Text Classification
Transformers
PyTorch
Safetensors
English
bert
sst2
glue
torchdistill
text-embeddings-inference
Instructions to use yoshitomo-matsubara/bert-base-uncased-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yoshitomo-matsubara/bert-base-uncased-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yoshitomo-matsubara/bert-base-uncased-sst2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yoshitomo-matsubara/bert-base-uncased-sst2") model = AutoModelForSequenceClassification.from_pretrained("yoshitomo-matsubara/bert-base-uncased-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from yoshitomo-matsubara/bert-base-uncased-sst2: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/yoshitomo-matsubara/bert-base-uncased-sst2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://yoshitomo-matsubara/bert-base-uncased-sst2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/yoshitomo-matsubara/bert-base-uncased-sst2/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 54c06155a2b31fcee414fdd5bf25a365f837a5827e4071c6a0353c1fdb3cc261
- Size of remote file:
- 438 MB
- SHA256:
- 7b62912abee9dd3577d677e442c5f45daabbbd9681ea6ece286ce6c2f3de5f27
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