cometadata

funding-extraction-qwen3.5-9B-non-thinking-artifact-data-mix-grpo-mixed-reward

README

License: apache-2.0

Training Pipeline

Trained on the cometadata/funding-extraction-artifact-data-mix-grpo-mixed-reward dataset using its pre-split sft / rl / test separations on the Tinker training service.

Stage 1: Supervised Fine-Tuning (SFT)

Base model: Qwen/Qwen3.5-9B
Data (data/sft/): 3,528 real + 7,240 synthetic funding statements with gold-standard funder/award labels (synthetic upsampled 2×)
Data augmentation: 50% of training examples augmented with synthetic noise (OCR-like case errors, digit/letter swaps, Unicode artifacts, XML/HTML tags, LaTeX markup)

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Model Details

Model Provider

cometadata

Model Tree

Base

Qwen/Qwen3.5-9B

Adapter

this model

Input Modalities

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Output Modalities

Text

Supported Functionality

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Table with columns: Field, P, R, F1, F0.5, F1.5
Field	P	R	F1	F0.5	F1.5
Funder	0.9384	0.9362	0.9373	0.9379	0.9369
Award ID	0.9069	0.8909	0.8988	0.9037	0.8957
Scheme	0.7407	0.8264	0.7812	0.7564	0.7980
Title	0.9048	0.3958	0.5507	0.7197	0.4787

Table with columns: Field, P, R, F1, F0.5, F1.5
Field	P	R	F1	F0.5	F1.5
Funder	0.8882	0.8960	0.8921	0.8897	0.8936
Award ID	0.8889	0.8732	0.8810	0.8857	0.8779
Scheme	0.6889	0.7686	0.7266	0.7035	0.7422
Title	0.9048	0.3958	0.5507	0.7197	0.4787

Table with columns: Field, P, R, F1, F0.5, F1.5
Field	P	R	F1	F0.5	F1.5
Funder	0.8796	0.8874	0.8835	0.8812	0.8850
Award ID	0.8859	0.8702	0.8780	0.8827	0.8750
Scheme	0.6667	0.7438	0.7031	0.6808	0.7182
Title	0.8095	0.3542	0.4928	0.6439	0.4283

Table with columns: Field, P, R, F1, F0.5, F1.5
Field	P	R	F1	F0.5	F1.5
Funder	0.9376	0.8923	0.9144	0.9282	0.9058
Award ID	0.8407	0.8339	0.8373	0.8394	0.8360
Scheme	0.4118	0.5927	0.4860	0.4385	0.5221
Title	0.1034	0.0170	0.0293	0.0514	0.0229

Table with columns: Field, P, R, F1, F0.5, F1.5
Field	P	R	F1	F0.5	F1.5
Funder	0.9008	0.8555	0.8776	0.8913	0.8689
Award ID	0.8138	0.8072	0.8105	0.8125	0.8092
Scheme	0.3725	0.5363	0.4397	0.3968	0.4724
Title	0.0690	0.0114	0.0195	0.0342	0.0153

Table with columns: Field, P, R, F1, F0.5, F1.5
Field	P	R	F1	F0.5	F1.5
Funder	0.8722	0.8276	0.8493	0.8629	0.8408
Award ID	0.7963	0.7898	0.7930	0.7949	0.7918
Scheme	0.3333	0.4798	0.3934	0.3550	0.4227
Title	0.0690	0.0114	0.0195	0.0342	0.0153

Table with columns: Field, P, R, F1, F0.5, F1.5
Field	P	R	F1	F0.5	F1.5
Funder	0.9285	0.9216	0.9250	0.9271	0.9237
Award ID	0.8586	0.8560	0.8573	0.8581	0.8568
Scheme	0.7413	0.6704	0.7041	0.7260	0.6907
Title	0.7723	0.2267	0.3506	0.5214	0.2897

Table with columns: Field, P, R, F1, F0.5, F1.5
Field	P	R	F1	F0.5	F1.5
Funder	0.9001	0.8906	0.8953	0.8981	0.8935
Award ID	0.8416	0.8390	0.8403	0.8411	0.8398
Scheme	0.6757	0.6110	0.6417	0.6617	0.6296
Title	0.6634	0.1948	0.3011	0.4479	0.2489

Table with columns: Field, P, R, F1, F0.5, F1.5
Field	P	R	F1	F0.5	F1.5
Funder	0.8801	0.8690	0.8745	0.8778	0.8724
Award ID	0.8317	0.8291	0.8304	0.8312	0.8299
Scheme	0.6039	0.5461	0.5735	0.5913	0.5627
Title	0.6139	0.1802	0.2787	0.4144	0.2303

Table with columns: Field, P, R, F1, F0.5, F1.5
Field	P	R	F1	F0.5	F1.5
Funder	0.9348	0.9383	0.9365	0.9355	0.9372
Award ID	0.8711	0.8690	0.8700	0.8707	0.8696
Scheme	0.7515	0.6844	0.7164	0.7371	0.7037
Title	0.8750	0.2442	0.3818	0.5769	0.3138

Table with columns: Field, P, R, F1, F0.5, F1.5
Field	P	R	F1	F0.5	F1.5
Funder	0.9072	0.9061	0.9066	0.9070	0.9064
Award ID	0.8538	0.8517	0.8527	0.8534	0.8523
Scheme	0.6871	0.6257	0.6550	0.6739	0.6434
Title	0.7500	0.2093	0.3273	0.4945	0.2690

Table with columns: Field, P, R, F1, F0.5, F1.5
Field	P	R	F1	F0.5	F1.5
Funder	0.8863	0.8842	0.8852	0.8859	0.8848
Award ID	0.8439	0.8418	0.8428	0.8434	0.8424
Scheme	0.6074	0.5531	0.5789	0.5957	0.5687
Title	0.7083	0.1977	0.3091	0.4670	0.2540

Table with columns: Field, Llama 3.1 8B, Qwen3.5-9B, Δ
Field	Llama 3.1 8B	Qwen3.5-9B	Δ
Funder	0.9001	0.8921	−0.008
Award ID	0.8780	0.8810	+0.003
Scheme	0.6466	0.7266	+0.080
Title	0.5316	0.5507	+0.019

Table with columns: Field, Llama 3.1 8B, Qwen3.5-9B, Δ
Field	Llama 3.1 8B	Qwen3.5-9B	Δ
Funder	0.8999	0.8953	−0.005
Award ID	0.8477	0.8403	−0.007
Scheme	0.6370	0.6417	+0.005
Title	0.4110	0.3011	−0.110

python

from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B")model = PeftModel.from_pretrained(base_model, "cometadata/funding-extraction-qwen3.5-9B-artifact-data-mix-grpo-mixed-reward")tokenizer = AutoTokenizer.from_pretrained("cometadata/funding-extraction-qwen3.5-9B-artifact-data-mix-grpo-mixed-reward") prompt = """Extract funding information from the following statement: This work was supported by the National Science Foundation under grant DMS-1613002 and by the NIH (R01-AI123456).""" messages = [    {"role": "system", "content": "You are an expert at extracting structured funding metadata from academic papers. Given a funding statement, extract all funders and their associated awards. Return a JSON array of funder objects. Each funder has:\n- \"funder_name\": string or null\n- \"awards\": array of objects with \"award_ids\" (array of strings), \"funding_scheme\" (array of strings), and \"award_title\" (array of strings)\nReturn ONLY the JSON array, no other text."},    {"role": "user", "content": prompt},] # Model trained with thinking disabled; keep enable_thinking=False.inputs = tokenizer.apply_chat_template(    messages, return_tensors="pt", add_generation_prompt=True, enable_thinking=False)outputs = model.generate(inputs, max_new_tokens=512, do_sample=False)print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B")model = PeftModel.from_pretrained(base_model, "cometadata/funding-extraction-qwen3.5-9B-artifact-data-mix-grpo-mixed-reward")tokenizer = AutoTokenizer.from_pretrained("cometadata/funding-extraction-qwen3.5-9B-artifact-data-mix-grpo-mixed-reward") prompt = """Extract funding information from the following statement: This work was supported by the National Science Foundation under grant DMS-1613002 and by the NIH (R01-AI123456).""" messages = [    {"role": "system", "content": "You are an expert at extracting structured funding metadata from academic papers. Given a funding statement, extract all funders and their associated awards. Return a JSON array of funder objects. Each funder has:\n- \"funder_name\": string or null\n- \"awards\": array of objects with \"award_ids\" (array of strings), \"funding_scheme\" (array of strings), and \"award_title\" (array of strings)\nReturn ONLY the JSON array, no other text."},    {"role": "user", "content": prompt},] # Model trained with thinking disabled; keep enable_thinking=False.inputs = tokenizer.apply_chat_template(    messages, return_tensors="pt", add_generation_prompt=True, enable_thinking=False)outputs = model.generate(inputs, max_new_tokens=512, do_sample=False)print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

json

[  {    "funder_name": "National Science Foundation",    "awards": [      {        "award_ids": ["DMS-1613002"],        "funding_scheme": [],        "award_title": []      }    ]  },  {    "funder_name": "NIH",    "awards": [      {        "award_ids": ["R01-AI123456"],        "funding_scheme": [],        "award_title": []      }    ]  }]

[  {    "funder_name": "National Science Foundation",    "awards": [      {        "award_ids": ["DMS-1613002"],        "funding_scheme": [],        "award_title": []      }    ]  },  {    "funder_name": "NIH",    "awards": [      {        "award_ids": ["R01-AI123456"],        "funding_scheme": [],        "award_title": []      }    ]  }]

funding-extraction-qwen3.5-9B-non-thinking-artifact-data-mix-grpo-mixed-reward

README

Training Pipeline

Stage 1: Supervised Fine-Tuning (SFT)

Explore FriendliAI today

README

Training Pipeline

Stage 1: Supervised Fine-Tuning (SFT)

Stage 2: Reinforcement Learning (GRPO)

Evaluation Results

arxiv_test.jsonl (300 held-out examples)

Permissive (partial_ratio + token_set, no damping)

Balanced (length-damped + acronym detection)

Strict (token_sort_ratio only)

funding-entity-extraction-dataset-mix test sets

`test.jsonl` (347 examples)

`test_degraded.jsonl` (1,288 examples)

`test_with_context.jsonl` (322 examples)

Comparison to the Llama 3.1 8B baseline

Usage

Output Format

funding-extraction-qwen3.5-9B-non-thinking-artifact-data-mix-grpo-mixed-reward

README

Training Pipeline

Stage 1: Supervised Fine-Tuning (SFT)

Explore FriendliAI today

README

Training Pipeline

Stage 1: Supervised Fine-Tuning (SFT)

Stage 2: Reinforcement Learning (GRPO)

Evaluation Results

arxiv_test.jsonl (300 held-out examples)

Permissive (partial_ratio + token_set, no damping)

Balanced (length-damped + acronym detection)

Strict (token_sort_ratio only)

funding-entity-extraction-dataset-mix test sets

test.jsonl (347 examples)

test_degraded.jsonl (1,288 examples)

test_with_context.jsonl (322 examples)

Comparison to the Llama 3.1 8B baseline

Usage

Output Format

`test.jsonl` (347 examples)

`test_degraded.jsonl` (1,288 examples)

`test_with_context.jsonl` (322 examples)