Text Classification
Transformers
ONNX
PEFT
English
cross-encoder
reranker
thread-matching
conversational-ai
lora
Eval Results (legacy)
Instructions to use Algokruti/thread-reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Algokruti/thread-reranker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Algokruti/thread-reranker")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Algokruti/thread-reranker", device_map="auto") - PEFT
How to use Algokruti/thread-reranker with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Download config.json from Algokruti/thread-reranker: direct link, hf CLI and curl.
- Browser
- Download file 330 Bytes
-
https://huggingface.co/Algokruti/thread-reranker/resolve/main/config.json
- Command line
-
hf download hf://Algokruti/thread-reranker/config.json
-
curl -L -o config.json https://huggingface.co/Algokruti/thread-reranker/resolve/main/config.json
330 Bytes
| { | |
| "base_model": "microsoft/MiniLM-L6-H384-uncased", | |
| "lora_r": 8, | |
| "lora_alpha": 16, | |
| "num_structured_features": 5, | |
| "structured_feature_names": [ | |
| "entity_overlap", | |
| "keyword_matches", | |
| "flow_continuity", | |
| "recency_score", | |
| "hours_since_active" | |
| ], | |
| "max_length": 256, | |
| "best_val_f1": 0.5887755102040816 | |
| } |