Instructions to use textattack/bert-base-uncased-STS-B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-STS-B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-STS-B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-STS-B") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-STS-B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from textattack/bert-base-uncased-STS-B: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/textattack/bert-base-uncased-STS-B/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://textattack/bert-base-uncased-STS-B/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/textattack/bert-base-uncased-STS-B/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- cecfeadee61daf765d4ddd410ddd0ea2da8ac7c2d9001fb3a2a89063549e8f7d
- Size of remote file:
- 438 MB
- SHA256:
- 33697e4730c87502071c8570a5e1643284c555557d14a4a0161277ba7cbc0a3c
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