Text Classification
Transformers
PyTorch
English
bert
zero-shot-classification
text-embeddings-inference
Instructions to use MoritzLaurer/MiniLM-L6-mnli-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MoritzLaurer/MiniLM-L6-mnli-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MoritzLaurer/MiniLM-L6-mnli-binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MoritzLaurer/MiniLM-L6-mnli-binary") model = AutoModelForSequenceClassification.from_pretrained("MoritzLaurer/MiniLM-L6-mnli-binary") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a0a8874595859af875d10f60a10ad06d892bda375c55702417c7ac44c5e4a0ed
- Size of remote file:
- 90.9 MB
- SHA256:
- 11110698debde3b4d250bb6a2b97a53a485067c2b1a4ba1204280d6a155cdb2a
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