Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use henryscheible/sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/sst2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/sst2") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from henryscheible/sst2: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/henryscheible/sst2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://henryscheible/sst2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/henryscheible/sst2/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- b9689cf3c52ee794e29fab455e0edd9ba277bc2357dcb98871f3ccb2feddc0f8
- Size of remote file:
- 438 MB
- SHA256:
- aae534785f8b91365f6283d269a85148c8bf5604d2129a688e24cc6c0026f39f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.