Text Classification
Transformers
PyTorch
Safetensors
English
bert
fast
monarch-matrices
mnli
efficiency
triton
hardware-efficient
sub-quadratic
fast-inference
h100-optimized
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use ykae/monarch-bert-base-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ykae/monarch-bert-base-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ykae/monarch-bert-base-mnli", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ykae/monarch-bert-base-mnli", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("ykae/monarch-bert-base-mnli", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ef1e88e418c92406fed8f0a403a5335cf80f08082ec0998a3cda9bcfc3f65fd9
- Size of remote file:
- 220 MB
- SHA256:
- df08af71ced68de7c27a397d9fe6324915c9d23f44d9f846bd01b7009b3ccfef
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