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Statement Extractor (T5-Gemma 2)
A fine-tuned T5-Gemma 2 model for extracting structured statements from text. Part of corp-o-rate.com.
Model Description
This model extracts subject-predicate-object triples from unstructured text, with automatic entity type recognition and coreference resolution.
- Architecture: T5-Gemma 2 (270M-270M, 540M total parameters)
- Training Data: 77,515 examples from corporate and news documents
- Final Eval Loss: 0.209
- Max Input Length: 4,096 tokens
- Max Output Length: 2,048 tokens
Usage
Python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
model = AutoModelForSeq2SeqLM.from_pretrained(
"Corp-o-Rate-Community/statement-extractor",
torch_dtype=torch.bfloat16,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
"Corp-o-Rate-Community/statement-extractor",
trust_remote_code=True,
)
text = "Apple Inc. announced a commitment to carbon neutrality by 2030."
inputs = tokenizer(f"<page>{text}</page>", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=2048, num_beams=4)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
Input Format
Wrap your text in <page> tags:
<page>Your text here...</page>
Output Format
The model outputs XML with extracted statements:
<statements>
<stmt>
<subject type="ORG">Apple Inc.</subject>
<object type="EVENT">carbon neutrality by 2030</object>
<predicate>committed to</predicate>
<text>Apple Inc. committed to achieving carbon neutrality by 2030.</text>
</stmt>
</statements>
Entity Types
| Type | Description |
|---|---|
| ORG | Organizations (companies, agencies) |
| PERSON | People (names, titles) |
| GPE | Geopolitical entities (countries, cities) |
| LOC | Locations (mountains, rivers) |
| PRODUCT | Products (devices, services) |
| EVENT | Events (announcements, meetings) |
| WORK_OF_ART | Creative works (reports, books) |
| LAW | Legal documents |
| DATE | Dates and time periods |
| MONEY | Monetary values |
| PERCENT | Percentages |
| QUANTITY | Quantities and measurements |
Demo
Try the interactive demo at statement-extractor.vercel.app
Training
- Base model:
google/t5gemma-2-270m-270m - Training examples: 77,515
- Final eval loss: 0.209
- Training with refinement phase (LR=1e-6, epochs=0.2)
- Beam search: num_beams=4
About corp-o-rate
This model is part of corp-o-rate.com - an AI-powered platform for ESG analysis and corporate accountability.
License
MIT License
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