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AdarshSingh7647
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Model Details
Model Provider
AdarshSingh7647
Model Tree
Qwen/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
Input — a chat message with the question followed by each candidate table, labeled ### Table 1, ### Table 2, ...:
text
Question: Which table shows 2022 quarterly revenue by region?### Table 1| Region | Q1 2022 | Q2 2022 | Q3 2022 | Q4 2022 ||---|---|---|---|---|| North America | 120 | 134 | 128 | 145 || Europe | 88 | 91 | 95 | 102 |### Table 2| Product | Units Sold | Year ||---|---|---|| Widget A | 4200 | 2021 |### Table 3| Region | Headcount ||---|---|| North America | 340 |
Output — a single JSON object with the ranked, one-indexed candidate positions, best first:
json
{"ranked_tables": [1, 3, 2]}
Map the numbers back to your own table ids to get the reranked list — position 1 in the output is ### Table 1 from the input, etc.
This card does not publish a verified results table for the Answer-Only objective on this training mix — earlier published numbers for this checkpoint could not be confirmed against source eval logs, so they're left out rather than risk repeating an error.
For a fully-verified comparison, see TabRankStandardSFT and TabRank, which include in-distribution results (SQA, TAT-QA, HybridQA, TabFact, NQ-Tables) and out-of-distribution results on 7 benchmarks from the IBM table-text-ir-evaluation suite. Source eval code and logs: GitHub.
python
from vllm import LLM, SamplingParamsfrom transformers import AutoTokenizerrepo = "AdarshSingh7647/TabRankNaive"tok = AutoTokenizer.from_pretrained(repo)llm = LLM(model=repo, dtype="bfloat16", max_model_len=32768)system = ("You are a table relevance expert. Given a question and a set of candidate tables ""rank them from most to least useful for answering the question. Output exactly ""JSON with key ranked_tables.")user = "Question: ...\n\n### Table 1\n...\n\n### Table 2\n...\n"msgs = [{"role": "system", "content": system}, {"role": "user", "content": user}]text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)out = llm.generate([text], SamplingParams(temperature=0.6, top_p=0.95, max_tokens=2048))print(out[0].outputs[0].text)
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerrepo = "AdarshSingh7647/TabRankNaive"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype=torch.bfloat16, device_map="auto")system = ("You are a table relevance expert. Given a question and a set of candidate tables ""rank them from most to least useful for answering the question. Output exactly ""JSON with key ranked_tables.")user = "Question: ...\n\n### Table 1\n...\n\n### Table 2\n...\n"msgs = [{"role": "system", "content": system}, {"role": "user", "content": user}]text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)inputs = tok(text, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=2048, temperature=0.6, top_p=0.95, do_sample=True)print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
Full training and eval code: github.com/AdarshSingh7647/TabRanker.
bibtex
@misc{singh2026tabrank,title={TabRank: Chain-of-Thought Distillation for Table Re-Rankers},author={Adarsh Singh and Kushal Raj Bhandari and Jianxi Gao and Soham Dan and Vivek Gupta},year={2026},eprint={2607.25182},archivePrefix={arXiv},primaryClass={cs.CL},url={https://arxiv.org/abs/2607.25182}}
The MultiTabQA data in this checkpoint's training mix comes from RAG over Tables:
bibtex
@misc{zou2025ragtableshierarchicalmemory,title={RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking},author={Jiaru Zou and Dongqi Fu and Sirui Chen and Xinrui He and Zihao Li and Yada Zhu and Jiawei Han and Jingrui He},year={2025},eprint={2504.01346},archivePrefix={arXiv},primaryClass={cs.CL},url={https://arxiv.org/abs/2504.01346}}