Transformers 4.37 中文文档（六）（5）

>>> from transformers import pipeline
>>> import torch
>>> torch.manual_seed(0)
>>> generator = pipeline('text-generation', model = 'gpt2')
>>> prompt = "Hello, I'm a language model"
>>> generator(prompt, max_length = 30)
[{'generated_text': "Hello, I'm a language model expert, so I'm a big believer in the concept that I know very well and then I try to look into"}]

>>> text2text_generator = pipeline("text2text-generation", model = 'google/flan-t5-base')
>>> prompt = "Translate from English to French: I'm very happy to see you"
>>> text2text_generator(prompt)
[{'generated_text': 'Je suis très heureuse de vous rencontrer.'}]

pip install -q transformers accelerate

>>> from transformers import pipeline, AutoTokenizer
>>> import torch
>>> torch.manual_seed(0)
>>> model = "tiiuae/falcon-7b-instruct"
>>> tokenizer = AutoTokenizer.from_pretrained(model)
>>> pipe = pipeline(
...     "text-generation",
...     model=model,
...     tokenizer=tokenizer,
...     torch_dtype=torch.bfloat16,
...     device_map="auto",
... )

>>> torch.manual_seed(0)
>>> prompt = """Classify the text into neutral, negative or positive. 
... Text: This movie is definitely one of my favorite movies of its kind. The interaction between respectable and morally strong characters is an ode to chivalry and the honor code amongst thieves and policemen.
... Sentiment:
... """
>>> sequences = pipe(
...     prompt,
...     max_new_tokens=10,
... )
>>> for seq in sequences:
...     print(f"Result: {seq['generated_text']}")
Result: Classify the text into neutral, negative or positive. 
Text: This movie is definitely one of my favorite movies of its kind. The interaction between respectable and morally strong characters is an ode to chivalry and the honor code amongst thieves and policemen.
Sentiment:
Positive

>>> torch.manual_seed(1)
>>> prompt = """Return a list of named entities in the text.
... Text: The Golden State Warriors are an American professional basketball team based in San Francisco.
... Named entities:
... """
>>> sequences = pipe(
...     prompt,
...     max_new_tokens=15,
...     return_full_text = False,    
... )
>>> for seq in sequences:
...     print(f"{seq['generated_text']}")
- Golden State Warriors
- San Francisco

>>> torch.manual_seed(2)
>>> prompt = """Translate the English text to Italian.
... Text: Sometimes, I've believed as many as six impossible things before breakfast.
... Translation:
... """
>>> sequences = pipe(
...     prompt,
...     max_new_tokens=20,
...     do_sample=True,
...     top_k=10,
...     return_full_text = False,
... )
>>> for seq in sequences:
...     print(f"{seq['generated_text']}")
A volte, ho creduto a sei impossibili cose prima di colazione.

>>> torch.manual_seed(3)
>>> prompt = """Permaculture is a design process mimicking the diversity, functionality and resilience of natural ecosystems. The principles and practices are drawn from traditional ecological knowledge of indigenous cultures combined with modern scientific understanding and technological innovations. Permaculture design provides a framework helping individuals and communities develop innovative, creative and effective strategies for meeting basic needs while preparing for and mitigating the projected impacts of climate change.
... Write a summary of the above text.
... Summary:
... """
>>> sequences = pipe(
...     prompt,
...     max_new_tokens=30,
...     do_sample=True,
...     top_k=10,
...     return_full_text = False,
... )
>>> for seq in sequences:
...     print(f"{seq['generated_text']}")
Permaculture is an ecological design mimicking natural ecosystems to meet basic needs and prepare for climate change. It is based on traditional knowledge and scientific understanding.

>>> torch.manual_seed(4)
>>> prompt = """Answer the question using the context below.
... Context: Gazpacho is a cold soup and drink made of raw, blended vegetables. Most gazpacho includes stale bread, tomato, cucumbers, onion, bell peppers, garlic, olive oil, wine vinegar, water, and salt. Northern recipes often include cumin and/or pimentón (smoked sweet paprika). Traditionally, gazpacho was made by pounding the vegetables in a mortar with a pestle; this more laborious method is still sometimes used as it helps keep the gazpacho cool and avoids the foam and silky consistency of smoothie versions made in blenders or food processors.
... Question: What modern tool is used to make gazpacho?
... Answer:
... """
>>> sequences = pipe(
...     prompt,
...     max_new_tokens=10,
...     do_sample=True,
...     top_k=10,
...     return_full_text = False,
... )
>>> for seq in sequences:
...     print(f"Result: {seq['generated_text']}")
Result: Modern tools are used, such as immersion blenders

>>> torch.manual_seed(5)
>>> prompt = """There are 5 groups of students in the class. Each group has 4 students. How many students are there in the class?"""
>>> sequences = pipe(
...     prompt,
...     max_new_tokens=30,
...     do_sample=True,
...     top_k=10,
...     return_full_text = False,
... )
>>> for seq in sequences:
...     print(f"Result: {seq['generated_text']}")
Result: 
There are a total of 5 groups, so there are 5 x 4=20 students in the class.

>>> torch.manual_seed(6)
>>> prompt = """I baked 15 muffins. I ate 2 muffins and gave 5 muffins to a neighbor. My partner then bought 6 more muffins and ate 2\. How many muffins do we now have?"""
>>> sequences = pipe(
...     prompt,
...     max_new_tokens=10,
...     do_sample=True,
...     top_k=10,
...     return_full_text = False,
... )
>>> for seq in sequences:
...     print(f"Result: {seq['generated_text']}")
Result: 
The total number of muffins now is 21

>>> torch.manual_seed(0)
>>> prompt = """Text: The first human went into space and orbited the Earth on April 12, 1961.
... Date: 04/12/1961
... Text: The first-ever televised presidential debate in the United States took place on September 28, 1960, between presidential candidates John F. Kennedy and Richard Nixon. 
... Date:"""
>>> sequences = pipe(
...     prompt,
...     max_new_tokens=8,
...     do_sample=True,
...     top_k=10,
... )
>>> for seq in sequences:
...     print(f"Result: {seq['generated_text']}")
Result: Text: The first human went into space and orbited the Earth on April 12, 1961.
Date: 04/12/1961
Text: The first-ever televised presidential debate in the United States took place on September 28, 1960, between presidential candidates John F. Kennedy and Richard Nixon. 
Date: 09/28/1960

Let's go through this step-by-step:
1\. You start with 15 muffins.
2\. You eat 2 muffins, leaving you with 13 muffins.
3\. You give 5 muffins to your neighbor, leaving you with 8 muffins.
4\. Your partner buys 6 more muffins, bringing the total number of muffins to 14.
5\. Your partner eats 2 muffins, leaving you with 12 muffins.
Therefore, you now have 12 muffins.

>>> from tokenizers import Tokenizer
>>> from tokenizers.models import BPE
>>> from tokenizers.trainers import BpeTrainer
>>> from tokenizers.pre_tokenizers import Whitespace
>>> tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
>>> trainer = BpeTrainer(special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
>>> tokenizer.pre_tokenizer = Whitespace()
>>> files = [...]
>>> tokenizer.train(files, trainer)

>>> from transformers import PreTrainedTokenizerFast
>>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)

>>> tokenizer.save("tokenizer.json")

>>> from transformers import PreTrainedTokenizerFast
>>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_file="tokenizer.json")

## 提示 vs 微调
通过优化您的提示，您可以取得出色的结果，但是您可能仍然在考虑是否微调模型对您的情况更有效。以下是一些微调较小模型可能是首选的情况：
+   您的领域与 LLMs 预先训练的领域大相径庭，广泛的提示优化并未产生足够的结果。
+   您需要您的模型在资源稀缺的语言中表现良好。
+   您需要训练模型的数据是受严格监管的敏感数据。
+   由于成本、隐私、基础设施或其他限制，您必须使用小型模型。
在上述所有示例中，您需要确保您已经拥有或可以轻松获得足够大的领域特定数据集，以合理的成本来微调模型。您还需要有足够的时间和资源来微调模型。
如果上述示例不适用于您，优化提示可能会更有益。
# 开发者指南
# 使用🤗 Tokenizers 中的分词器
> 原始文本：[`huggingface.co/docs/transformers/v4.37.2/en/fast_tokenizers`](https://huggingface.co/docs/transformers/v4.37.2/en/fast_tokenizers)
PreTrainedTokenizerFast 依赖于 [🤗 Tokenizers](https://huggingface.co/docs/tokenizers) 库。从🤗 Tokenizers 库获得的分词器可以非常简单地加载到🤗 Transformers 中。
在进入具体内容之前，让我们首先通过几行代码创建一个虚拟的分词器：
```py
>>> from tokenizers import Tokenizer
>>> from tokenizers.models import BPE
>>> from tokenizers.trainers import BpeTrainer
>>> from tokenizers.pre_tokenizers import Whitespace
>>> tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
>>> trainer = BpeTrainer(special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
>>> tokenizer.pre_tokenizer = Whitespace()
>>> files = [...]
>>> tokenizer.train(files, trainer)

>>> from transformers import PreTrainedTokenizerFast
>>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)

>>> tokenizer.save("tokenizer.json")

>>> from transformers import PreTrainedTokenizerFast
>>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_file="tokenizer.json")