from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# or AutoModelForSequenceClassification for moderation
# or SentenceTransformer for semantic-search

model_name = "yogami9/need-content-moderation"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage
input_text = "Your input here"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)

Training Data

Base Model: unitary/toxic-bert
Task: Text Classification
Fine-tuned for: NEED Service Marketplace Platform
Language: English
License: MIT

Trained on curated datasets specific to the NEED platform's service categories and user interactions.

Limitations

Optimized for English language
Best performance on NEED platform-specific queries
May require fine-tuning for other domains

Contact

Organization: NEED Service App
Email: needserviceapp@gmail.com
GitHub: https://github.com/Need-Service-App

Related Models

All NEED AI models: