Instructions to use textattack/bert-base-uncased-SST-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-SST-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-SST-2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-SST-2") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-SST-2", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from textattack/bert-base-uncased-SST-2: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/textattack/bert-base-uncased-SST-2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://textattack/bert-base-uncased-SST-2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/textattack/bert-base-uncased-SST-2/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- df8452ffddef3186eef885b4cb936d49862db359df147d24c159c128c33ec0ef
- Size of remote file:
- 438 MB
- SHA256:
- 57ebdee44ea63b8f3a2a53011dabbd37a7bec8da5d38834beb9751075bb8b821
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