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
Confusion about unlimited context length
#21
by zhm0 - opened
We found in the configuration_hhem_v2.py file that hhem-2.1 is still based on flan-t5-base, and the output during inference is "Token indices sequence length is longer than the specified maximum sequence length for this model (1706 > 512). Running this sequence through the model will result in indexing errors". Does the unlimited context length require parameter control?
Thanks for your interest in HHEM. This issue has been asked by other users. You can simply ignore it.