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---
license: openrail
inference:
parameters:
temperature: 0.7
max_length: 24
language:
- en
library_name: transformers
pipeline_tag: text2text-generation
tags:
- text-generation-inference
widget:
- text: 'generate title: Importance, Dataset, AI'
example_title: Example 1
- text: 'generate title: Amazon, Product, Business'
example_title: Example 2
- text: 'generate title: History, Computer, Software'
example_title: Example 3
---
,,,python
def generate_title(keywords):
input_ids = tokenizer(keywords, return_tensors="pt", padding="longest", truncation=True, max_length=32).input_ids.to(device)
outputs = model.generate(
input_ids,
num_beams=3,
num_beam_groups=3,
num_return_sequences=3,
repetition_penalty=7.0,
diversity_penalty=4.0,
no_repeat_ngram_size=3,
temperature=0.9,
max_length=32
)
return tokenizer.batch_decode(outputs, skip_special_tokens=True)
keywords = 'This repository contains a fine-tuned model for generating high-quality product descriptions.'
generate_title(keywords)
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