Update README.md

README.md

@@ -5,7 +5,7 @@ license: mit
 # Model Card for FIRST
 
 <!-- Provide a quick summary of what the model is/does. -->
-FIRST is a language models trained specifically for reranking tasks, leveraging the output logits of the first generated identifier to directly produce a ranked ordering of candidates. Built on the Zephyr-7B-β model, FIRST undergoes single-stage fine-tuning on the converted alphabetic RankZephyr dataset, which includes RankGPT-4 reorderings of OpenAI's Ada2 outputs for 5k queries.
+FIRST is a language models trained specifically for listwise reranking tasks, leveraging the output logits of the first generated identifier to directly produce a ranked ordering of candidates. Built on the Zephyr-7B-β model, FIRST undergoes single-stage fine-tuning on a converted alphabetic version of the RankZephyr dataset, which includes RankGPT-4 reorderings of OpenAI's Ada2 outputs for 5k queries.
 
 ### Model Description
 
@@ -13,14 +13,14 @@ FIRST is a language models trained specifically for reranking tasks, leveraging
 - **Model type:** A 7B parameter GPT-like model based on Zephyr-7B-β model and further fine-tuned on task-specific listwise reranking data
 - **Language(s) (NLP):** Primarily English
 - **License:** MIT
-- **Finetuned from model [optional]:** [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)
+- **Finetuned from model:** [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
 - **Repository:** [https://github.com/gangiswag/llm-reranker](https://github.com/gangiswag/llm-reranker)
-- **Paper [optional]:** [https://arxiv.org/abs/2406.15657](https://arxiv.org/abs/2406.15657)
+- **Paper:** [https://arxiv.org/abs/2406.15657](https://arxiv.org/abs/2406.15657)
 
 
 ### Evaluations
@@ -43,12 +43,6 @@ More details can be found in the paper.
 
 FIRST is trained specifically on monolingual English data, effectiveness on multilingual sets is not guaranteed.
 
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
 ## Citation [optional]
 
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->