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---
license: mit
language:
- en
base_model: distilbert/distilbert-base-uncased
library_name: transformers
tags:
- distilbert
- bert
- text-classification
- commission-detection
- social-media
pipeline_tag: text-classification
datasets:
- custom
model-index:
- name: distilbert-commissions
results:
- task:
type: text-classification
name: Text Classification
dataset:
type: custom
name: Scraped Social Media Profiles (Bluesky & Twitter)
metrics:
- name: Accuracy
type: accuracy
value: 0.9506
verified: false
- name: Precision
type: precision
value: 0.9513
verified: false
- name: Recall
type: recall
value: 0.9506
verified: false
- name: F1 Score
type: f1
value: 0.9508
verified: false
---
# DistilBERT Commission Detection Model
## Model Description
This is a fine-tuned DistilBERT model for detecting commission-related content in social media profiles and posts. The model classifies text to identify whether an artist's profile/bio/post content shows they are open or closed for commissions, or if the text is unclear.
## Model Details
### Model Architecture
- **Base Model**: [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased)
- **Model Type**: Text Classification
- **Language**: English
- **License**: MIT
### Training Data
- **Sources**: Manually scraped profile names, bios, and posts from Bluesky and Twitter by a crowd of furries uploading classifications via a custom extension built specifically to make this dataset
- **Dataset**: Custom dataset of ~1000 rows and user classifications with an equal amount of artificial data to boost pattern recognition
## Performance
| Metric | Value |
|--------|-------|
| Accuracy | 95.06% |
| Precision | 95.13% |
| Recall | 95.06% |
| F1 Score | 95.08% |
*Note: These metrics are not independently verified.*
## Usage
I recommend a high temperature when inferencing to lower the model's confidence. I use between 1.5 - 3.0.
```python
# Example inference #
from transformers import DistilBertForSequenceClassification, DistilBertTokenizer
import torch
# Load model and tokenizer #
model_name = 'zohfur/distilbert-commissions'
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name, num_labels=3)
# Example usage #
example_sentences = [
"Commissions are currently closed.",
"Check my bio for commission status.",
"C*mms 0pen on p-site",
"DM for comms",
"Taking art requests, dm me",
"comm completed for personmcperson, thank you <3",
"open for trades",
"Comms are not open",
"Comms form will be open soon, please check back later",
"~ Furry artist - 25 y.o - he/him - c*mms 0pen: 2/5 - bots dni ~"
]
# Map label integers back to strings #
label_map = {0: 'open', 1: 'closed', 2: 'unclear'}
def predict_with_temperature(model, tokenizer, sentences, temperature=1.5):
# Prepare input #
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
encoded_input = {key: value.to(device) for key, value in encoded_input.items()}
model.to(device)
model.eval()
# Make predictions with temperature scaling #
with torch.no_grad():
outputs = model(**encoded_input)
logits = outputs['logits'] / temperature # Apply temperature scaling #
probabilities = torch.softmax(logits, dim=1)
# Extract predictions and confidence scores #
predicted_class_indices = torch.argmax(probabilities, dim=1)
confidences = torch.max(probabilities, dim=1).values
# Convert to CPU and prepare results #
predictions = {
'sentences': sentences,
'labels': [label_map[idx.item()] for idx in predicted_class_indices],
'confidences': [score.item() for score in confidences]
}
return predictions
def print_predictions(predictions):
"""Print formatted predictions with confidence scores."""
print("\nClassification Results:")
print("=" * 50)
for i, (sentence, label, confidence) in enumerate(zip(
predictions['sentences'],
predictions['labels'],
predictions['confidences']
), 1):
print(f"\n{i}. Sentence: '{sentence}'")
print(f" Predicted Label: {label}")
print(f" Confidence Score: {confidence:.4f}")
# Make predictions with temperature scaling #
predictions = predict_with_temperature(model, tokenizer, example_sentences, temperature=1.5)
# Print results #
print_predictions(predictions)
```
## Limitations and Biases
### Limitations
- **Language**: Only trained on English text
- **False Positives**: Requires a high temperature to avoid false positives (particularly with the words "open" and "closed")
- **Platform Bias**: Trained on Bsky and Twitter/X data, might not perform as well on other platforms like FurAffinity or Instagra
## Training Details
### Training Procedure
- **Base Model**: DistilBERT base uncased
- **Fine-tuning**: Finetuned using Huggingface's Trainer, evaluated using Trainer and sklearn.metrics
- **Optimization**: Wandb hyperparameter sweep using bayers algorithm to reach highest f1 score
### Data Preprocessing
- Classifications uploaded voluntarily by crowdsourcing extension users
- Problematic unicode characters cleaned from dataset
- Label encoding for classification
- Class weights computed to adjust weights inversely proportional to class frequencies
## Model Card Authors
All credit to original author Zohfur. Base model attributed to distilbert.
## Model Card Contact
For questions or concerns about this model, please contact: [[email protected]] |