Instructions to use binwang/roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use binwang/roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="binwang/roberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("binwang/roberta-base") model = AutoModelForMaskedLM.from_pretrained("binwang/roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from binwang/roberta-base: direct link, hf CLI and curl.
- Browser
- Download file 570 Bytes
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https://huggingface.co/binwang/roberta-base/resolve/main/README.md
- Command line
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hf download hf://binwang/roberta-base/README.md
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curl -L -o README.md https://huggingface.co/binwang/roberta-base/resolve/main/README.md
570 Bytes
metadata
tags:
- historical-research
- sbert-wk
Historical research checkpoint — SBERT-WK (2020)
No longer actively maintained. Retained for reproducibility of the original work.
This repository hosts the original 12-layer RoBERTa checkpoint used in the SBERT-WK experiments. It is a supporting checkpoint for the sentence embedding method.
For the paper, original software environment, and reproduction instructions, see SBERT-WK: A Sentence Embedding Method by Dissecting BERT-based Word Models.