Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use galkinv42/nli-cross-encoder-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use galkinv42/nli-cross-encoder-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="galkinv42/nli-cross-encoder-roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("galkinv42/nli-cross-encoder-roberta") model = AutoModelForSequenceClassification.from_pretrained("galkinv42/nli-cross-encoder-roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from galkinv42/nli-cross-encoder-roberta: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/galkinv42/nli-cross-encoder-roberta/resolve/main/tokenizer.json
- Command line
-
hf download hf://galkinv42/nli-cross-encoder-roberta/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/galkinv42/nli-cross-encoder-roberta/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- 23655b84172a600a5e44f67f7de2c5fdb422056037e0e9d6b87373d97b781448
- Size of remote file:
- 16.3 MB
- SHA256:
- 7e41945aa4a9532bcb7e07c2dcc2adcfe5efe501d3d26bd47d2d9440afa19150
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