vectara
/

hallucination_evaluation_model

@@ -23,7 +23,13 @@ widget:
 ---
 <img src="candle.png" width="50" height="50" style="display: inline;">  In Loving memory of Simon Mark Hughes...
-# Cross-Encoder for Hallucination Detection
 This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
 The model outputs a probabilitity from 0 to 1, 0 being a hallucination and 1 being factually consistent.
 The predictions can be thresholded at 0.5 to predict whether a document is consistent with its source.
@@ -38,9 +44,11 @@ This model is based on [microsoft/deberta-v3-base](https://huggingface.co/micros
 * [AnyScale Ranking Test for Hallucinations](https://www.anyscale.com/blog/llama-2-is-about-as-factually-accurate-as-gpt-4-for-summaries-and-is-30x-cheaper) - 86.6 % Accuracy
 ## LLM Hallucination Leaderboard
-If you want to stay up to date with results of the latest tests using this model to evaluate the top LLM models, a public leaderboard is maintained and periodically updated on the [vectara/hallucination-leaderboard](https://github.com/vectara/hallucination-leaderboard) GitHub repository.
-## Note about using the Inference API Widget on the Right
 To use the model with the widget, you need to pass both documents as a single string separated with [SEP]. For example:
 * A man walks into a bar and buys a drink [SEP] A bloke swigs alcohol at a pub
@@ -148,7 +156,9 @@ array([0.61051559, 0.00047493709, 0.99639291, 0.00021221573, 0.99599433, 0.00141
 ```
 ## Contact Details
-Feel free to contact us on
 * X/Twitter - https://twitter.com/vectara or http://twitter.com/ofermend
 * Discussion [forums](https://discuss.vectara.com/)
-* Discord [server](https://discord.gg/GFb8gMz6UH)

 ---
 <img src="candle.png" width="50" height="50" style="display: inline;">  In Loving memory of Simon Mark Hughes...
+# Introduction
+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) for a free Vectara account.
+Now let's dive into the details of the model.
+## Cross-Encoder for Hallucination Detection
 This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
 The model outputs a probabilitity from 0 to 1, 0 being a hallucination and 1 being factually consistent.
 The predictions can be thresholded at 0.5 to predict whether a document is consistent with its source.
 * [AnyScale Ranking Test for Hallucinations](https://www.anyscale.com/blog/llama-2-is-about-as-factually-accurate-as-gpt-4-for-summaries-and-is-30x-cheaper) - 86.6 % Accuracy
 ## LLM Hallucination Leaderboard
+If you want to stay up to date with results of the latest tests using this model to evaluate the top LLM models, we have a [public leaderboard](https://huggingface.co/spaces/vectara/leaderboard) that is periodically updated, and results are also available on the [GitHub repository](https://github.com/vectara/hallucination-leaderboard).
+# Using HHEM
+## Using the Inference API Widget on the Right
 To use the model with the widget, you need to pass both documents as a single string separated with [SEP]. For example:
 * A man walks into a bar and buys a drink [SEP] A bloke swigs alcohol at a pub
 ```
 ## Contact Details
+Feel free to contact us with any questions:
 * X/Twitter - https://twitter.com/vectara or http://twitter.com/ofermend
 * Discussion [forums](https://discuss.vectara.com/)
+* Discord [server](https://discord.gg/GFb8gMz6UH)
+For more information about [Vectara](https://vectara.com) and how to use our RAG-as-a-service API platform, check out our [documentation](https://docs.vectara.com/docs/).