Quickstart¶

Philosophy¶

Transformers is an opinionated library built for NLP researchers seeking to use/study/extend large-scale transformers models.

The library was designed with two strong goals in mind:

• be as easy and fast to use as possible:

• we strongly limited the number of user-facing abstractions to learn, in fact, there are almost no abstractions, just three standard classes required to use each model: configuration, models and tokenizer,

• all of these classes can be initialized in a simple and unified way from pretrained instances by using a common from_pretrained() instantiation method which will take care of downloading (if needed), caching and loading the related class from a pretrained instance supplied in the library or your own saved instance.

• as a consequence, this library is NOT a modular toolbox of building blocks for neural nets. If you want to extend/build-upon the library, just use regular Python/PyTorch modules and inherit from the base classes of the library to reuse functionalities like model loading/saving.

• provide state-of-the-art models with performances as close as possible to the original models:

• we provide at least one example for each architecture which reproduces a result provided by the official authors of said architecture,

• the code is usually as close to the original code base as possible which means some PyTorch code may be not as pytorchic as it could be as a result of being converted TensorFlow code.

A few other goals:

• expose the models’ internals as consistently as possible:

• we give access, using a single API to the full hidden-states and attention weights,

• tokenizer and base model’s API are standardized to easily switch between models.

• incorporate a subjective selection of promising tools for fine-tuning/investigating these models:

• a simple/consistent way to add new tokens to the vocabulary and embeddings for fine-tuning,

• simple ways to mask and prune transformer heads.

Main concepts¶

The library is build around three types of classes for each model:

• model classes e.g., BertModel which are 20+ PyTorch models (torch.nn.Modules) that work with the pretrained weights provided in the library. In TF2, these are tf.keras.Model.

• configuration classes which store all the parameters required to build a model, e.g., BertConfig. You don’t always need to instantiate these your-self. In particular, if you are using a pretrained model without any modification, creating the model will automatically take care of instantiating the configuration (which is part of the model)

• tokenizer classes which store the vocabulary for each model and provide methods for encoding/decoding strings in a list of token embeddings indices to be fed to a model, e.g., BertTokenizer

All these classes can be instantiated from pretrained instances and saved locally using two methods:

• from_pretrained() let you instantiate a model/configuration/tokenizer from a pretrained version either provided by the library itself (currently 27 models are provided as listed here) or stored locally (or on a server) by the user,

• save_pretrained() let you save a model/configuration/tokenizer locally so that it can be reloaded using from_pretrained().

We’ll finish this quickstart tour by going through a few simple quick-start examples to see how we can instantiate and use these classes. The rest of the documentation is organized into two parts:

• the MAIN CLASSES section details the common functionalities/method/attributes of the three main type of classes (configuration, model, tokenizer) plus some optimization related classes provided as utilities for training,

• the PACKAGE REFERENCE section details all the variants of each class for each model architectures and, in particular, the input/output that you should expect when calling each of them.

Quick tour: Usage¶

Here are two examples showcasing a few Bert and GPT2 classes and pre-trained models.

See the full API reference for examples of each model class.

BERT example¶

Let’s start by preparing a tokenized input (a list of token embeddings indices to be fed to Bert) from a text string using BertTokenizer

import torch
from transformers import BertTokenizer, BertModel, BertForMaskedLM

# OPTIONAL: if you want to have more information on what's happening under the hood, activate the logger as follows
import logging
logging.basicConfig(level=logging.INFO)

# Load pre-trained model tokenizer (vocabulary)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Tokenize input
text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
tokenized_text = tokenizer.tokenize(text)

# Mask a token that we will try to predict back with BertForMaskedLM
assert tokenized_text == ['[CLS]', 'who', 'was', 'jim', 'henson', '?', '[SEP]', 'jim', '[MASK]', 'was', 'a', 'puppet', '##eer', '[SEP]']

# Convert token to vocabulary indices
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
# Define sentence A and B indices associated to 1st and 2nd sentences (see paper)
segments_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]

# Convert inputs to PyTorch tensors
tokens_tensor = torch.tensor([indexed_tokens])
segments_tensors = torch.tensor([segments_ids])


Let’s see how we can use BertModel to encode our inputs in hidden-states:

# Load pre-trained model (weights)
model = BertModel.from_pretrained('bert-base-uncased')

# Set the model in evaluation mode to deactivate the DropOut modules
# This is IMPORTANT to have reproducible results during evaluation!
model.eval()

# If you have a GPU, put everything on cuda
tokens_tensor = tokens_tensor.to('cuda')
segments_tensors = segments_tensors.to('cuda')
model.to('cuda')

# Predict hidden states features for each layer
# See the models docstrings for the detail of the inputs
outputs = model(tokens_tensor, token_type_ids=segments_tensors)
# Transformers models always output tuples.
# See the models docstrings for the detail of all the outputs
# In our case, the first element is the hidden state of the last layer of the Bert model
encoded_layers = outputs[0]
# We have encoded our input sequence in a FloatTensor of shape (batch size, sequence length, model hidden dimension)
assert tuple(encoded_layers.shape) == (1, len(indexed_tokens), model.config.hidden_size)


And how to use BertForMaskedLM to predict a masked token:

# Load pre-trained model (weights)
model.eval()

# If you have a GPU, put everything on cuda
tokens_tensor = tokens_tensor.to('cuda')
segments_tensors = segments_tensors.to('cuda')
model.to('cuda')

# Predict all tokens
outputs = model(tokens_tensor, token_type_ids=segments_tensors)
predictions = outputs[0]

# confirm we were able to predict 'henson'
predicted_index = torch.argmax(predictions[0, masked_index]).item()
predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
assert predicted_token == 'henson'


OpenAI GPT-2¶

Here is a quick-start example using GPT2Tokenizer and GPT2LMHeadModel class with OpenAI’s pre-trained model to predict the next token from a text prompt.

First let’s prepare a tokenized input from our text string using GPT2Tokenizer

import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel

# OPTIONAL: if you want to have more information on what's happening, activate the logger as follows
import logging
logging.basicConfig(level=logging.INFO)

# Load pre-trained model tokenizer (vocabulary)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

# Encode a text inputs
text = "Who was Jim Henson ? Jim Henson was a"
indexed_tokens = tokenizer.encode(text)

# Convert indexed tokens in a PyTorch tensor
tokens_tensor = torch.tensor([indexed_tokens])


Let’s see how to use GPT2LMHeadModel to generate the next token following our text:

# Load pre-trained model (weights)

# Set the model in evaluation mode to deactivate the DropOut modules
# This is IMPORTANT to have reproducible results during evaluation!
model.eval()

# If you have a GPU, put everything on cuda
tokens_tensor = tokens_tensor.to('cuda')
model.to('cuda')

# Predict all tokens
outputs = model(tokens_tensor)
predictions = outputs[0]

# get the predicted next sub-word (in our case, the word 'man')
predicted_index = torch.argmax(predictions[0, -1, :]).item()
predicted_text = tokenizer.decode(indexed_tokens + [predicted_index])
assert predicted_text == 'Who was Jim Henson? Jim Henson was a man'


Examples for each model class of each model architecture (Bert, GPT, GPT-2, Transformer-XL, XLNet and XLM) can be found in the documentation.

Using the past¶

GPT-2, as well as some other models (GPT, XLNet, Transfo-XL, CTRL), make use of a past or mems attribute which can be used to prevent re-computing the key/value pairs when using sequential decoding. It is useful when generating sequences as a big part of the attention mechanism benefits from previous computations.

Here is a fully-working example using the past with GPT2LMHeadModel and argmax decoding (which should only be used as an example, as argmax decoding introduces a lot of repetition):

from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch

tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

generated = tokenizer.encode("The Manhattan bridge")
context = torch.tensor([generated])
past = None

for i in range(100):
print(i)
output, past = model(context, past=past)
token = torch.argmax(output[..., -1, :])

generated += [token.tolist()]
context = token.unsqueeze(0)

sequence = tokenizer.decode(generated)

print(sequence)


The model only requires a single token as input as all the previous tokens’ key/value pairs are contained in the past.