Improve language tag

Hi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.

README.md

@@ -1,37 +1,48 @@
----
-license: mit
-language:
-- zh
-- en
-base_model:
-- Qwen/Qwen2.5-7B-Instruct
-pipeline_tag: text-generation
----
-
-# Dataset Information
-
-We introduce an omnidirectional and automatic RAG benchmark, **OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain**, in the financial domain. Our benchmark is characterized by its multi-dimensional evaluation framework, including:
-
-1. a matrix-based RAG scenario evaluation system that categorizes queries into five task classes and 16 financial topics, leading to a structured assessment of diverse query scenarios;
-2. a multi-dimensional evaluation data generation approach, which combines GPT-4-based automatic generation and human annotation, achieving an 87.47% acceptance ratio in human evaluations on generated instances;
-3. a multi-stage evaluation system that evaluates both retrieval and generation performance, result in a comprehensive evaluation on the RAG pipeline;
-4. robust evaluation metrics derived from rule-based and LLM-based ones, enhancing the reliability of assessments through manual annotations and supervised fine-tuning of an LLM evaluator.
-
-Useful Links: 📝 [Paper](https://arxiv.org/abs/2412.13018) • 🤗 [Hugging Face](https://huggingface.co/collections/RUC-NLPIR/omnieval-67629ccbadd3a715a080fd25) • 🧩 [Github](https://github.com/RUC-NLPIR/OmniEval)
-
-We have trained two models from Qwen2.5-7B by the lora strategy and human-annotation labels to implement model-based evaluation.Note that the evaluator of hallucination is different from other four.
-
-We provide the evaluator for the hallucination metric in this repo.
-
-# 🌟 Citation
-```bibtex
-@misc{wang2024omnievalomnidirectionalautomaticrag,
-      title={OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain}, 
-      author={Shuting Wang and Jiejun Tan and Zhicheng Dou and Ji-Rong Wen},
-      year={2024},
-      eprint={2412.13018},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2412.13018}, 
-}
-```
+---
+license: mit
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+pipeline_tag: text-generation
+---
+
+# Dataset Information
+
+We introduce an omnidirectional and automatic RAG benchmark, **OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain**, in the financial domain. Our benchmark is characterized by its multi-dimensional evaluation framework, including:
+
+1. a matrix-based RAG scenario evaluation system that categorizes queries into five task classes and 16 financial topics, leading to a structured assessment of diverse query scenarios;
+2. a multi-dimensional evaluation data generation approach, which combines GPT-4-based automatic generation and human annotation, achieving an 87.47% acceptance ratio in human evaluations on generated instances;
+3. a multi-stage evaluation system that evaluates both retrieval and generation performance, result in a comprehensive evaluation on the RAG pipeline;
+4. robust evaluation metrics derived from rule-based and LLM-based ones, enhancing the reliability of assessments through manual annotations and supervised fine-tuning of an LLM evaluator.
+
+Useful Links: 📝 [Paper](https://arxiv.org/abs/2412.13018) • 🤗 [Hugging Face](https://huggingface.co/collections/RUC-NLPIR/omnieval-67629ccbadd3a715a080fd25) • 🧩 [Github](https://github.com/RUC-NLPIR/OmniEval)
+
+We have trained two models from Qwen2.5-7B by the lora strategy and human-annotation labels to implement model-based evaluation.Note that the evaluator of hallucination is different from other four.
+
+We provide the evaluator for the hallucination metric in this repo.
+
+# 🌟 Citation
+```bibtex
+@misc{wang2024omnievalomnidirectionalautomaticrag,
+      title={OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain}, 
+      author={Shuting Wang and Jiejun Tan and Zhicheng Dou and Ji-Rong Wen},
+      year={2024},
+      eprint={2412.13018},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2412.13018}, 
+}
+```