T5 Summary En Ru Zh Large 2048

🚀 T5 Model for Multilingual Text Summary

This T5-based model is designed to handle the generation of summary text content in a multitasking mode. It comes with a built - in translation function supporting Russian, Chinese, and English. The model can understand text in these languages and translate the results among them.

✨ Features

• Multilingual Support: Understands text in Russian, Chinese, and English, and can generate summaries or translate results into any of these languages.
• Conditional Summary Generation: Offers 3 types of summary generation in the source language: simple concise, shortened, and elongated. You can also limit the output to a specific number of words.
• Translation Function: Translates the summary into the target language by specifying the target - language identifier as a prefix.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import T5ForConditionalGeneration, T5Tokenizer

device = 'cuda' #or 'cpu' for translate on cpu

model_name = 'utrobinmv/t5_summary_en_ru_zh_large_2048'
model = T5ForConditionalGeneration.from_pretrained(model_name)
model.eval()
model.to(device)

generation_config = model.generation_config

# for quality generation
generation_config.length_penalty = 0.6
generation_config.no_repeat_ngram_size = 2
generation_config.num_beams = 10

tokenizer = T5Tokenizer.from_pretrained(model_name)

text = """Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said.  The policy includes the termination of accounts of anti - vaccine influencers.  Tech giants have been criticised for not doing more to counter false health information on their sites.  In July, US President Joe Biden said social media platforms were largely responsible for people's scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue.  YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines.  In a blog post, the company said it had seen false claims about Covid jabs "spill over into misinformation about vaccines in general". The new policy covers long - approved vaccines, such as those against measles or hepatitis B.  "We're expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO," the post said, referring to the World Health Organization."""

# text summary generate
prefix = 'summary: '
src_text = prefix + text
input_ids = tokenizer(src_text, return_tensors="pt")

generated_tokens = model.generate(**input_ids.to(device), generation_config=generation_config)

result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(result)
#YouTube to remove videos claiming approved COVID - 19 vaccines cause harm, including autism, cancer, and infertility. It will terminate accounts of anti - vaccine influencers and expand its medical misinformation policies.

# text brief summary generate
prefix = 'summary brief: '
src_text = prefix + text
input_ids = tokenizer(src_text, return_tensors="pt")

generated_tokens = model.generate(**input_ids.to(device), generation_config=generation_config)

result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(result)
#YouTube has announced a crackdown on misinformation about Covid - 19 vaccines.

# generate a 4 - word summary of the text
prefix = 'summary brief 4 words: '
src_text = prefix + text
input_ids = tokenizer(src_text, return_tensors="pt")

generated_tokens = model.generate(**input_ids.to(device), generation_config=generation_config)

result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(result)
#YouTube removes vaccine misinformation.

# text big summary generate
prefix = 'summary big: '
src_text = prefix + text
input_ids = tokenizer(src_text, return_tensors="pt")

generated_tokens = model.generate(**input_ids.to(device), generation_config=generation_config)

result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(result)
#YouTube, owned by Google, is removing videos claiming approved vaccines are dangerous and cause autism, cancer, or infertility. The company will terminate accounts of anti - vaccine influencers and expand its medical misinformation policies. This follows criticism of tech giants for not doing more to combat false health information on their sites. In July, US President Joe Biden called for social media platforms to address the issue of vaccine skepticism. Since implementing a ban on Covid vaccine content in 2021, 13 million videos have been removed. New policies cover long - approved vaccinations, such as those against measles or hepatitis B.

Advanced Usage

from transformers import T5ForConditionalGeneration, T5Tokenizer

device = 'cuda' #or 'cpu' for translate on cpu

model_name = 'utrobinmv/t5_summary_en_ru_zh_large_2048'
model = T5ForConditionalGeneration.from_pretrained(model_name)
model.eval()
model.to(device)

generation_config = model.generation_config

# for quality generation
generation_config.length_penalty = 0.6
generation_config.no_repeat_ngram_size = 2
generation_config.num_beams = 10

tokenizer = T5Tokenizer.from_pretrained(model_name)

text = """在北京冬奥会自由式滑雪女子坡面障碍技巧决赛中，中国选手谷爱凌夺得银牌。祝贺谷爱凌！今天上午，自由式滑雪女子坡面障碍技巧决赛举行。决赛分三轮进行，取选手最佳成绩排名决出奖牌。第一跳，中国选手谷爱凌获得69.90分。在12位选手中排名第三。完成动作后，谷爱凌又扮了个鬼脸，甚是可爱。第二轮中，谷爱凌在道具区第三个障碍处失误，落地时摔倒。获得16.98分。网友：摔倒了也没关系，继续加油！在第二跳失误摔倒的情况下，谷爱凌顶住压力，第三跳稳稳发挥，流畅落地！获得86.23分！此轮比赛，共12位选手参赛，谷爱凌第10位出场。网友：看比赛时我比谷爱凌紧张，加油！"""

# text summary generate
prefix = 'summary to en: '
src_text = prefix + text
input_ids = tokenizer(src_text, return_tensors="pt")

generated_tokens = model.generate(**input_ids.to(device), generation_config=generation_config)

result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(result)
#In the women's freestyle skiing final at the Beijing Winter Olympics, Chinese skater Gu Ailing won silver. She scored 69.90 in the first jump, ranked 3rd among 12 competitors. Despite a fall, she managed to land smoothly, earning 86.23 points.

# text brief summary generate
prefix = 'summary brief to en: '
src_text = prefix + text
input_ids = tokenizer(src_text, return_tensors="pt")

generated_tokens = model.generate(**input_ids.to(device), generation_config=generation_config)

📚 Documentation

The model can understand the following task prefixes:

🔧 Technical Details

The model is trained to compress a context of 2048 tokens. It outputs a summary of up to 200 tokens in big tasks, 50 tokens in normal summary tasks, and 20 tokens in brief tasks.

📄 License

This project is licensed under the apache - 2.0 license.