https://github.com/riccorl/transformers-embedder
A Word Level Transformer layer based on PyTorch and 🤗 Transformers.
https://github.com/riccorl/transformers-embedder
allennlp bert deep-learning embeddings hidden-states huggingface huggingface-transformers language-model natural-language-processing nlp preprocess pretrained-models python pytorch sentences tokenizer transformer transformer-embedder transformers transformers-embedder
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A Word Level Transformer layer based on PyTorch and 🤗 Transformers.
- Host: GitHub
- URL: https://github.com/riccorl/transformers-embedder
- Owner: Riccorl
- Created: 2020-11-27T14:54:14.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-01-31T04:40:19.000Z (over 1 year ago)
- Last Synced: 2025-04-14T04:16:32.603Z (about 2 months ago)
- Topics: allennlp, bert, deep-learning, embeddings, hidden-states, huggingface, huggingface-transformers, language-model, natural-language-processing, nlp, preprocess, pretrained-models, python, pytorch, sentences, tokenizer, transformer, transformer-embedder, transformers, transformers-embedder
- Language: Python
- Homepage: https://riccorl.github.io/transformers-embedder
- Size: 905 KB
- Stars: 34
- Watchers: 1
- Forks: 5
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Transformers Embedder
[](https://github.dev/Riccorl/transformers-embedder)
[](https://pytorch.org/)
[](https://huggingface.co/transformers/)
[](https://github.com/psf/black)
[](https://github.com/Riccorl/transformers-embedder/actions/workflows/python-publish-pypi.yml)
[](https://github.com/Riccorl/transformers-embedder/actions/workflows/python-publish-conda.yml)
[](https://github.com/Riccorl/transformers-embedder/releases)
[](https://anaconda.org/riccorl/transformers-embedder)
[](https://deepsource.io/gh/Riccorl/transformers-embedder/?ref=repository-badge)
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/).