Instructions to use conversify/response-score with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use conversify/response-score with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="conversify/response-score")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("conversify/response-score") model = AutoModelForSequenceClassification.from_pretrained("conversify/response-score", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from conversify/response-score: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/conversify/response-score/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://conversify/response-score/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/conversify/response-score/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- 3ca4c447839bb321a45a1dc050b540b77b673107cfb2fefbd75b814b8b58c4fd
- Size of remote file:
- 438 MB
- SHA256:
- ea58fb2bb5c59068f77b523eabefbd70e592100ba9b70a4a3b563c7ff8ac224a
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