Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Goodnight7/mhqa-cross-encoder-reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Goodnight7/mhqa-cross-encoder-reranker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Goodnight7/mhqa-cross-encoder-reranker")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Goodnight7/mhqa-cross-encoder-reranker") model = AutoModelForSequenceClassification.from_pretrained("Goodnight7/mhqa-cross-encoder-reranker", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Goodnight7/mhqa-cross-encoder-reranker: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Goodnight7/mhqa-cross-encoder-reranker/resolve/main/tokenizer.json
- Command line
-
hf download hf://Goodnight7/mhqa-cross-encoder-reranker/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Goodnight7/mhqa-cross-encoder-reranker/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 61c623b5d358a2f3534e1173a59a647ea3ed22f345c69fc1ef4aa3646ad337d6
- Size of remote file:
- 17.1 MB
- SHA256:
- 12c287c46a2290dd1d1b1fb117ab5db83823f206b7d9a0b96da8e1e8d8cc65ae
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