Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use leomaurodesenv/nli-MiniLM2-L6-H768-answerable-or-not-augmented with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leomaurodesenv/nli-MiniLM2-L6-H768-answerable-or-not-augmented with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="leomaurodesenv/nli-MiniLM2-L6-H768-answerable-or-not-augmented")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("leomaurodesenv/nli-MiniLM2-L6-H768-answerable-or-not-augmented") model = AutoModelForSequenceClassification.from_pretrained("leomaurodesenv/nli-MiniLM2-L6-H768-answerable-or-not-augmented", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc50ea105c59826328f881922dc9772745fee55ebefcf2d769dc59feefd35421
- Size of remote file:
- 328 MB
- SHA256:
- 6e989e4065155947e2b734c09b718c45d7e9b6e7a221e9ab16870367d50d3466
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