Instructions to use binwang/bert-base-nli-stsb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use binwang/bert-base-nli-stsb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="binwang/bert-base-nli-stsb")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("binwang/bert-base-nli-stsb") model = AutoModelForMaskedLM.from_pretrained("binwang/bert-base-nli-stsb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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No longer actively maintained.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 12-layer BERT checkpoint fine-tuned on NLI and STS-B 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.