umarfarzan/sideeffect-algorithm-expert

Social Media Content Analyzer

This model is fine-tuned from DeepSeek-R1-Distill-Llama-8B to analyze social media content and generate:

1. Detailed content critiques analyzing:
   • Hook effectiveness
   • Reliability factor
   • Relatability
   • Shareability
2. Attention-grabbing titles optimized for TikTok, Instagram Reels, or YouTube Shorts

Usage Example

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "umarfarzan/social-media-content-analyzer"
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)

def generate_content_analysis(transcript, confidence_score):
    prompt = f"""Below is a transcript from a social media video along with its confidence score.
Your task is to analyze the content and provide a detailed content critique analyzing the hook, reliability factor, relatability, and shareability.

### Transcript:
{transcript}

### Confidence Score:
{confidence_score}

### Content Critique:"""

    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    outputs = model.generate(
        input_ids=inputs.input_ids,
        attention_mask=inputs.attention_mask,
        max_new_tokens=1000,
        temperature=0.7,
        top_p=0.9
    )
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response.split("### Content Critique:")[1].strip()

# Example usage
transcript = "Let me show you how to track your expenses with this simple spreadsheet template..."
score = 88
critique = generate_content_analysis(transcript, score)
print(critique)

Training

This model was fine-tuned using Unsloth on a dataset of social media content with expert annotations.