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Model: AdarshSingh7647/TabRankMultiTableNaive Source: Original Platform
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132
README.md
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-8B
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- table-ranking
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- listwise-reranking
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- table-retrieval
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- table-question-answering
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- qwen3
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language:
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- en
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---
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# TabRankMultiTableNaive
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A single call generative listwise reranker for table retrieval built on Qwen3 8B.
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Given a question and a set of candidate tables the model returns the tables ordered
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from most to least useful for answering the question. It reads all candidates in one
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prompt and emits the full ranking in a single generation.
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This checkpoint is the **Single plus Multi Table Answer Only** variant.
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## What this variant does
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The model is trained to output the ranking directly. Reasoning traces were shown during training but masked from the loss so the model learns to rank without writing a chain of thought. This makes it the fastest variant at inference.
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## Training data
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This model is trained on a mix of **NQ Tables** and **MultiTabQA** which adds multi table retrieval questions. The sibling checkpoint [TabRankSingleTableNaive](https://huggingface.co/AdarshSingh7647/TabRankSingleTableNaive)
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uses the other training mix with the same objective.
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## Input and output format
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The user message lists candidate tables as blocks headed by `### Table 1` `### Table 2`
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and so on. The model returns a JSON object whose value is the ranked list of one based
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candidate positions best first:
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```json
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{"ranked_tables": [3, 1, 5, 2, 4]}
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```
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Map those positions back to your table ids to obtain the reranked list.
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## Evaluation
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Scored as a listwise reranker that reorders a first stage top 25 candidate list on four
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table question answering benchmarks. SQA and TAT QA use the full test split. HybridQA and
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TabFact use a fixed shared 500 query sample. `acc@10` counts a query correct only when every
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gold table falls inside the top 10.
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| Dataset | n | recall@10 | ndcg@10 | acc@10 | MRR |
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|---|---|---|---|---|---|
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| SQA | 148 | 0.878 | 0.727 | 0.818 | 0.689 |
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| TAT QA | 362 | 0.678 | 0.544 | 0.467 | 0.610 |
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| HybridQA | 500 | 0.901 | 0.788 | 0.810 | 0.818 |
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| TabFact | 500 | 0.801 | 0.719 | 0.624 | 0.807 |
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| **Mean** | | **0.815** | **0.695** | **0.680** | **0.731** |
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## Usage with vLLM
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```python
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from vllm import LLM, SamplingParams
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from transformers import AutoTokenizer
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repo = "AdarshSingh7647/TabRankMultiTableNaive"
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tok = AutoTokenizer.from_pretrained(repo)
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llm = LLM(model=repo, dtype="bfloat16", max_model_len=32768)
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system = ("You are a table relevance expert. Given a question and a set of candidate tables "
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"rank them from most to least useful for answering the question. Reason in a "
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"<think>...</think> block then output exactly JSON with key ranked_tables.")
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user = "Question: ...\n\n### Table 1\n...\n\n### Table 2\n...\n"
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msgs = [{"role": "system", "content": system}, {"role": "user", "content": user}]
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text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
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# the model outputs the ranking json directly
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out = llm.generate([text], SamplingParams(temperature=0.6, top_p=0.95, max_tokens=8192))
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print(out[0].outputs[0].text)
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```
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## Usage with Transformers
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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repo = "AdarshSingh7647/TabRankMultiTableNaive"
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tok = AutoTokenizer.from_pretrained(repo)
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model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype=torch.bfloat16, device_map="auto")
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system = ("You are a table relevance expert. Given a question and a set of candidate tables "
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"rank them from most to least useful for answering the question. Reason in a "
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"<think>...</think> block then output exactly JSON with key ranked_tables.")
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user = "Question: ...\n\n### Table 1\n...\n\n### Table 2\n...\n"
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msgs = [{"role": "system", "content": system}, {"role": "user", "content": user}]
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text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
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inputs = tok(text, return_tensors="pt").to(model.device)
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out = model.generate(**inputs, max_new_tokens=8192, temperature=0.6, top_p=0.95, do_sample=True)
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print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
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```
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## Model details
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* Base model Qwen3 8B
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* Method LoRA rank 16 fine tuning merged into the base weights so it loads directly
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* Precision bfloat16 single file safetensors near 16 GB
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* Family the six TabRank checkpoints span three objectives (Answer Only, Reasoning Generation,
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Reasoning Conditioned) across two training mixes (Single Table, Single plus Multi Table)
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## Citation
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The MultiTabQA data comes from RAG over Tables. Please cite it when using the Single plus
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Multi Table checkpoints:
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```bibtex
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@misc{zou2025ragtableshierarchicalmemory,
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title={RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking},
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author={Jiaru Zou and Dongqi Fu and Sirui Chen and Xinrui He and Zihao Li and Yada Zhu and Jiawei Han and Jingrui He},
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year={2025},
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eprint={2504.01346},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2504.01346}
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}
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```
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The accompanying TabRank paper is currently under review. A citation will be added here once it
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is available on arXiv.
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 12288,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 40960,
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"max_window_layers": 36,
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"model_type": "qwen3",
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"num_attention_heads": 32,
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
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"pad_token_id": null,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": false,
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"transformers_version": "5.7.0",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"temperature": 0.6,
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"top_k": 20,
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"top_p": 0.95,
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"transformers_version": "5.7.0"
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}
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3
model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:13db893968211707182259270b0da8a5b2bdc0e6ff75e756f32d4b59a12bffc1
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size 16381517208
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BIN
tabrank_task.png
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tabrank_task.png
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tokenizer.json
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3
tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
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size 11422650
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31
tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": [
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"<|im_start|>",
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|object_ref_end|>",
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"<|box_start|>",
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"<|box_end|>",
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"<|quad_start|>",
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"<|quad_end|>",
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|video_pad|>"
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],
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"is_local": true,
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"local_files_only": false,
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"model_max_length": 131072,
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"pad_token": "<|endoftext|>",
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"padding_side": "right",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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