Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Goodnight7/mhqa-cross-encoder-reranker-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Goodnight7/mhqa-cross-encoder-reranker-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Goodnight7/mhqa-cross-encoder-reranker-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Goodnight7/mhqa-cross-encoder-reranker-v2") model = AutoModelForSequenceClassification.from_pretrained("Goodnight7/mhqa-cross-encoder-reranker-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 991ca0517b6fb90a2b19d80f4a88fcb57d9afe073514fc076498be7e45444288
- Size of remote file:
- 17.1 MB
- SHA256:
- c64b175d67ecd8c9eb34e4c8f299b2e0017a03bfbb425586e62ee79a8653cded
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.