vectara
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hallucination_evaluation_model

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microsoft/deberta-v3-base

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ofermend commited on Mar 27

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 The HHEM model is an open source model, created by [Vectara](https://vectara.com), for detecting hallucinations in LLMs. It is particularly useful in the context of building retrieval-augmented-generation (RAG) applications where a set of facts is summarized by an LLM, but the model can also be used in other contexts.
 If you are interested to learn more about RAG or experiment with Vectara, you can [sign up](https://console.vectara.com/signup/?utm_source=huggingface&utm_medium=space&utm_term=hhem-model&utm_content=console&utm_campaign=) for a free Vectara account.
 Now let's dive into the details of the model.
 ## Cross-Encoder for Hallucination Detection

 The HHEM model is an open source model, created by [Vectara](https://vectara.com), for detecting hallucinations in LLMs. It is particularly useful in the context of building retrieval-augmented-generation (RAG) applications where a set of facts is summarized by an LLM, but the model can also be used in other contexts.
 If you are interested to learn more about RAG or experiment with Vectara, you can [sign up](https://console.vectara.com/signup/?utm_source=huggingface&utm_medium=space&utm_term=hhem-model&utm_content=console&utm_campaign=) for a free Vectara account.
+Vectara now implements an improved version of HHEM which is a calibrated score with longer sequence length, and non-English language support. See more details [here](https://vectara.com/blog/automating-hallucination-detection-introducing-vectara-factual-consistency-score/)
 Now let's dive into the details of the model.
 ## Cross-Encoder for Hallucination Detection