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pierjoe/
Qwen3-VL-4B-SCT-Classifier

Name: Qwen3-VL-4B-SCT-Classifier
Availability: InStock

safetensors

qwen3_vl

finance

Qwen/Qwen3-VL-4B-Instruct

finetune

mit

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Qwen2.5-VL-3B-SCT-Classifier

A Vision-Language Model fine-tuned to classify SEC proxy statement tables as Summary Compensation Tables (SCT) vs non-SCT tables.

Model Description

This model adds a classification head on top of Qwen/Qwen2.5-VL-3B-Instruct for binary table classification. The base model is frozen and only the classifier head is trained.

Task: Given an image of a table from SEC DEF14A filings, classify whether it's a Summary Compensation Table or not.

Architecture

Base Model: Qwen/Qwen2.5-VL-3B-Instruct (frozen)
Classifier Head: Linear(2048→512) → ReLU → Dropout(0.1) → Linear(512→2)
Pooling: Mean pooling over last hidden states

Training

Dataset: pierjoe/sec-table-classifier
- ~1,500 positive samples (SCT tables)
- ~3,000 negative samples (other tables from same documents)
Loss: CrossEntropyLoss with class weights [1.0, 2.0] to reduce false negatives
Optimizer: AdamW, LR=5e-6
Epochs: 3
Batch Size: 2

Performance

Test Accuracy: ~99%
False Negatives: Minimized via weighted loss (priority: don't miss real SCT tables)

Usage

python

import torch
import torch.nn as nn
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from safetensors.torch import load_file
from PIL import Image

# Define classifier
class VLMClassifier(nn.Module):
    def __init__(self, base_model, num_labels=2):
        super().__init__()
        self.base_model = base_model
        hidden_size = base_model.config.hidden_size
        self.classifier = nn.Sequential(
            nn.Linear(hidden_size, 512),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(512, num_labels)
        )
        
    def forward(self, input_ids, attention_mask, pixel_values, image_grid_thw):
        outputs = self.base_model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            pixel_values=pixel_values,
            image_grid_thw=image_grid_thw,
            output_hidden_states=True,
            return_dict=True
        )
        hidden_states = outputs.hidden_states[-1]
        pooled = hidden_states.mean(dim=1)
        return self.classifier(pooled.float())

# Load model
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct")
base_model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen2.5-VL-3B-Instruct", 
    torch_dtype=torch.bfloat16, 
    device_map="cuda:0"
)
model = VLMClassifier(base_model, num_labels=2).to("cuda")
model.classifier.load_state_dict(load_file("classifier_head.safetensors"))
model.eval()

# Inference
img = Image.open("table.png").convert("RGB")
messages = [[{
    "role": "user",
    "content": [
        {"type": "image", "image": img},
        {"type": "text", "text": "Classify this table."}
    ]
}]]
texts = [processor.apply_chat_template(m, tokenize=False, add_generation_prompt=True) for m in messages]
inputs = processor(text=texts, images=[img], padding=True, return_tensors="pt")

with torch.no_grad():
    logits = model(
        inputs["input_ids"].to("cuda"),
        inputs["attention_mask"].to("cuda"),
        inputs["pixel_values"].to("cuda", dtype=torch.bfloat16),
        inputs["image_grid_thw"].to("cuda")
    )
    prob_sct = torch.softmax(logits, dim=-1)[0, 1].item()

print(f"P(SCT) = {prob_sct:.3f}")
# Use threshold 0.3 for fewer false negatives
is_sct = prob_sct >= 0.3

Files

classifier_head.safetensors - Classifier head weights
classifier_config.json - Model configuration
config.json - Base model config
notebooks/ - Training and testing notebooks

Citation

Part of SEC executive compensation extraction pipeline.

License

Apache 2.0 (same as base model)

pierjoe/Qwen3-VL-4B-SCT-Classifier