Instructions to use hamishivi/fixed-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hamishivi/fixed-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hamishivi/fixed-roberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hamishivi/fixed-roberta-base") model = AutoModel.from_pretrained("hamishivi/fixed-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from hamishivi/fixed-roberta-base: direct link, hf CLI and curl.
- Browser
- Download file 195 Bytes
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https://huggingface.co/hamishivi/fixed-roberta-base/resolve/main/README.md
- Command line
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hf download hf://hamishivi/fixed-roberta-base/README.md
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curl -L -o README.md https://huggingface.co/hamishivi/fixed-roberta-base/resolve/main/README.md
195 Bytes
Fixed-roberta-base
roberta-base but with a resized embedding matrix and an extra dim in the token type embedding matrix for better sharding/partitioning.