transformer-embedder 1.7.16

Word level transformer based embeddings

Transformer Embedder

A Word Level Transformer layer based on PyTorch and 🤗 Transformers.

How to use

Install the library from PyPI:

pip install transformer-embedder

It offers a PyTorch layer and a tokenizer that support almost every pretrained model from Huggingface 🤗Transformers library. Here is a quick example:

import transformer_embedder as tre

tokenizer = tre.Tokenizer("bert-base-cased")
model = tre.TransformerEmbedder("bert-base-cased", subtoken_pooling="mean", output_layer="sum")

example = "This is a sample sentence"
inputs = tokenizer(example, return_tensors=True)

{
   'input_ids': tensor([[ 101, 1188, 1110,  170, 6876, 5650,  102]]),
   'offsets': tensor([[0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6]]]),
   'attention_mask': tensor([[True, True, True, True, True, True, True]]),
   'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0]])
   'sentence_length': 7  # with special tokens included
}

outputs = model(**inputs)

# outputs.shape[1:-1]       # remove [CLS] and [SEP]
torch.Size([1, 5, 768])
# len(example)
5

Info

One of the annoyance of using transfomer-based models is that it is not trivial to compute word embeddings from the sub-token embeddings they output. With this library it's as easy as using 🤗Transformers API to get word-level embeddings from theoretically every transformer model it supports.

Model

The TransformerEmbedder offer 4 ways to retrieve the word embeddings, defined by subtoken_pooling parameter:

• first: uses only the embedding of the first sub-token of each word
• last: uses only the embedding of the last sub-token of each word
• mean: computes the mean of the embeddings of the sub-tokens of each word
• none: returns the raw output of the transformer model without sub-token pooling

There are also multiple type of outputs you can get using output_layer parameter:

• last: returns the last hidden state of the transformer model
• concat: returns the concatenation of the last four hidden states of the transformer model
• sum: returns the sum of the last four hidden states of the transformer model
• pooled: returns the output of the pooling layer

If you also want all the outputs from the HuggingFace model, you can set return_all=True to get them.

class TransformerEmbedder(torch.nn.Module):
    def __init__(
        self,
        model: Union[str, tr.PreTrainedModel],
        subtoken_pooling: str = "first",
        output_layer: str = "last",
        fine_tune: bool = True,
        return_all: bool = False,
    )

Tokenizer

The Tokenizer class provides the tokenize method to preprocess the input for the TransformerEmbedder layer. You can pass raw sentences, pre-tokenized sentences and sentences in batch. It will preprocess them returning a dictionary with the inputs for the model. By passing return_tensors=True it will return the inputs as torch.Tensor.

By default, if you pass text (or batch) as strings, it splits them on spaces

text = "This is a sample sentence"
tokenizer(text)

text = ["This is a sample sentence", "This is another sample sentence"]
tokenizer(text)

You can also use SpaCy to pre-tokenize the inputs into words first, using use_spacy=True

text = "This is a sample sentence"
tokenizer(text, use_spacy=True)

text = ["This is a sample sentence", "This is another sample sentence"]
tokenizer(text, use_spacy=True)

or you can pass an pre-tokenized sentence (or batch of sentences) by setting is_split_into_words=True

text = ["This", "is", "a", "sample", "sentence"]
tokenizer(text, is_split_into_words=True)

text = [
    ["This", "is", "a", "sample", "sentence", "1"],
    ["This", "is", "sample", "sentence", "2"],
]
tokenizer(text, is_split_into_words=True) # here is_split_into_words is redundant

Examples

First, initialize the tokenizer

import transformer_embedder as tre

tokenizer = tre.Tokenizer("bert-base-cased")

• You can pass a single sentence as a string:

text = "This is a sample sentence"
tokenizer(text)

{
  'input_ids': [101, 1188, 1110, 170, 6876, 5650, 102],
  'offsets': [(0, 0), (1, 1), (2, 2), (3, 3), (4, 4), (5, 5), (6, 6)],
  'attention_mask': [True, True, True, True, True, True, True],
  'token_type_ids': [0, 0, 0, 0, 0, 0, 0],
  'sentence_length': 7
}

• A sentence pair

text = "This is a sample sentence A"
text_pair = "This is a sample sentence B"
tokenizer(text, text_pair)

{
  'input_ids': [101, 1188, 1110, 170, 6876, 5650, 138, 102, 1188, 1110, 170, 6876, 5650, 139, 102],
  'offsets': [(0, 0), (1, 1), (2, 2), (3, 3), (4, 4), (5, 5), (6, 6), (7, 7), (8, 8), (9, 9), (10, 10), (11, 11), (12, 12), (13, 13), (14, 14)],
  'attention_mask': [True, True, True, True, True, True, True, True, True, True, True, True, True, True, True],
  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],
  'sentence_length': 15
}

• A batch of sentences or sentence pairs. Using padding=True and return_tensors=True, the tokenizer returns the text ready for the model

batch = [
    ["This", "is", "a", "sample", "sentence", "1"],
    ["This", "is", "sample", "sentence", "2"],
    ["This", "is", "a", "sample", "sentence", "3"],
    # ...
    ["This", "is", "a", "sample", "sentence", "n", "for", "batch"],
]
tokenizer(batch, padding=True, return_tensors=True)

batch_pair = [
    ["This", "is", "a", "sample", "sentence", "pair", "1"],
    ["This", "is", "sample", "sentence", "pair", "2"],
    ["This", "is", "a", "sample", "sentence", "pair", "3"],
    # ...
    ["This", "is", "a", "sample", "sentence", "pair", "n", "for", "batch"],
]
tokenizer(batch, batch_pair, padding=True, return_tensors=True)

Custom fields

It is possible to add custom fields to the model input and tell the tokenizer how to pad them using add_padding_ops. Start by simply tokenizing the input (without padding or tensor mapping)

import transformer_embedder as tre

tokenizer = tre.Tokenizer("bert-base-cased")

text = [
    ["This", "is", "a", "sample", "sentence"],
    ["This", "is", "another", "example", "sentence", "just", "make", "it", "longer"]
]
inputs = tokenizer(text)

Then add the custom fileds to the result

custom_fields = {
  "custom_filed_1": [
    [0, 0, 0, 0, 1, 0, 0],
    [0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0]
  ]
}

inputs.update(custom_fields)

Now we can add the padding logic for our custom field custom_filed_1. add_padding_ops method takes in input

• key: name of the field in the tokenzer input
• value: value to use for padding
• length: length to pad. It can be an int, or two string value, subtoken in which the element is padded to the batch max length relative to the sub-tokens length, and word where the element is padded to the batch max length relative to the original word length

tokenizer.add_padding_ops("custom_filed_1", 0, "word")

Finally, pad the input and convert it to a tensor:

# manual processing
inputs = tokenizer.pad_batch(inputs)
inputs = tokenizer.to_tensor(inputs)

The inputs are ready for the model, including the custom filed.

>>> inputs

{
   "input_ids": tensor(
       [
           [101, 1188, 1110, 170, 6876, 5650, 102, 0, 0, 0, 0],
           [101, 1188, 1110, 1330, 1859, 5650, 1198, 1294, 1122, 2039, 102],
       ]
   ),
   "offsets": tensor(
       [
           [[0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6], [7, 7], [-1, -1], [-1, -1], [-1, -1]],
           [[0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6], [7, 7], [8, 8], [9, 9], [10, 10]],
       ]
   ),
   "attention_mask": tensor(
       [
           [True, True, True, True, True, True, True, False, False, False, False],
           [True, True, True, True, True, True, True, True, True, True, True],
       ]
   ),
   "word_mask": tensor(
       [
           [True, True, True, True, True, True, True, False, False, False, False],
           [True, True, True, True, True, True, True, True, True, True, True],
       ]
   ),
   "token_type_ids": tensor(
       [[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
   ),
   "sentence_length": tensor([7, 11]),
   "custom_filed_1": tensor(
       [[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0]]
   ),
}

SpaCy Tokenizer

By default, it uses the multilingual model xx_sent_ud_sm. You can change it with the language parameter during the tokenizer initialization. For example, if you prefer an English tokenizer:

tokenizer = tre.Tokenizer("bert-base-cased", language="en_core_web_sm")

For a complete list of languages and models, you can go here.

To-Do

Future developments

• Add an optional word tokenizer, maybe using SpaCy
• Add add_special_tokens wrapper
• Make pad_batch function more general
• Add logic (like how to pad, etc) for custom fields
  • Documentation
• Include all model outputs
  • Documentation
• A TensorFlow version (improbable)

Acknowledgements

Most of the code in the TransformerEmbedder class is taken from the AllenNLP library. The pretrained models and the core of the tokenizer is from 🤗 Transformers.