Commit Graph

18 Commits

Author SHA1 Message Date
Matthew Honnibal
1612990e88 Implement cosine loss for spacy pretrain. Make default 2019-03-20 11:06:58 +00:00
Matthew Honnibal
62afa64a8d Expose batch size and length caps on CLI for pretrain (#3417)
Add and document CLI options for batch size, max doc length, min doc length for `spacy pretrain`.

Also improve CLI output.

Closes #3216 

## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [x] I have submitted the spaCy Contributor Agreement.
- [x] I ran the tests, and all new and existing tests passed.
- [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
2019-03-16 21:38:45 +01:00
Ines Montani
25602c794c Tidy up and fix small bugs and typos 2019-02-08 14:14:49 +01:00
Matthew Honnibal
0f83b98afa Remove unused code from spacy pretrain 2018-12-18 19:19:26 +01:00
Matthew Honnibal
7c504b6ddb Try to implement more losses for pretraining
* Try to implement cosine loss
This one seems to be correct? Still unsure, but it performs okay

* Try to implement the von Mises-Fisher loss
This one's definitely not right yet.
2018-12-17 14:48:27 +00:00
Matthew Honnibal
df15279e88 Reduce batch size during pretrain 2018-12-10 15:30:23 +00:00
Matthew Honnibal
83ac227bd3
💫 Better support for semi-supervised learning (#3035)
The new spacy pretrain command implemented BERT/ULMFit/etc-like transfer learning, using our Language Modelling with Approximate Outputs version of BERT's cloze task. Pretraining is convenient, but in some ways it's a bit of a strange solution. All we're doing is initialising the weights. At the same time, we're putting a lot of work into our optimisation so that it's less sensitive to initial conditions, and more likely to find good optima. I discuss this a bit in the pseudo-rehearsal blog post: https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting
Support semi-supervised learning in spacy train

One obvious way to improve these pretraining methods is to do multi-task learning, instead of just transfer learning. This has been shown to work very well: https://arxiv.org/pdf/1809.08370.pdf . This patch makes it easy to do this sort of thing.

    Add a new argument to spacy train, --raw-text. This takes a jsonl file with unlabelled data that can be used in arbitrary ways to do semi-supervised learning.

    Add a new method to the Language class and to pipeline components, .rehearse(). This is like .update(), but doesn't expect GoldParse objects. It takes a batch of Doc objects, and performs an update on some semi-supervised objective.

    Move the BERT-LMAO objective out from spacy/cli/pretrain.py into spacy/_ml.py, so we can create a new pipeline component, ClozeMultitask. This can be specified as a parser or NER multitask in the spacy train command. Example usage:

python -m spacy train en ./tmp ~/data/en-core-web/train/nw.json ~/data/en-core-web/dev/nw.json --pipeline parser --raw-textt ~/data/unlabelled/reddit-100k.jsonl --vectors en_vectors_web_lg --parser-multitasks cloze

Implement rehearsal methods for pipeline components

The new --raw-text argument and nlp.rehearse() method also gives us a good place to implement the the idea in the pseudo-rehearsal blog post in the parser. This works as follows:

    Add a new nlp.resume_training() method. This allocates copies of pre-trained models in the pipeline, setting things up for the rehearsal updates. It also returns an optimizer object. This also greatly reduces confusion around the nlp.begin_training() method, which randomises the weights, making it not suitable for adding new labels or otherwise fine-tuning a pre-trained model.

    Implement rehearsal updates on the Parser class, making it available for the dependency parser and NER. During rehearsal, the initial model is used to supervise the model being trained. The current model is asked to match the predictions of the initial model on some data. This minimises catastrophic forgetting, by keeping the model's predictions close to the original. See the blog post for details.

    Implement rehearsal updates for tagger

    Implement rehearsal updates for text categoriz
2018-12-10 16:25:33 +01:00
Ines Montani
f37863093a 💫 Replace ujson, msgpack and dill/pickle/cloudpickle with srsly (#3003)
Remove hacks and wrappers, keep code in sync across our libraries and move spaCy a few steps closer to only depending on packages with binary wheels 🎉

See here: https://github.com/explosion/srsly

    Serialization is hard, especially across Python versions and multiple platforms. After dealing with many subtle bugs over the years (encodings, locales, large files) our libraries like spaCy and Prodigy have steadily grown a number of utility functions to wrap the multiple serialization formats we need to support (especially json, msgpack and pickle). These wrapping functions ended up duplicated across our codebases, so we wanted to put them in one place.

    At the same time, we noticed that having a lot of small dependencies was making maintainence harder, and making installation slower. To solve this, we've made srsly standalone, by including the component packages directly within it. This way we can provide all the serialization utilities we need in a single binary wheel.

    srsly currently includes forks of the following packages:

        ujson
        msgpack
        msgpack-numpy
        cloudpickle



* WIP: replace json/ujson with srsly

* Replace ujson in examples

Use regular json instead of srsly to make code easier to read and follow

* Update requirements

* Fix imports

* Fix typos

* Replace msgpack with srsly

* Fix warning
2018-12-03 01:28:22 +01:00
Matthew Honnibal
4aa1002546 Merge branch 'develop' of https://github.com/explosion/spaCy into develop 2018-11-30 20:58:51 +00:00
Matthew Honnibal
6bd1cc57ee Increase length limit for pretrain 2018-11-30 20:58:18 +00:00
Ines Montani
37c7c85a86 💫 New JSON helpers, training data internals & CLI rewrite (#2932)
* Support nowrap setting in util.prints

* Tidy up and fix whitespace

* Simplify script and use read_jsonl helper

* Add JSON schemas (see #2928)

* Deprecate Doc.print_tree

Will be replaced with Doc.to_json, which will produce a unified format

* Add Doc.to_json() method (see #2928)

Converts Doc objects to JSON using the same unified format as the training data. Method also supports serializing selected custom attributes in the doc._. space.

* Remove outdated test

* Add write_json and write_jsonl helpers

* WIP: Update spacy train

* Tidy up spacy train

* WIP: Use wasabi for formatting

* Add GoldParse helpers for JSON format

* WIP: add debug-data command

* Fix typo

* Add missing import

* Update wasabi pin

* Add missing import

* 💫 Refactor CLI (#2943)

To be merged into #2932.

## Description
- [x] refactor CLI To use [`wasabi`](https://github.com/ines/wasabi)
- [x] use [`black`](https://github.com/ambv/black) for auto-formatting
- [x] add `flake8` config
- [x] move all messy UD-related scripts to `cli.ud`
- [x] make converters function that take the opened file and return the converted data (instead of having them handle the IO)

### Types of change
enhancement

## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [x] I have submitted the spaCy Contributor Agreement.
- [x] I ran the tests, and all new and existing tests passed.
- [x] My changes don't require a change to the documentation, or if they do, I've added all required information.

* Update wasabi pin

* Delete old test

* Update errors

* Fix typo

* Tidy up and format remaining code

* Fix formatting

* Improve formatting of messages

* Auto-format remaining code

* Add tok2vec stuff to spacy.train

* Fix typo

* Update wasabi pin

* Fix path checks for when train() is called as function

* Reformat and tidy up pretrain script

* Update argument annotations

* Raise error if model language doesn't match lang

* Document new train command
2018-11-30 20:16:14 +01:00
Matthew Honnibal
008e1ee1dd Update pretrain command 2018-11-29 12:36:43 +00:00
Matthew Honnibal
61e435610e
💫 Feature/improve pretraining (#2971)
* Improve spacy pretrain script

* Implement BERT-style 'masked language model' objective. Much better
results.

* Improve logging.

* Add length cap for documents, to avoid memory errors.

* Require thinc 7.0.0.dev1

* Require thinc 7.0.0.dev1

* Add argument for using pretrained vectors

* Fix defaults

* Fix syntax error

* Improve spacy pretrain script

* Implement BERT-style 'masked language model' objective. Much better
results.

* Improve logging.

* Add length cap for documents, to avoid memory errors.

* Require thinc 7.0.0.dev1

* Require thinc 7.0.0.dev1

* Add argument for using pretrained vectors

* Fix defaults

* Fix syntax error

* Tweak pretraining script

* Fix data limits in spacy.gold

* Fix pretrain script
2018-11-28 18:04:58 +01:00
Matthew Honnibal
2ddd428834 Fix pretrain script 2018-11-15 23:34:35 +00:00
Matthew Honnibal
f8afaa0c1c Fix pretrain 2018-11-15 22:46:53 +00:00
Matthew Honnibal
6af6950e46 Fix pretrain 2018-11-15 22:45:36 +00:00
Matthew Honnibal
3e7b214e57 Make pretrain script work with stream from stdin 2018-11-15 22:44:07 +00:00
Matthew Honnibal
8fdb9bc278
💫 Add experimental ULMFit/BERT/Elmo-like pretraining (#2931)
* Add 'spacy pretrain' command

* Fix pretrain command for Python 2

* Fix pretrain command

* Fix pretrain command
2018-11-15 22:17:16 +01:00