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
How does HHEMv2 compare to top-tier NLI models?
#24 opened 4 months ago
by
hzhiqi
the max token length for HHEMv2? Token indices error and killed.
1
#23 opened 8 months ago
by
hustzjl
Finetune hallucination_evaluation_model checkpoint on custom data
1
#22 opened about 1 year ago
by
hegdma
Confusion about unlimited context length
1
#21 opened about 1 year ago
by
zhm0
How can I leverage a GPU to perform inference?
1
#20 opened about 1 year ago
by
KevinWangHP
why don't you make it more standard?
#19 opened about 1 year ago
by
Skepsun
not loading from checkpoint
🔥 2
5
#18 opened over 1 year ago
by
tcapelle
How to use "Trust_remote_code" using InferenceClient
1
#11 opened almost 2 years ago
by
shubhangikat
New Commit Causing Inference Error
4
#10 opened almost 2 years ago
by
izimmerman
Adding `safetensors` variant of this model
#7 opened about 2 years ago
by
SFconvertbot
Adding `safetensors` variant of this model
#5 opened over 2 years ago
by
barkermrl
Model not performing well on large documents like chat summary
3
#4 opened over 2 years ago
by
sourabh89
Adding `safetensors` variant of this model
#2 opened over 2 years ago
by
SFconvertbot