kazu.utils.sapbert

Classes

HFSapbertInferenceDataset

A dataset to be used for inferencing.

SapBertHelper

Helper class to wrap useful SapBert inference functions.
class kazu.utils.sapbert.HFSapbertInferenceDataset[source]

Bases: Dataset[dict[str, Tensor]]

A dataset to be used for inferencing.

In addition to standard BERT encodings, this uses an ‘indices’ encoding that can be used to track the vector index of an embedding. This is needed in a multi GPU environment.

__init__(encodings)[source]

Simple implementation of IterableDataset, producing HF tokenizer input_id.

Parameters:

encodings (BatchEncoding) – Expected to be produced by a transformers.PreTrainedTokenizerFast. ‘slow’ tokenizers (transformers.PreTrainedTokenizer) store their encodings differently and so won’t work with this class as-is.

class kazu.utils.sapbert.SapBertHelper[source]

Bases: object

Helper class to wrap useful SapBert inference functions.

Original source:

https://github.com/cambridgeltl/sapbert

Licensed under MIT

Copyright (c) Facebook, Inc. and its affiliates.

Full License

MIT License

Copyright (c) Facebook, Inc. and its affiliates.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Paper:

Fangyu Liu, Ehsan Shareghi, Zaiqiao Meng, Marco Basaldella, and Nigel Collier. 2021.
Self-alignment pretraining for biomedical entity representations.
In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4228–4238.
Bibtex Citation Details
@inproceedings{liu2021self,
    title={Self-Alignment Pretraining for Biomedical Entity Representations},
    author={Liu, Fangyu and Shareghi, Ehsan and Meng, Zaiqiao and Basaldella, Marco and Collier, Nigel},
    booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
    pages={4228--4238},
    month = jun,
    year={2021}
}
__init__(path)[source]
Parameters:

path (str) – passed to transformers.AutoConfig, transformers.AutoTokenizer, transformers.AutoModel .from_pretrained.

get_embedding_dataloader_from_strings(texts, batch_size, num_workers, max_length=50)[source]

Get a dataloader with dataset HFSapbertInferenceDataset and DataCollatorWithPadding.

This should be used to generate embeddings for strings of interest.

Parameters:

  • texts (list[str]) – strings to use in the dataset

  • batch_size (int)

  • num_workers (int)

  • max_length (int)

Returns:

Return type:

DataLoader[BatchEncoding]

static get_embeddings(output)[source]

Get a tensor of embeddings in original order.

Parameters:

output (list[dict[int, Tensor]]) – int is the original index of the input.

Returns:

Return type:

Tensor

get_embeddings_for_strings(texts, batch_size=16)[source]

For a list of strings, generate embeddings.

This is a convenience function for users, as we need to carry out these steps several times in the codebase.

Parameters:

  • texts (list[str])

  • batch_size (int) – optional batch size to use. If not specified, use 16

Returns:

a 2d tensor of embeddings

Return type:

Tensor

get_embeddings_from_dataloader(loader)[source]

Get the cls token output from all data in a dataloader as a 2d tensor.

Parameters:

loader (DataLoader[BatchEncoding])

Returns:

2d tensor of cls output

Return type:

Tensor

static get_prediction_from_batch(model, batch)[source]

Pass the batch through the model, and return the Tensor of the cls token.

Parameters:
Return type:

dict[int, Tensor]