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youngermax/
text-tagger-v1

Name: text-tagger-v1
Availability: InStock

youngermax/text-tagging

autotrain_compatible

text-generation-inference

endpoints_compatible

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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

python

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:]))

youngermax/text-tagger-v1

Model Details

Model Description

Uses

Direct Use

youngermax/
text-tagger-v1