change readme
Browse files
README.md
CHANGED
@@ -128,7 +128,7 @@ generated_tokens = model.generate(**input_ids.to(device), generation_config=gene
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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#YouTube
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# text brief summary generate
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prefix = 'summary brief: '
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@@ -139,7 +139,18 @@ generated_tokens = model.generate(**input_ids.to(device), generation_config=gene
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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#YouTube
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# text big summary generate
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prefix = 'summary big: '
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@@ -150,7 +161,7 @@ generated_tokens = model.generate(**input_ids.to(device), generation_config=gene
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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#YouTube
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```
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@@ -187,7 +198,7 @@ generated_tokens = model.generate(**input_ids.to(device), generation_config=gene
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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#In Beijing Winter Olympics
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# text brief summary generate
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prefix = 'summary brief to en: '
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@@ -198,7 +209,18 @@ generated_tokens = model.generate(**input_ids.to(device), generation_config=gene
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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#
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# text big summary generate
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prefix = 'summary big to en: '
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@@ -209,7 +231,7 @@ generated_tokens = model.generate(**input_ids.to(device), generation_config=gene
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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#In
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```
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@@ -246,7 +268,7 @@ generated_tokens = model.generate(**input_ids.to(device), generation_config=gene
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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-
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# text brief summary generate
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prefix = 'summary brief: '
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@@ -257,7 +279,18 @@ generated_tokens = model.generate(**input_ids.to(device), generation_config=gene
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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-
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# text big summary generate
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prefix = 'summary big: '
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@@ -268,7 +301,7 @@ generated_tokens = model.generate(**input_ids.to(device), generation_config=gene
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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-
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```
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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#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.
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# text brief summary generate
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prefix = 'summary brief: '
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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#YouTube has announced a crackdown on misinformation about Covid-19 vaccines.
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# generate a 4-word summary of the text
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prefix = 'summary brief 4 words: '
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src_text = prefix + text
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input_ids = tokenizer(src_text, return_tensors="pt")
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generated_tokens = model.generate(**input_ids.to(device), generation_config=generation_config)
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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#YouTube removes vaccine misinformation.
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# text big summary generate
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prefix = 'summary big: '
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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#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.
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```
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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#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.
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# text brief summary generate
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prefix = 'summary brief to en: '
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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#"Chinese Skier Wins Silver in Beijing"
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# generate a 4-word summary of the text
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prefix = 'summary brief to en 4 words: '
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src_text = prefix + text
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input_ids = tokenizer(src_text, return_tensors="pt")
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generated_tokens = model.generate(**input_ids.to(device), generation_config=generation_config)
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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#"Chinese Skier Wins Silver"
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# text big summary generate
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prefix = 'summary big to en: '
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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#In the women's freestyle ski slope obstacle technique final at the Beijing Winter Olympics, Chinese skater Gu Ailing won silver. She scored 69.90 in her first jump, placing third among the 12 competitors. Despite a fall in the second round, she managed to land smoothly, earning 86.23 points. The final was held in three rounds.
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```
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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#Эйфелева башня - самое высокое здание в Париже, высотой 324 метра. Ее основание квадратное, размером 125 метров с каждой стороны. Во время строительства она превзошла монумент Вашингтона, став самым высоким искусственным сооружением в мире.
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# text brief summary generate
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prefix = 'summary brief: '
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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#Эйфелева башня - самое высокое здание в Париже, высотой 324 метра.
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# generate a 4-word summary of the text
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prefix = 'summary brief 4 words: '
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src_text = prefix + text
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input_ids = tokenizer(src_text, return_tensors="pt")
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generated_tokens = model.generate(**input_ids.to(device), generation_config=generation_config)
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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#Эйфелева башня - самая высокая.
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# text big summary generate
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prefix = 'summary big: '
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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#Эйфелева башня - самое высокое здание в Париже, высотой 324 метра. Ее основание квадратное, размером 125 метров с каждой стороны. Во время строительства она превзошла монумент Вашингтона, став самым высоким искусственным сооружением в мире. Из-за добавления вещательной антенны на вершине башни она сейчас выше здания Крайслер на 5,2 метра (17 футов). За исключением передатчиков, башня является второй самой высокой отдельно стоящей структурой во Франции после виадука Мийо.
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```
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