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README.md

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-license: mit
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+---
+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.
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+- **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)
+
+### 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)
+
+
+### Evaluations
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+At the time of release, FIRST demonstrates superior performance across a variety of datasets. The table below provides a detailed performance comparison against other LLM rerankers on the BEIR benchmark.
+| Reranker      | Training Data  | Avg.  | Climate FEVER | DBPedia | FEVER | FiQA  | Hotpot QA | MS Marco | NFCorpus | NQ    | Sci-docs | Sci-fact | Trec-COVID |
+|---------------|----------------|-------|---------------|---------|-------|-------|-----------|----------|----------|-------|----------|----------|------------|
+| Rank Vicuna   | GPT 3.5        | 50.7  | **28.2**      | 50.0    | 81.0  | 35.9  | 73.5      | 36.7     | 33.1     | 58.6  | 18.4     | 70.5     | 71.3       |
+| Rank Zephyr   | GPT 3.5 + 3.5  | 53.7  | 25.6          | 50.0    | 80.1  | **42.2**  | 71.6  | 42.7     | **37.7** | 65.6  | **20.5** | **76.7** | 78.4       |
+| **FIRST**     | GPT-4          | **54.3**  | 26.7          | **50.9**| **81.7**| 42.2  | **74.2**  | **44.4** | 37.4     | **66.4**| 20.4     | 74.6     | **78.8**  |
+More details can be found in the paper.
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+ We forward here an excerpt from the [Zephyr-7B-β model card](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta/blob/main/README.md#bias-risks--limitations):
+
+> **"Zephyr-7B-β has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model ([mistralai/Mistral-7B-v0.1]), however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this."**
+
+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. -->
+If you find FIRST useful for your work, please consider citing our paper:
+```bibtex
+@article{reddy2024first,
+  title={FIRST: Faster Improved Listwise Reranking with Single Token Decoding},
+  author={Reddy, Revanth Gangi and Doo, JaeHyeok and Xu, Yifei and Sultan, Md Arafat and Swain, Deevya and Sil, Avirup and Ji, Heng},
+  journal={arXiv preprint arXiv:2406.15657},
+  year={2024}
+}