Instructions to use vectara/hallucination_evaluation_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vectara/hallucination_evaluation_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vectara/hallucination_evaluation_model", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("vectara/hallucination_evaluation_model", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Model not performing well on large documents like chat summary
#4
by sourabh89 - opened
Hi , How can I extend this to support large documents (>512 tokens) ?
I don't have information on how to do that with the current model. However, we are preparing a more advanced version of the model that will feature a longer context length.
Hello there, any progress on that attempt? Thanks for your time.
We've launched the Factual Consistency Score (docs, blog) on our platform using an internal version of the HHEM model that supports a much longer context length.
We are preparing a new open source model with better performance, including support for a longer context length. There is no specific ETA, but it'll be quite soon.