Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
text-embeddings-inference
Instructions to use henryscheible/eval_mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/eval_mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/eval_mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/eval_mnli") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/eval_mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from henryscheible/eval_mnli: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/henryscheible/eval_mnli/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://henryscheible/eval_mnli/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/henryscheible/eval_mnli/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 16fa87cfdce11d38ec45caaa1f63718c947b527161705ba02947484e2988c0fd
- Size of remote file:
- 438 MB
- SHA256:
- 2f69da878f47071e090cce071152136b06e41233af60902cecb5735c4f894b50
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