metadata
library_name: transformers
datasets:
- youngermax/text-tagging
Model Details
Model Description
This model identifies multiple topics related to the text in natural language. It is finetuned on youngermax/text-tagging for 3.5 epoch over ~1.3 hours on a free Kaggle P100.
- Developed by: Lincoln Maxwell
- Model type: Generative Pretrained Transformer
- Language(s) (NLP): English
- Finetuned from model: DistilGPT2
Uses
Direct Use
input_ids = tokenizer.encode(prompt + '<|topic|>', return_tensors='pt').to('cuda')
# Generate text
output = model.generate(
input_ids,
max_length=1024,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id,
top_k=100,
top_p=0.5,
temperature=1
)
# Decode the output
text = tokenizer.decode(output[0], skip_special_tokens=False, early_stopping=True)
text = text[len(prompt):text.find('<|endoftext|>')]
topics = list(set(list(map(lambda x: x.strip(), text.split('<|topic|>')))[1:]))