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
| { | |
| "created_at_utc": "2026-06-09T17:45:48.616170+00:00", | |
| "kind": "cross_encoder_train", | |
| "pairs_dir": "experiments/cross_encoder_pairs", | |
| "model_name": "xlm-roberta-base", | |
| "output_dir": "models/mhqa-cross-encoder-reranker", | |
| "train_pairs": 84327, | |
| "eval_pairs": 27938, | |
| "eval_metrics": { | |
| "eval_loss": 0.011129951104521751, | |
| "eval_runtime": 21.446, | |
| "eval_samples_per_second": 1302.717, | |
| "eval_steps_per_second": 40.754, | |
| "epoch": 2.0 | |
| }, | |
| "push_to_hub": true, | |
| "hub_model_id": "Goodnight7/mhqa-cross-encoder-reranker" | |
| } |