Introduction to the Transformers Library for NLP: pipelines

Introduction to the Transformers Library for NLP: pipelines#

First, let’s install the required libraries to make sure everything works

!pip install datasets evaluate transformers[sentencepiece]

Transformers are everywhere!#

Transformer models are used to solve all kinds of NLP tasks, like the ones mentioned in the previous section. Here are some of the companies and organizations using Hugging Face and Transformer models, who also contribute back to the community by sharing their models:

The 🤗 Transformers library provides the functionality to create and use those shared models. The Model Hub contains thousands of pretrained models that anyone can download and use. You can also upload your own models to the Hub!

⚠️ The Hugging Face Hub is not limited to Transformer models. Anyone can share any kind of models or datasets they want! Create a huggingface.co account to benefit from all available features!

Before diving into how Transformer models work under the hood, let’s look at a few examples of how they can be used to solve some interesting NLP problems.

Working with pipelines#

The most basic object in the 🤗 Transformers library is the pipeline() function. It connects a model with its necessary preprocessing and postprocessing steps, allowing us to directly input any text and get an intelligible answer:

from transformers import pipeline

classifier = pipeline("sentiment-analysis")

text = """Dear Amazon, last week I ordered an Optimus Prime action figure \
from your online store in Germany. Unfortunately, when I opened the package, \
I discovered to my horror that I had been sent an action figure of Megatron \
instead! As a lifelong enemy of the Decepticons, I hope you can understand my \
dilemma. To resolve the issue, I demand an exchange of Megatron for the \
Optimus Prime figure I ordered. Enclosed are copies of my records concerning \
this purchase. I expect to hear from you soon. Sincerely, Bumblebee."""

classifier(text)

No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).
Using a pipeline without specifying a model name and revision in production is not recommended.

[{'label': 'NEGATIVE', 'score': 0.9015460014343262}]

We can even pass several sentences!

texts = [
    "I hate this so much!",
    "I'm not sure how I feel about this.",
    "I love this!",
]

classifier(texts)

[{'label': 'NEGATIVE', 'score': 0.9994558691978455},
 {'label': 'NEGATIVE', 'score': 0.9992402791976929},
 {'label': 'POSITIVE', 'score': 0.9998764991760254}]

By default, this pipeline selects a particular pretrained model that has been fine-tuned for sentiment analysis in English. The model is downloaded and cached when you create the classifier object. If you rerun the command, the cached model will be used instead and there is no need to download the model again.

There are three main steps involved when you pass some text to a pipeline:

The text is preprocessed into a format the model can understand.
The preprocessed inputs are passed to the model.
The predictions of the model are post-processed, so you can make sense of them.

Some of the currently available pipelines are:

feature-extraction (get the vector representation of a text)
fill-mask
ner (named entity recognition)
question-answering
sentiment-analysis / text-classification
summarization
text-generation
translation
zero-shot-classification

Let’s have a look at a few of these!

Named Entity Recognition (NER)#

Named entity recognition (NER) is a task where the model has to find which parts of the input text correspond to entities such as persons, locations, or organizations. Let’s look at an example:

from transformers import pipeline

ner = pipeline("ner", grouped_entities=True)
prediction = ner("My name is Victor and I am a student at IE University in Madrid.")

No model was supplied, defaulted to dbmdz/bert-large-cased-finetuned-conll03-english and revision f2482bf (https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english).
Using a pipeline without specifying a model name and revision in production is not recommended.
Some weights of the model checkpoint at dbmdz/bert-large-cased-finetuned-conll03-english were not used when initializing BertForTokenClassification: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']
- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).

import pandas as pd

df = pd.DataFrame(prediction)
df

	entity_group	score	word	start	end
0	PER	0.998917	Victor	11	17
1	ORG	0.997800	IE University	40	53
2	LOC	0.995458	Madrid	57	63

Here the model correctly identified that Victor is a person (PER), IE University as an organization (ORG), and Madrid a location (LOC).

Question Answering (QA)#

The question-answering pipeline answers questions using information from a given context:

reader = pipeline("question-answering")

text = """Dear Amazon, last week I ordered an Optimus Prime action figure \
from your online store in Germany. Unfortunately, when I opened the package, \
I discovered to my horror that I had been sent an action figure of Megatron \
instead! As a lifelong enemy of the Decepticons, I hope you can understand my \
dilemma. To resolve the issue, I demand an exchange of Megatron for the \
Optimus Prime figure I ordered. Enclosed are copies of my records concerning \
this purchase. I expect to hear from you soon. Sincerely, Bumblebee."""

question = "What does the customer want?"

outputs = reader(question=question, context=text)
pd.DataFrame([outputs])    

No model was supplied, defaulted to distilbert/distilbert-base-cased-distilled-squad and revision 626af31 (https://huggingface.co/distilbert/distilbert-base-cased-distilled-squad).
Using a pipeline without specifying a model name and revision in production is not recommended.

	score	start	end	answer
0	0.631292	335	358	an exchange of Megatron

Note that this pipeline works by extracting information from the provided context; it does not generate the answer.

Summarization#

Summarization is the task of reducing a text into a shorter text while keeping all (or most) of the important aspects referenced in the text. Here’s an example:

summarizer = pipeline("summarization")
summarizer(
    """
    America has changed dramatically during recent years. Not only has the number of 
    graduates in traditional engineering disciplines such as mechanical, civil, 
    electrical, chemical, and aeronautical engineering declined, but in most of 
    the premier American universities engineering curricula now concentrate on 
    and encourage largely the study of engineering science. As a result, there 
    are declining offerings in engineering subjects dealing with infrastructure, 
    the environment, and related issues, and greater concentration on high 
    technology subjects, largely supporting increasingly complex scientific 
    developments. While the latter is important, it should not be at the expense 
    of more traditional engineering.

    Rapidly developing economies such as China and India, as well as other 
    industrial countries in Europe and Asia, continue to encourage and advance 
    the teaching of engineering. Both China and India, respectively, graduate 
    six and eight times as many traditional engineers as does the United States. 
    Other industrial countries at minimum maintain their output, while America 
    suffers an increasingly serious decline in the number of engineering graduates 
    and a lack of well-educated engineers.
"""
)

No model was supplied, defaulted to sshleifer/distilbart-cnn-12-6 and revision a4f8f3e (https://huggingface.co/sshleifer/distilbart-cnn-12-6).
Using a pipeline without specifying a model name and revision in production is not recommended.

[{'summary_text': ' America has changed dramatically during recent years . The number of engineering graduates in the U.S. has declined in traditional engineering disciplines such as mechanical, civil,    electrical, chemical, and aeronautical engineering . Rapidly developing economies such as China and India continue to encourage and advance the teaching of engineering .'}]

you can specify a max_length or a min_length for the result.

summarizer = pipeline("summarization")
outputs = summarizer(text, max_length=45, clean_up_tokenization_spaces=True)
print(outputs[0]['summary_text'])

No model was supplied, defaulted to sshleifer/distilbart-cnn-12-6 and revision a4f8f3e (https://huggingface.co/sshleifer/distilbart-cnn-12-6).
Using a pipeline without specifying a model name and revision in production is not recommended.
Your min_length=56 must be inferior than your max_length=45.

 Bumblebee ordered an Optimus Prime action figure from your online store in Germany. Unfortunately, when I opened the package, I discovered to my horror that I had been sent an action figure of Megatron instead.

Translation#

For translation, the easiest way is to pick the model you want to use on the Model Hub. Here we’ll try translating from English to German:

translator = pipeline("translation_en_to_de", 
                      model="Helsinki-NLP/opus-mt-en-de")
outputs = translator(text, clean_up_tokenization_spaces=True, min_length=100)

Sehr geehrter Amazon, letzte Woche habe ich eine Optimus Prime Action Figur aus Ihrem Online-Shop in Deutschland bestellt. Leider, als ich das Paket öffnete, entdeckte ich zu meinem Entsetzen, dass ich stattdessen eine Action Figur von Megatron geschickt worden war! Als lebenslanger Feind der Decepticons, Ich hoffe, Sie können mein Dilemma verstehen. Um das Problem zu lösen, Ich fordere einen Austausch von Megatron für die Optimus Prime Figur habe ich bestellt. Eingeschlossen sind Kopien meiner Aufzeichnungen über diesen Kauf. Ich erwarte, von Ihnen bald zu hören. Aufrichtig, Bumblebee.

pd.set_option('display.max_colwidth', None)

pd.DataFrame(outputs)

translation_text

0

Sehr geehrter Amazon, letzte Woche habe ich eine Optimus Prime Action Figur aus Ihrem Online-Shop in Deutschland bestellt. Leider, als ich das Paket öffnete, entdeckte ich zu meinem Entsetzen, dass ich stattdessen eine Action Figur von Megatron geschickt worden war! Als lebenslanger Feind der Decepticons, Ich hoffe, Sie können mein Dilemma verstehen. Um das Problem zu lösen, Ich fordere einen Austausch von Megatron für die Optimus Prime Figur habe ich bestellt. Eingeschlossen sind Kopien meiner Aufzeichnungen über diesen Kauf. Ich erwarte, von Ihnen bald zu hören. Aufrichtig, Bumblebee.

Text generation#

Now let’s see how to use a pipeline to generate some text. The main idea here is that you provide a prompt and the model will auto-complete it by generating the remaining text. This is similar to the predictive text feature that is found on many phones. Text generation involves randomness, so it’s normal if you don’t get the same results as shown below.

generator = pipeline("text-generation")
response = "Dear Bumblebee, I am sorry to hear that your order was mixed up."
prompt = text + "\n\nCustomer service response:\n" + response
outputs = generator(prompt, max_length=200)

No model was supplied, defaulted to openai-community/gpt2 and revision 6c0e608 (https://huggingface.co/openai-community/gpt2).
Using a pipeline without specifying a model name and revision in production is not recommended.

Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.
Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.

print(outputs[0]['generated_text'])

Dear Amazon, last week I ordered an Optimus Prime action figure from your online store in Germany. Unfortunately, when I opened the package, I discovered to my horror that I had been sent an action figure of Megatron instead! As a lifelong enemy of the Decepticons, I hope you can understand my dilemma. To resolve the issue, I demand an exchange of Megatron for the Optimus Prime figure I ordered. Enclosed are copies of my records concerning this purchase. I expect to hear from you soon. Sincerely, Bumblebee.

Customer service response:
Dear Bumblebee, I am sorry to hear that your order was mixed up. Please make sure you will send me a replacement action figure in an appropriately labeled package. All Transformers are based on each previous Optimus Prime model, and can not be shipped in a custom order.

In addition, I would like to apologize for any inconvenience that has been caused by a purchase error. Thank you for your understanding. I

Using any model from the Hub in a pipeline#

The previous examples used the default model for the task at hand, but you can also choose a particular model from the Hub to use in a pipeline for a specific task — say, text generation. Go to the Model Hub and click on the corresponding tag on the left to display only the supported models for that task. You should get to a page like this one.

Let’s try the distilgpt2 model! Here’s how to load it in the same pipeline as before:

generator = pipeline("text-generation", model="distilgpt2")
generator(
    "In this course, we will teach you how to",
    max_length=30,
    num_return_sequences=2,
)

Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.
Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.

[{'generated_text': 'In this course, we will teach you how to convert from one-line English to another.'},
 {'generated_text': 'In this course, we will teach you how to properly read a language with some of the most advanced language features the world has to offer. This course'}]

You can refine your search for a model by clicking on the language tags, and pick a model that will generate text in another language. The Model Hub even contains checkpoints for multilingual models that support several languages.

Once you select a model by clicking on it, you’ll see that there is a widget enabling you to try it directly online. This way you can quickly test the model’s capabilities before downloading it.

Exercises#

Search in the Model Hub for a text-classification model that can be used for sentiment analysis in French. Then use it in a pipeline to classify the following text: “Je suis content de voir que tu t’amuses bien avec les transformers.” What do you think of the model’s predictions?

pipe = pipeline('sentiment-analysis', model="lxyuan/distilbert-base-multilingual-cased-sentiments-student")

pipe("Je suis content de voir que tu t'amuses bien avec les transformers.")

[{'label': 'positive', 'score': 0.6099125742912292}]

Search in the Model Hub for a text-classification model that can be used to classify news headlines into different topics.

pipe = pipeline('text-classification', 'M47Labs/english_news_classification_headlines')

Explore the “feature-extraction” pipeline, and use it with a few examples.

What do you think it does? What would be the use cases for this kind of pipeline?

pipe = pipeline('feature-extraction')

No model was supplied, defaulted to distilbert/distilbert-base-cased and revision 935ac13 (https://huggingface.co/distilbert/distilbert-base-cased).
Using a pipeline without specifying a model name and revision in production is not recommended.

pipe("What is your name?")

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import numpy as np

feature = pipe("What is your name?")

np.array(feature).shape

(1, 7, 768)

feature = pipe("What is your name, again?")

np.array(feature).shape

(1, 9, 768)