README.md

---
language: en
license: mit
tags:
- classification
- lobbying
- linkedin
datasets: custom
---

# Lobbyist classifier (English (US))

Binary sequence classifier fine-tuned to predict whether a LinkedIn-style job position (title + employer + description) corresponds to a **lobbyist** (1) or not (0). Trained for the project "Who Becomes a Lobbyist?" (MINISTERIALLOBBY) on Revelio/LinkedIn position text, with labels from the German Bundestag lobby register (DE) or LobbyView (US).

- **Base model:** `distilbert-base-uncased`
- **Task:** Sequence classification (2 labels: non-lobbyist, lobbyist)
- **Max length:** 256 tokens

## Evaluation (5-fold CV)

- Mean F1: 0.8942 (± 0.0025)
- Fold F1 scores: [0.8954220915581689, 0.891170431211499, 0.8943089430894309, 0.8919135308246597, 0.8982282653481665]
- Training samples: 12834 (positive: 6417)

## Intended use

- Research: classify past or current job positions as lobby vs non-lobby for career-path and panel analyses.
- Not for commercial use without checking compliance with LinkedIn/Revelio terms.

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

repo_id = "cornelius/lobbyist-classifier-us"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSequenceClassification.from_pretrained(repo_id)

def predict(texts, threshold=0.95):
    inp = tokenizer(texts, truncation=True, max_length=256, padding="max_length", return_tensors="pt")
    with torch.no_grad():
        logits = model(**inp).logits
    probs = torch.softmax(logits, dim=1)
    return probs[:, 1].numpy()  # prob lobbyist

# Single position: title + " " + company + " " + description
text = "Senior Public Affairs Manager  Acme Corp  Government relations and advocacy."
prob = predict([text])[0]
print(f"P(lobbyist) = {prob:.2f}")
```

## Citation

If you use this model, please cite the paper "Who Becomes a Lobbyist? Comparative Evidence from the US and Germany" (MINISTERIALLOBBY project, DFG).