distil-bert-classifier

📌 Model Card: distil-bert-classifier

This model is a fine-tuned DistilBERT model for sequence classification, designed to identify whether a place (e.g., restaurants, businesses) is NEW, CLOSED, or NEUTRAL based on short text snippets.

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🧠 Model Details

Model Description

• Base Model: distilbert-base-uncased
• Task: Sequence Classification
• Classes: NEW, CLOSED, NEUTRAL
• Language: English
• License: MIT (confirm if needed)
• Developer: virustechhacks

This model helps extract signals about business status from textual data such as reviews, posts, or headlines.

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🔗 Model Sources

• Repository: https://huggingface.co/virustechhacks/distil-bert-classifier

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🚀 Uses

✅ Direct Use

Classify short text snippets into:

• NEW → Newly opened places
• CLOSED → Shut down or no longer operating
• NEUTRAL → No clear status signal

🔄 Downstream Use

Outputs can be aggregated into features like:

• closed_signal_ratio
• new_signal_ratio
• mention_count

These can feed into larger ML pipelines (e.g., XGBoost models).

⚠️ Out-of-Scope

• General sentiment analysis beyond defined labels
• Non-English text
• Long documents (>128 tokens)
• High-stakes decision-making systems

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⚠️ Bias, Risks, and Limitations

• Synthetic Data Bias:
  Trained on rule-based synthetic data → may not generalize well to real-world language.

• No Time Awareness:
  Cannot distinguish recent vs outdated signals.

• Token Limit:
  Inputs >128 tokens are truncated.

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💡 Recommendations

For production use:

• Fine-tune on real-world datasets
• Add timestamp-based features
• Evaluate thoroughly on live data

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🛠️ How to Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import torch.nn.functional as F

repo_name = "virustechhacks/distil-bert-classifier"

tokenizer = AutoTokenizer.from_pretrained(repo_name)
model = AutoModelForSequenceClassification.from_pretrained(repo_name)

id_to_label = {0: "NEW", 1: "CLOSED", 2: "NEUTRAL"}

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

def predict_status(text):
    inputs = tokenizer(
        text,
        truncation=True,
        padding="max_length",
        max_length=128,
        return_tensors="pt"
    )
    inputs = {k: v.to(device) for k, v in inputs.items()}

    with torch.no_grad():
        outputs = model(**inputs)

    probs = F.softmax(outputs.logits, dim=-1)
    confidence, pred = torch.max(probs, dim=1)

    return id_to_label[pred.item()], confidence.item()

# Example
print(predict_status("Grand opening this weekend!"))
print(predict_status("The store ceased operations."))