Instructions to use s-nlp/bert-base-uncased-stsb-TTM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use s-nlp/bert-base-uncased-stsb-TTM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="s-nlp/bert-base-uncased-stsb-TTM", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("s-nlp/bert-base-uncased-stsb-TTM", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("s-nlp/bert-base-uncased-stsb-TTM", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Model Overview
It is a TT-compressed model of bert-base-uncased stsb model. Model was trained on STSB corpus with 0.87 combined score, and TTM-compressed with additional finetuning up to 58% (64 mln params) of original size with 0.843 score.
How to use
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained("s-nlp/bert-base-uncased-stsb-TTM", trust_remote_code=True)
license: other
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