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")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 61c623b5d358a2f3534e1173a59a647ea3ed22f345c69fc1ef4aa3646ad337d6
- Size of remote file:
- 17.1 MB
- SHA256:
- 12c287c46a2290dd1d1b1fb117ab5db83823f206b7d9a0b96da8e1e8d8cc65ae
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.