Instructions to use tzhao3/Bert-SST2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tzhao3/Bert-SST2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tzhao3/Bert-SST2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tzhao3/Bert-SST2") model = AutoModelForSequenceClassification.from_pretrained("tzhao3/Bert-SST2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from tzhao3/Bert-SST2: direct link, hf CLI and curl.
- Browser
- Download file 876 MB
-
https://huggingface.co/tzhao3/Bert-SST2/resolve/main/optimizer.pt
- Command line
-
hf download hf://tzhao3/Bert-SST2/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/tzhao3/Bert-SST2/resolve/main/optimizer.pt
876 MB
- Xet hash:
- 33ed77de26eca5733edcd32d12e62aabb6fb56440466fc42af89f914e2160e4b
- Size of remote file:
- 876 MB
- SHA256:
- 8746c9543cfef9df5c8294a65b4ab12a92da1552a49990e3c58183577ac4126e
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