Text Classification
Transformers
PyTorch
Safetensors
English
bert
wnli
glue
kd
torchdistill
text-embeddings-inference
Instructions to use yoshitomo-matsubara/bert-base-uncased-wnli_from_bert-large-uncased-wnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yoshitomo-matsubara/bert-base-uncased-wnli_from_bert-large-uncased-wnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yoshitomo-matsubara/bert-base-uncased-wnli_from_bert-large-uncased-wnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yoshitomo-matsubara/bert-base-uncased-wnli_from_bert-large-uncased-wnli") model = AutoModelForSequenceClassification.from_pretrained("yoshitomo-matsubara/bert-base-uncased-wnli_from_bert-large-uncased-wnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from yoshitomo-matsubara/bert-base-uncased-wnli_from_bert-large-uncased-wnli: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/yoshitomo-matsubara/bert-base-uncased-wnli_from_bert-large-uncased-wnli/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://yoshitomo-matsubara/bert-base-uncased-wnli_from_bert-large-uncased-wnli/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/yoshitomo-matsubara/bert-base-uncased-wnli_from_bert-large-uncased-wnli/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 528aabdc0c210639c23afcdd872b0de4df4e288920ecd75ec4fe6d9e32fed047
- Size of remote file:
- 438 MB
- SHA256:
- 3e3c9ed5f855deb4db085415f840726e1c8f3fb4586ecbe66519f8ddb36ae7c2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.