T5 Summary En Ru Zh Base 2048

🚀 用于英、俄、中文多语言文本摘要的T5模型

本模型旨在以多任务模式执行受控生成摘要文本内容的任务，并具备内置的英、俄、中语言翻译功能。

🚀 快速开始

该模型为T5多任务模型，具备有条件地受控生成摘要文本内容并进行翻译的能力。它总共能理解12种指令，具体依据设定的前缀而定：

1. "summary: " - 用于在源语言中生成简单简洁的内容
2. "summary brief: " - 用于在源语言中生成简短的摘要内容
3. "summary big: " - 用于在源语言中生成详细的摘要内容

该模型能够理解俄语、中文或英语列表中的任何语言文本，还能将结果翻译成俄语、中文或英语列表中的任何语言。

若要翻译成目标语言，需指定目标语言标识符作为前缀 "... to : "。其中，lang 可以取值为：ru、en、zh。无需指定源语言，此外，源文本可以是多语言的。

任务前缀如下： 4) "summary to en: " - 从多语言文本中生成英文摘要内容 5) "summary brief to en: " - 从多语言文本中生成英文的简短摘要内容 6) "summary big to en: " - 从多语言文本中生成英文的详细摘要内容 7) "summary to ru: " - 从多语言文本中生成俄文摘要内容 8) "summary brief to ru: " - 从多语言文本中生成俄文的简短摘要内容 9) "summary big to ru: " - 从多语言文本中生成俄文的详细摘要内容 10) "summary to zh: " - 从多语言文本中生成中文摘要内容 11) "summary brief to zh: " - 从多语言文本中生成中文的简短摘要内容 12) "summary big to zh: " - 从多语言文本中生成中文的详细摘要内容

该训练模型可处理2048个标记的上下文，并在详细任务中输出最多200个标记的摘要，在普通摘要任务中输出50个标记，在简短摘要任务中输出20个标记。

💻 使用示例

基础用法

英文文本摘要示例

from transformers import T5ForConditionalGeneration, T5Tokenizer

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

model_name = 'utrobinmv/t5_summary_en_ru_zh_base_2048'
model = T5ForConditionalGeneration.from_pretrained(model_name)
model.eval()
model.to(device)
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))

result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(result)
#YouTube is cracking down on videos that suggest Covid-19 vaccines are dangerous and harmful.

# 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))

result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(result)
#YouTube is cracking down on misleading information about Covid vaccines.

# 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))

result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(result)
#YouTube has said it will remove more than 1,500 videos of Covid vaccines from its platform in a bid to tackle the spread of misinformation about the jabs.

中文文本翻译成英文摘要示例

from transformers import T5ForConditionalGeneration, T5Tokenizer

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

model_name = 'utrobinmv/t5_summary_en_ru_zh_base_2048'
model = T5ForConditionalGeneration.from_pretrained(model_name)
model.eval()
model.to(device)
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))

result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(result)
#In Beijing Winter Olympics Games, Chinese contestant Grulove凌 won the silver card. Celebrate.

# 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))

result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(result)
#In Beijing Winter Olympics Games, Chinese contestant Gruelean won the silver card.

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

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

result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(result)
#In Beijing's Winter Olympics Games, the 12-year-old has won the silver card in a free-skating lady hillwalking contest. The first jump, Chinese contestant, 69.90.

俄文文本摘要示例

from transformers import T5ForConditionalGeneration, T5Tokenizer

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

model_name = 'utrobinmv/t5_summary_en_ru_zh_base_2048'
model = T5ForConditionalGeneration.from_pretrained(model_name)
model.eval()
model.to(device)
tokenizer = T5Tokenizer.from_pretrained(model_name)

text = """Высота башни составляет 324 метра (1063 фута), примерно такая же высота, как у 81-этажного здания, и самое высокое сооружение в Париже. Его основание квадратно, размером 125 метров (410 футов) с любой стороны. Во время строительства Эйфелева башня превзошла монумент Вашингтона, став самым высоким искусственным сооружением в мире, и этот титул она удерживала в течение 41 года до завершения строительство здания Крайслер в Нью-Йорке в 1930 году. Это первое сооружение которое достигло высоты 300 метров. Из-за добавления вещательной антенны на вершине башни в 1957 году она сейчас выше здания Крайслер на 5,2 метра (17 футов). За исключением передатчиков, Эйфелева башня является второй самой высокой отдельно стоящей структурой во Франции после виадука Мийо."""

# 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))

result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(result)
#Французская Эйфелева башня, ставшая самой высокой в мире, достигла высоты 300 метров (1063 фута).

# 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))

result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(result)
#Французская Эйфелева башня стала самой высокой в мире.

# 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))

result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(result)
#Французская Эйфелева башня, построенная в 1957 году, достигла высоты 300 метров (1063 фута) с любой стороны. Это самый высокий сооружения в мире после виадука Мийо.

📚 详细文档

支持的语言

俄语 (ru_RU)、中文 (zh_CN)、英语 (en_US)

📄 许可证

本模型采用的许可证为 apache - 2.0。