metadata
datasets:
- AnyaSchen/image2poetry_ru
language:
- ru
tags:
- image2poetry
- Pushkin
- Tyutchev
- Mayakovsky
- Esenin
- Blok
This repo contains model for russian poetry generation from images. Poetry can be generated in style of poets: Маяковский, Пушкин, Есенин, Тютчев, Блок. The model is fune-tuned concatecation of pre-trained model tuman/vit-rugpt2-image-captioning. To use this model you can write:
from PIL import Image
import requests
from transformers import AutoTokenizer, VisionEncoderDecoderModel, ViTImageProcessor
def generate_poetry(fine_tuned_model, image, tokenizer, author):
pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
pixel_values = pixel_values.to(device)
# Encode author's name and prepare as input to the decoder
author_input = f"<bos> {author} <sep>"
decoder_input_ids = tokenizer.encode(author_input, return_tensors="pt").to(device)
# Generate the poetry with the fine-tuned VisionEncoderDecoder model
generated_tokens = fine_tuned_model.generate(
pixel_values,
decoder_input_ids=decoder_input_ids,
max_length=300,
num_beams=3,
top_p=0.8,
temperature=2.0,
do_sample=True,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
)
# Decode the generated tokens
generated_poetry = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
generated_poetry = generated_poetry.split(f'{author}')[-1]
return generated_poetry
path = 'AnyaSchen/vit-rugpt3-large-poetry-ft'
fine_tuned_model = VisionEncoderDecoderModel.from_pretrained(path).to(device)
feature_extractor = ViTImageProcessor.from_pretrained(path)
tokenizer = AutoTokenizer.from_pretrained(path)
url = 'https://anandaindia.org/wp-content/uploads/2018/12/happy-man.jpg'
image = Image.open(requests.get(url, stream=True).raw)
generated_poetry = generate_poetry(fine_tuned_model, image, tokenizer, 'Маяковский')
print(generated_poetry)