("entropy/roberta_zinc_480m") model = AutoModelForMaskedLM.from_pretrained("entropy/roberta_zinc_480m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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There is also a [decoder model](https://huggingface.co/entropy/roberta_zinc_decoder) trained to reconstruct
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inputs from embeddings
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---
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license: mit
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---
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There is also a [decoder model](https://huggingface.co/entropy/roberta_zinc_decoder) trained to reconstruct
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inputs from embeddings
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## Compression Head
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There is a [compression head model](https://huggingface.co/entropy/roberta_zinc_compression_head) trained to
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compress embeddings generated by this model from the native size of 768 to smaller sizes (512, 256, 128, 64, 32).
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---
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license: mit
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---
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