transformers-embedder 3.0.11
Word level transformer based embeddings
License: Apache
Requires Python: >=3.6
Keywords: NLP deep learning transformer pytorch BERT google subtoken wordpieces embeddings

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# Transformers Embedder

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

## How to use

Install the library from [PyPI](https://pypi.org/project/transformers-embedder):

```bash
pip install transformers-embedder
```

or from [Conda](https://anaconda.org/riccorl/transformers-embedder):

```bash
conda install -c riccorl transformers-embedder
```

It offers a PyTorch layer and a tokenizer that support almost every pretrained model from Huggingface 
[🤗Transformers](https://huggingface.co/transformers/) library. Here is a quick example:

```python
import transformers_embedder as tre

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

model = tre.TransformersEmbedder(
    "bert-base-cased", subword_pooling_strategy="sparse", layer_pooling_strategy="mean"
)

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

```text
{
   'input_ids': tensor([[ 101, 1188, 1110, 170, 6876, 5650,  102]]),
   'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1]]),
   'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0]])
   'scatter_offsets': tensor([[0, 1, 2, 3, 4, 5, 6]]),
   'sparse_offsets': {
        'sparse_indices': tensor(
            [
                [0, 0, 0, 0, 0, 0, 0],
                [0, 1, 2, 3, 4, 5, 6],
                [0, 1, 2, 3, 4, 5, 6]
            ]
        ), 
        'sparse_values': tensor([1., 1., 1., 1., 1., 1., 1.]), 
        'sparse_size': torch.Size([1, 7, 7])
    },
   'sentence_length': 7  # with special tokens included
}
```

```python
outputs = model(**inputs)
```

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

## Info

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

### Model

#### Subword Pooling Strategy

The `TransformersEmbedder` class offers 3 ways to get the embeddings:

- `subword_pooling_strategy="sparse"`: computes the mean of the embeddings of the sub-tokens of each word 
  (i.e. the embeddings of the sub-tokens are pooled together) using a sparse matrix multiplication. This 
  strategy is the default one.
- `subword_pooling_strategy="scatter"`: computes the mean of the embeddings of the sub-tokens of each word
  using a scatter-gather operation. It is not deterministic, but it works with ONNX export.
- `subword_pooling_strategy="none"`: returns the raw output of the transformer model without sub-token pooling.

Here a little feature table:

|             |      Pooling       |   Deterministic    |        ONNX        |
|-------------|:------------------:|:------------------:|:------------------:|
| **Sparse**  | :white_check_mark: | :white_check_mark: |        :x:         |
| **Scatter** | :white_check_mark: |        :x:         | :white_check_mark: |
| **None**    |        :x:         | :white_check_mark: | :white_check_mark: |

#### Layer Pooling Strategy

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

- `layer_pooling_strategy="last"`: returns the last hidden state of the transformer model
- `layer_pooling_strategy="concat"`: returns the concatenation of the selected `output_layers` of the  
   transformer model
- `layer_pooling_strategy="sum"`: returns the sum of the selected `output_layers` of the transformer model
- `layer_pooling_strategy="mean"`: returns the average of the selected `output_layers` of the transformer model
- `layer_pooling_strategy="scalar_mix"`: returns the output of a parameterised scalar mixture layer of the 
   selected `output_layers` of the transformer model

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

```python
class TransformersEmbedder(torch.nn.Module):
    def __init__(
        self,
        model: Union[str, tr.PreTrainedModel],
        subword_pooling_strategy: str = "sparse",
        layer_pooling_strategy: str = "last",
        output_layers: Tuple[int] = (-4, -3, -2, -1),
        fine_tune: bool = True,
        return_all: bool = True,
    )
```

### Tokenizer

The `Tokenizer` class provides the `tokenize` method to preprocess the input for the `TransformersEmbedder` 
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 uses the HuggingFace tokenizer to tokenize them.

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

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

You can pass a pre-tokenized sentence (or batch of sentences) by setting `is_split_into_words=True`

```python
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)
```

#### Examples

First, initialize the tokenizer

```python
import transformers_embedder as tre

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

- You can pass a single sentence as a string:

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

```text
{
{
    'input_ids': [[101, 1188, 1110, 170, 6876, 5650, 102]],
    'token_type_ids': [[0, 0, 0, 0, 0, 0, 0]],
    'attention_mask': [[1, 1, 1, 1, 1, 1, 1]],
    'scatter_offsets': [[0, 1, 2, 3, 4, 5, 6]],
    'sparse_offsets': {
        'sparse_indices': tensor(
            [
                [0, 0, 0, 0, 0, 0, 0],
                [0, 1, 2, 3, 4, 5, 6],
                [0, 1, 2, 3, 4, 5, 6]
            ]
        ),
        'sparse_values': tensor([1., 1., 1., 1., 1., 1., 1.]),
        'sparse_size': torch.Size([1, 7, 7])
    },
    'sentence_lengths': [7],
}
```

- A sentence pair

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

```text
{
    'input_ids': [[101, 1188, 1110, 170, 6876, 5650, 138, 102, 1188, 1110, 170, 6876, 5650, 139, 102]],
    'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]],
    'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]],
    'scatter_offsets': [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]],
    'sparse_offsets': {
        'sparse_indices': tensor(
            [
                [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,  0],
                [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
                [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
            ]
        ),
        'sparse_values': tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),
        'sparse_size': torch.Size([1, 15, 15])
    },
    'sentence_lengths': [15],
}
```

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

```python
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 initializing the tokenizer with the model name:

```python
import transformers_embedder as tre

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

Then add the custom fields to it:

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

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 tokenizer input
- `value`: value to use for padding
- `length`: length to pad. It can be an `int`, or two string value, `subword` in which the element is padded 
to match the length of the subwords, and `word` where the element is padded relative to the length of the
batch after the merge of the subwords.

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

Finally, we can tokenize the input with the custom field:

```python
text = [
    "This is a sample sentence",
    "This is another example sentence just make it longer, with a comma too!"
]

tokenizer(text, padding=True, return_tensors=True, additional_inputs=custom_fields)
```

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

```text
>>> inputs

{
    'input_ids': tensor(
        [
            [ 101, 1188, 1110, 170, 6876, 5650, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [ 101, 1188, 1110, 1330, 1859, 5650, 1198, 1294, 1122, 2039, 117, 1114, 170, 3254, 1918, 1315, 106, 102]
        ]
    ),
    'token_type_ids': tensor(
        [
            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
        ]
    ), 
    'attention_mask': tensor(
        [
            [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
        ]
    ),
    'scatter_offsets': tensor(
        [
            [ 0, 1, 2, 3, 4, 5, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
            [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 13, 14, 15, 16]
        ]
    ),
    'sparse_offsets': {
        'sparse_indices': tensor(
            [
                [ 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,  1],
                [ 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 13, 14, 15, 16],
                [ 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]
            ]
        ),
        'sparse_values': tensor(
            [1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,
            1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,
            1.0000, 1.0000, 0.5000, 0.5000, 1.0000, 1.0000, 1.0000]
        ), 
        'sparse_size': torch.Size([2, 17, 18])
    }
    'sentence_lengths': [7, 17],
}
```

## Acknowledgements

Some code in the `TransformersEmbedder` class is taken from the [PyTorch Scatter](https://github.com/rusty1s/pytorch_scatter/)
library. The pretrained models and the core of the tokenizer is from [🤗 Transformers](https://huggingface.co/transformers/).