From bc93c34f5484bd8f1dc33df33d8d51899b035d2f Mon Sep 17 00:00:00 2001 From: Ines Montani Date: Tue, 22 Jun 2021 15:23:18 +1000 Subject: [PATCH 01/13] Add "New in v3.1" guide --- website/docs/api/entityrecognizer.md | 2 +- website/docs/usage/v3-1.md | 114 +++++++++++++++++++++++++++ website/meta/sidebars.json | 7 +- website/src/templates/index.js | 4 +- 4 files changed, 120 insertions(+), 7 deletions(-) create mode 100644 website/docs/usage/v3-1.md diff --git a/website/docs/api/entityrecognizer.md b/website/docs/api/entityrecognizer.md index b237729be..601b644c1 100644 --- a/website/docs/api/entityrecognizer.md +++ b/website/docs/api/entityrecognizer.md @@ -82,7 +82,7 @@ shortcut for this and instantiate the component using its string name and | `moves` | A list of transition names. Inferred from the data if set to `None`, which is the default. ~~Optional[List[str]]~~ | | _keyword-only_ | | | `update_with_oracle_cut_size` | During training, cut long sequences into shorter segments by creating intermediate states based on the gold-standard history. The model is not very sensitive to this parameter, so you usually won't need to change it. Defaults to `100`. ~~int~~ | -| `incorrect_spans_key` | Identifies spans that are known to be incorrect entity annotations. The incorrect entity annotations can be stored in the span group, under this key. Defaults to `None`. ~~Optional[str]~~ | +| `incorrect_spans_key` | Identifies spans that are known to be incorrect entity annotations. The incorrect entity annotations can be stored in the span group in [`Doc.spans`](/api/doc#spans), under this key. Defaults to `None`. ~~Optional[str]~~ | ## EntityRecognizer.\_\_call\_\_ {#call tag="method"} diff --git a/website/docs/usage/v3-1.md b/website/docs/usage/v3-1.md new file mode 100644 index 000000000..fb04c8e46 --- /dev/null +++ b/website/docs/usage/v3-1.md @@ -0,0 +1,114 @@ +--- +title: What's New in v3.1 +teaser: New features and how to upgrade +menu: + - ['New Features', 'features'] + - ['Upgrading Notes', 'upgrading'] +--- + +## New Features {#features hidden="true"} + + + +### Using predicted annotations during training {#predicted-annotations-training} + + + + + +This project shows how to use the `token.dep` attribute predicted by the parser +as a feature for a subsequent tagger component in the pipeline. + + + +### SpanCategorizer for predicting arbitrary and overlapping spans {#spancategorizer tag="experimental"} + +A common task in applied NLP is extracting spans of texts from documents, +including longer phrases or nested expressions. Named entity recognition isn't +the right tool for this problem, since an entity recognizer typically predicts +single token-based tags that are very sensitive to boundaries. This is effective +for proper nouns and self-contained expressions, but less useful for other types +of phrases or overlapping spans. The new +[`SpanCategorizer`](/api/spancategorizer) component and +[SpanCategorizer](/api/architectures#spancategorizer) architecture let you label +arbitrary and potentially overlapping spans of texts. A span categorizer +consists of two parts: a [suggester function](/api/spancategorizer#suggesters) +that proposes candidate spans, which may or may not overlap, and a labeler model +that predicts zero or more labels for each candidate. The predicted spans are +available via the [`Doc.spans`](/api/doc#spans) container. + + + + + + + +The upcoming version of our annotation tool [Prodigy](https://prodi.gy) +(currently available as a [pre-release](https://support.prodi.gy/t/3861) for all +users) features a [new workflow and UI](https://support.prodi.gy/t/3861) for +annotating overlapping and nested spans. You can use it to create training data +for spaCy's `SpanCategorizer` component. + + + +### Update the entity recognizer with partial incorrect annotations {#negative-samples} + +> #### config.cfg (excerpt) +> +> ```ini +> [components.ner] +> factory = "ner" +> incorrect_spans_key = "incorrect_spans" +> moves = null +> update_with_oracle_cut_size = 100 +> ``` + +The [`EntityRecognizer`](/api/entityrecognizer) can now be updated with known +incorrect annotations, which lets you take advantage of partial and sparse data. +For example, you'll be able to use the information that certain spans of text +are definitely **not** `PERSON` entities, without having to provide the +complete-gold standard annotations for the given example. The incorrect span +annotations can be added via the [`Doc.spans`](/api/doc#spans) in the training +data under the key defined as +[`incorrect_spans_key`](/api/entityrecognizer#init) in the component config. + + + +### New pipeline packages for Catalan and Danish {#pipeline-packages} + + + +| Package | Language | Tagger | Parser |  NER | +| ------------------------------------------------- | -------- | -----: | -----: | ---: | +| [`ca_core_news_sm`](/models/ca#ca_core_news_sm) | Catalan | | | | +| [`ca_core_news_md`](/models/ca#ca_core_news_md) | Catalan | | | | +| [`ca_core_news_lg`](/models/ca#ca_core_news_lg) | Catalan | | | | +| [`ca_core_news_trf`](/models/ca#ca_core_news_trf) | Catalan | | | | +| [`da_core_news_trf`](/models/da#da_core_news_trf) | Danish | | | | + +### Resizable text classification architectures {#resizable-textcat} + + + +### CLI command to assemble pipeline from config {#assemble} + +The [`spacy assemble`](/api/cli#assemble) command lets you assemble a pipeline +from a config file without additional training. It can be especially useful for +creating a blank pipeline with a custom tokenizer, rule-based components or word +vectors. + +```cli +$ python -m spacy assemble config.cfg ./output +``` + +### Support for streaming large or infinite corpora {#streaming-corpora} + + + +### New lemmatizers for Catalan and Italian {#pos-lemmatizers} + + + +## Notes about upgrading from v3.0 {#upgrading} + + diff --git a/website/meta/sidebars.json b/website/meta/sidebars.json index a7e87ff72..afa8c7e2d 100644 --- a/website/meta/sidebars.json +++ b/website/meta/sidebars.json @@ -9,7 +9,8 @@ { "text": "Models & Languages", "url": "/usage/models" }, { "text": "Facts & Figures", "url": "/usage/facts-figures" }, { "text": "spaCy 101", "url": "/usage/spacy-101" }, - { "text": "New in v3.0", "url": "/usage/v3" } + { "text": "New in v3.0", "url": "/usage/v3" }, + { "text": "New in v3.1", "url": "/usage/v3-1" } ] }, { @@ -135,9 +136,7 @@ }, { "label": "Legacy", - "items": [ - { "text": "Legacy functions", "url": "/api/legacy" } - ] + "items": [{ "text": "Legacy functions", "url": "/api/legacy" }] } ] } diff --git a/website/src/templates/index.js b/website/src/templates/index.js index a5adc6e50..2c68ff056 100644 --- a/website/src/templates/index.js +++ b/website/src/templates/index.js @@ -119,8 +119,8 @@ const AlertSpace = ({ nightly, legacy }) => { } const navAlert = ( - - 💥 Out now: spaCy v3.0 + + 💥 Out now: spaCy v3.1 ) From e9b68d4f4c02285bc83625101ea60d0a74d7c7cb Mon Sep 17 00:00:00 2001 From: Ines Montani Date: Tue, 22 Jun 2021 17:51:03 +1000 Subject: [PATCH 02/13] Update details and add example [ci skip] --- website/docs/usage/v3-1.md | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/website/docs/usage/v3-1.md b/website/docs/usage/v3-1.md index fb04c8e46..56a25c87a 100644 --- a/website/docs/usage/v3-1.md +++ b/website/docs/usage/v3-1.md @@ -72,11 +72,27 @@ annotations can be added via the [`Doc.spans`](/api/doc#spans) in the training data under the key defined as [`incorrect_spans_key`](/api/entityrecognizer#init) in the component config. +```python +train_doc = nlp.make_doc("Barack Obama was born in Hawaii.") +# The doc.spans key can be defined in the config +train_doc.spans["incorrect_spans"] = [ + Span(doc, 0, 2, label="ORG"), + Span(doc, 5, 6, label="PRODUCT") +] +``` + ### New pipeline packages for Catalan and Danish {#pipeline-packages} - +spaCy v3.1 adds 5 new pipeline packages, including a new core family for Catalan +and a new transformer-based pipeline for Danish using the +[`danish-bert-botxo`](http://huggingface.co/Maltehb/danish-bert-botxo) weights. +See the [models directory](/models) for an overview of all available trained +pipelines and the [training guide](/usage/training) for details on how to train +your own. + + | Package | Language | Tagger | Parser |  NER | | ------------------------------------------------- | -------- | -----: | -----: | ---: | From ca0d904faa9e8702d04c318335a0825825aeb962 Mon Sep 17 00:00:00 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pipeline. +The new +[`[training.annotating_components]`](/usage/training#annotating-components) +config setting lets you specify pipeline component names that should set +annotations on the predicted docs during training. This makes it easy to use the +predictions of a previous component in the pipeline as features for a subsequent +component, e.g. the dependency labels in the tagger: + +```ini +### config.cfg (excerpt) {highlight="7,12"} +[nlp] +pipeline = ["parser", "tagger"] + +[components.tagger.model.tok2vec.embed] +@architectures = "spacy.MultiHashEmbed.v1" +width = ${components.tagger.model.tok2vec.encode.width} +attrs = ["NORM","DEP"] +rows = [5000,2500] +include_static_vectors = false + +[training] +annotating_components = ["parser"] +``` @@ -41,7 +64,7 @@ available via the [`Doc.spans`](/api/doc#spans) container. - +[![Prodigy: example of the new manual spans UI](../images/prodigy_spans-manual.jpg)](https://support.prodi.gy/t/3861) The upcoming version of our annotation tool [Prodigy](https://prodi.gy) (currently available as a [pre-release](https://support.prodi.gy/t/3861) for all @@ -66,11 +89,11 @@ for spaCy's `SpanCategorizer` component. The [`EntityRecognizer`](/api/entityrecognizer) can now be updated with known incorrect annotations, which lets you take advantage of partial and sparse data. For example, you'll be able to use the information that certain spans of text -are definitely **not** `PERSON` entities, without having to provide the -complete-gold standard annotations for the given example. The incorrect span -annotations can be added via the [`Doc.spans`](/api/doc#spans) in the training -data under the key defined as -[`incorrect_spans_key`](/api/entityrecognizer#init) in the component config. +are definitely **not** `PERSON` entities, without having to provide the complete +gold-standard annotations for the given example. The incorrect span annotations +can be added via the [`Doc.spans`](/api/doc#spans) in the training data under +the key defined as [`incorrect_spans_key`](/api/entityrecognizer#init) in the +component config. ```python train_doc = nlp.make_doc("Barack Obama was born in Hawaii.") @@ -104,7 +127,12 @@ your own. ### Resizable text classification architectures {#resizable-textcat} - +Previously, a trained [`TextCategorizer`](/api/textcategorizer) architectures +could not be resized, meaning that you couldn't add new labels to an already +trained text classifier. In spaCy v3.1, the +[TextCatCNN](/api/architectures#TextCatCNN) and +[TextCatBOW](/api/architectures#TextCatBOW) architectures are now resizable, +while ensuring that the predictions for the old labels remain the same. ### CLI command to assemble pipeline from config {#assemble} @@ -119,11 +147,39 @@ $ python -m spacy assemble config.cfg ./output ### Support for streaming large or infinite corpora {#streaming-corpora} - +> #### config.cfg (excerpt) +> +> ```ini +> [training] +> max_epochs = -1 +> ``` + +The training process now supports streaming large or infinite corpora +out-of-the-box, which can be controlled via the +[`[training.max_epochs]`](/api/data-formats#training) config setting. Setting it +to `-1` means that the train corpus should be streamed rather than loaded into +memory with no shuffling within the training loop. For details on how to +implement a custom corpus loader, e.g. to stream in data from a remote storage, +see the usage guide on +[custom data reading](/usage/training#custom-code-readers-batchers). + +When streaming a corpus, only the first 100 examples will be used for +[initialization](/usage/training#config-lifecycle). This is no problem if you're +training a component like the text classifier with data that specifies all +available labels in every example. If necessary, you can use the +[`init labels`](/api/cli#init-labels) command to pre-generate the labels for +your components using a representative sample so the model can be initialized +correctly before training. ### New lemmatizers for Catalan and Italian {#pos-lemmatizers} - +The trained pipelines for [Catalan](/models/ca) and [Italian](/models/it) now +include lemmatizers that use the predicted part-of-speech tags as part of the +lookup lemmatization for higher lemmatization accuracy. If you're training your +own pipelines for these languages and you want to include a lemmatizer, make +sure you have the +[`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) package +installed, which provides the relevant tables. ## Notes about upgrading from v3.0 {#upgrading} From a1e4aca267ecc175184bd9d70bbee051d798f168 Mon Sep 17 00:00:00 2001 From: Ines Montani Date: Thu, 24 Jun 2021 11:40:36 +1000 Subject: [PATCH 04/13] Fix sentence [ci skip] --- website/docs/usage/v3-1.md | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/website/docs/usage/v3-1.md b/website/docs/usage/v3-1.md index 6a02b5e4c..9ebb8b094 100644 --- a/website/docs/usage/v3-1.md +++ b/website/docs/usage/v3-1.md @@ -127,10 +127,9 @@ your own. ### Resizable text classification architectures {#resizable-textcat} -Previously, a trained [`TextCategorizer`](/api/textcategorizer) architectures -could not be resized, meaning that you couldn't add new labels to an already -trained text classifier. In spaCy v3.1, the -[TextCatCNN](/api/architectures#TextCatCNN) and +Previously, the [`TextCategorizer`](/api/textcategorizer) architectures could +not be resized, meaning that you couldn't add new labels to an already trained +model. In spaCy v3.1, the [TextCatCNN](/api/architectures#TextCatCNN) and [TextCatBOW](/api/architectures#TextCatBOW) architectures are now resizable, while ensuring that the predictions for the old labels remain the same. From 3e058dee62a09dd61f788739a7f5f6218a095d61 Mon Sep 17 00:00:00 2001 From: Ines Montani Date: Thu, 24 Jun 2021 12:36:04 +1000 Subject: [PATCH 05/13] Update features [ci skip] --- website/docs/usage/v3-1.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/website/docs/usage/v3-1.md b/website/docs/usage/v3-1.md index 9ebb8b094..37cec8803 100644 --- a/website/docs/usage/v3-1.md +++ b/website/docs/usage/v3-1.md @@ -144,6 +144,14 @@ vectors. $ python -m spacy assemble config.cfg ./output ``` +### Pretty pipeline package READMEs {#package-readme} + +The [`spacy package`](/api/cli#package) command now auto-generates a pretty +`README.md` based on the pipeline information defined in the `meta.json`. This +includes a table with a general overview, as well as the label scheme and +accuracy figures, if available. For an example, see the +[model releases](https://github.com/explosion/spacy-models/releases). + ### Support for streaming large or infinite corpora {#streaming-corpora} > #### config.cfg (excerpt) From 5daf450f51e02d0ae7fa3624e30cbe7ba925e629 Mon Sep 17 00:00:00 2001 From: Ines Montani Date: Thu, 24 Jun 2021 18:06:28 +1000 Subject: [PATCH 06/13] Update upgrading notes [ci skip] --- website/docs/usage/v3-1.md | 29 ++++++++++++++++++++++++++++- 1 file changed, 28 insertions(+), 1 deletion(-) diff --git a/website/docs/usage/v3-1.md b/website/docs/usage/v3-1.md index 37cec8803..a00ecb10e 100644 --- a/website/docs/usage/v3-1.md +++ b/website/docs/usage/v3-1.md @@ -190,4 +190,31 @@ installed, which provides the relevant tables. ## Notes about upgrading from v3.0 {#upgrading} - +### Pipeline package version compatibility {#version-compat} + +> #### Using legacy implementations +> +> In spaCy v3, you'll still be able to load and reference legacy implementations +> via [`spacy-legacy`](https://github.com/explosion/spacy-legacy), even if the +> components or architectures change and newer versions are available in the +> core library. + +When you're loading a pipeline package trained with spaCy v3.0, you will see a +warning telling you that the pipeline may be incompatible. This doesn't +necessarily have to be true, but we recommend running your pipelines against +your test suite or evaluation data to make sure there are no unexpected results. +If you're using one of the [trained pipelines](/models) we provide, you should +run [`spacy download`](/api/cli#download) to update to the latest version. To +see an overview of all installed packages and their compatibility, you can run +[`spacy validate`](/api/cli#validate). + +If you've trained your own custom pipeline and you've confirmed that it's still +working as expected, you can update the spaCy version requirements in the +[`meta.json`](/api/data-formats#meta): + +```diff +- "spacy_version": ">=3.0.0,<3.1.0", ++ "spacy_version": ">=3.0.0,<3.2.0", +``` + + From 35425d7e261763ea4bd2546c42fda692cf81ca91 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Thu, 24 Jun 2021 10:03:43 +0200 Subject: [PATCH 07/13] Add details for Catalan and Danish --- website/docs/usage/v3-1.md | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/website/docs/usage/v3-1.md b/website/docs/usage/v3-1.md index a00ecb10e..fe17773c2 100644 --- a/website/docs/usage/v3-1.md +++ b/website/docs/usage/v3-1.md @@ -115,15 +115,18 @@ See the [models directory](/models) for an overview of all available trained pipelines and the [training guide](/usage/training) for details on how to train your own. - +Thanks to Carlos Rodríguez Penagos and the +[Barcelona Supercomputing Center](https://temu.bsc.es/) for their contributions +for Catalan and to Kenneth Enevoldsen for Danish. For additional Danish +pipelines, check out [DaCy](https://github.com/KennethEnevoldsen/DaCy). -| Package | Language | Tagger | Parser |  NER | -| ------------------------------------------------- | -------- | -----: | -----: | ---: | -| [`ca_core_news_sm`](/models/ca#ca_core_news_sm) | Catalan | | | | -| [`ca_core_news_md`](/models/ca#ca_core_news_md) | Catalan | | | | -| [`ca_core_news_lg`](/models/ca#ca_core_news_lg) | Catalan | | | | -| [`ca_core_news_trf`](/models/ca#ca_core_news_trf) | Catalan | | | | -| [`da_core_news_trf`](/models/da#da_core_news_trf) | Danish | | | | +| Package | Language | UPOS | Parser LAS |  NER F | +| ------------------------------------------------- | -------- | ---: | ---------: | -----: | +| [`ca_core_news_sm`](/models/ca#ca_core_news_sm) | Catalan | 98.2 | 87.4 | 79.8 | +| [`ca_core_news_md`](/models/ca#ca_core_news_md) | Catalan | 98.3 | 88.2 | 84.0 | +| [`ca_core_news_lg`](/models/ca#ca_core_news_lg) | Catalan | 98.5 | 88.4 | 84.2 | +| [`ca_core_news_trf`](/models/ca#ca_core_news_trf) | Catalan | 98.9 | 93.0 | 91.2 | +| [`da_core_news_trf`](/models/da#da_core_news_trf) | Danish | 98.0 | 85.0 | 82.9 | ### Resizable text classification architectures {#resizable-textcat} From 92dc6b409ef7a7483895455b7836883195c89c1c Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Thu, 24 Jun 2021 10:34:07 +0200 Subject: [PATCH 08/13] Notes on source with vectors --- website/docs/usage/v3-1.md | 32 +++++++++++++++++++++++++++++++- 1 file changed, 31 insertions(+), 1 deletion(-) diff --git a/website/docs/usage/v3-1.md b/website/docs/usage/v3-1.md index fe17773c2..c6bfb0490 100644 --- a/website/docs/usage/v3-1.md +++ b/website/docs/usage/v3-1.md @@ -220,4 +220,34 @@ working as expected, you can update the spaCy version requirements in the + "spacy_version": ">=3.0.0,<3.2.0", ``` - +### Sourcing pipeline components with vectors {#source-vectors} + +If you're sourcing a pipeline component that requires static vectors (for +example, a tagger or parser from an `md` or `lg` pretrained pipeline), be sure +to include the source model's vectors in the setting `[initialize.vectors]`. In +spaCy v3.0, a bug allowed vectors to be loaded implicitly through `source`, +however in v3.1 this setting must be provided explicitly as +`[initialize.vectors]`: + +```ini +### config.cfg (excerpt) +[components.ner] +source = "en_core_web_md" + +[initialize] +vectors = "en_core_web_md" +``` + + + +Each pipeline can only store one set of static vectors, so it's not possible to +assemble a pipeline with components that were trained on different static +vectors. + + + +[`spacy train`](/api/cli#train) and [`spacy assemble`](/api/cli#assemble) will +provide warnings if the source and target pipelines don't contain the same +vectors. If you are sourcing a rule-based component like an entity ruler or +lemmatizer that does not use the vectors as a model feature, then this warning +can be safely ignored. From 68721af6283dc3b72b95377212ad95349c673ff5 Mon Sep 17 00:00:00 2001 From: Ines Montani Date: Thu, 24 Jun 2021 20:32:23 +1000 Subject: [PATCH 09/13] Formatting and preliminary intro [ci skip] --- website/docs/usage/v3-1.md | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/website/docs/usage/v3-1.md b/website/docs/usage/v3-1.md index c6bfb0490..4b8868df7 100644 --- a/website/docs/usage/v3-1.md +++ b/website/docs/usage/v3-1.md @@ -8,7 +8,16 @@ menu: ## New Features {#features hidden="true"} - +It's been great to see the adoption of the new spaCy v3, which introduced +[transformer-based](/usage/embeddings-transformers) pipelines, a new +[config and training system](/usage/training) for reproducible experiments, a +new [projects system](/usage/projects) for end-to-end workflows, and many +[other features](/usage/v3). Version 3.1 adds a bunch of new features on top of +it, including the ability to use predicted annotations during training, a new +`SpanCategorizer` component for predicting arbitrary and potentially overlapping +spans, support for partial incorrect annotations in the entity recognizer, new +trained pipelines for Catalan and Danish, as well as many other bug fixes and +improvements. ### Using predicted annotations during training {#predicted-annotations-training} @@ -115,10 +124,10 @@ See the [models directory](/models) for an overview of all available trained pipelines and the [training guide](/usage/training) for details on how to train your own. -Thanks to Carlos Rodríguez Penagos and the -[Barcelona Supercomputing Center](https://temu.bsc.es/) for their contributions -for Catalan and to Kenneth Enevoldsen for Danish. For additional Danish -pipelines, check out [DaCy](https://github.com/KennethEnevoldsen/DaCy). +> Thanks to Carlos Rodríguez Penagos and the +> [Barcelona Supercomputing Center](https://temu.bsc.es/) for their +> contributions for Catalan and to Kenneth Enevoldsen for Danish. For additional +> Danish pipelines, check out [DaCy](https://github.com/KennethEnevoldsen/DaCy). | Package | Language | UPOS | Parser LAS |  NER F | | ------------------------------------------------- | -------- | ---: | ---------: | -----: | From 0d2e2b59bc8d7224b2c6d0a02bad43064467dfa4 Mon Sep 17 00:00:00 2001 From: Ines Montani Date: Thu, 24 Jun 2021 22:53:20 +1000 Subject: [PATCH 10/13] Update intro [ci skip] --- website/docs/usage/v3-1.md | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/website/docs/usage/v3-1.md b/website/docs/usage/v3-1.md index 4b8868df7..72fbac1f5 100644 --- a/website/docs/usage/v3-1.md +++ b/website/docs/usage/v3-1.md @@ -10,14 +10,13 @@ menu: It's been great to see the adoption of the new spaCy v3, which introduced [transformer-based](/usage/embeddings-transformers) pipelines, a new -[config and training system](/usage/training) for reproducible experiments, a -new [projects system](/usage/projects) for end-to-end workflows, and many -[other features](/usage/v3). Version 3.1 adds a bunch of new features on top of -it, including the ability to use predicted annotations during training, a new -`SpanCategorizer` component for predicting arbitrary and potentially overlapping -spans, support for partial incorrect annotations in the entity recognizer, new -trained pipelines for Catalan and Danish, as well as many other bug fixes and -improvements. +[config and training system](/usage/training) for reproducible experiments, +[projects](/usage/projects) for end-to-end workflows, and many +[other features](/usage/v3). Version 3.1 adds more on top of it, including the +ability to use predicted annotations during training, a new `SpanCategorizer` +component for predicting arbitrary and potentially overlapping spans, support +for partial incorrect annotations in the entity recognizer, new trained +pipelines for Catalan and Danish, as well as many bug fixes and improvements. ### Using predicted annotations during training {#predicted-annotations-training} From 454441244238408e4c9550d3fa3bcf69fc93c9ad Mon Sep 17 00:00:00 2001 From: Ines Montani Date: Fri, 25 Jun 2021 13:52:48 +1000 Subject: [PATCH 11/13] Update wording [ci skip] Co-authored-by: Sofie Van Landeghem --- website/docs/usage/v3-1.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/docs/usage/v3-1.md b/website/docs/usage/v3-1.md index 72fbac1f5..76cbf224e 100644 --- a/website/docs/usage/v3-1.md +++ b/website/docs/usage/v3-1.md @@ -24,7 +24,7 @@ By default, components are updated in isolation during training, which means that they don't see the predictions of any earlier components in the pipeline. The new [`[training.annotating_components]`](/usage/training#annotating-components) -config setting lets you specify pipeline component names that should set +config setting lets you specify pipeline components that should set annotations on the predicted docs during training. This makes it easy to use the predictions of a previous component in the pipeline as features for a subsequent component, e.g. the dependency labels in the tagger: From 41292a1b847f64179bc97c8e13543f3a75110485 Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Tue, 29 Jun 2021 10:45:36 +0200 Subject: [PATCH 12/13] Add note about updating with fill-config --- website/docs/usage/v3-1.md | 21 +++++++++++++++++---- 1 file changed, 17 insertions(+), 4 deletions(-) diff --git a/website/docs/usage/v3-1.md b/website/docs/usage/v3-1.md index 76cbf224e..5e3181b36 100644 --- a/website/docs/usage/v3-1.md +++ b/website/docs/usage/v3-1.md @@ -24,10 +24,10 @@ By default, components are updated in isolation during training, which means that they don't see the predictions of any earlier components in the pipeline. The new [`[training.annotating_components]`](/usage/training#annotating-components) -config setting lets you specify pipeline components that should set -annotations on the predicted docs during training. This makes it easy to use the -predictions of a previous component in the pipeline as features for a subsequent -component, e.g. the dependency labels in the tagger: +config setting lets you specify pipeline components that should set annotations +on the predicted docs during training. This makes it easy to use the predictions +of a previous component in the pipeline as features for a subsequent component, +e.g. the dependency labels in the tagger: ```ini ### config.cfg (excerpt) {highlight="7,12"} @@ -228,6 +228,19 @@ working as expected, you can update the spaCy version requirements in the + "spacy_version": ">=3.0.0,<3.2.0", ``` +### Updating v3.0 configs + +To update a config from spaCy v3.0 with the new v3.1 settings, run +[`init fill-config`](/api/cli#init-fill-config): + +```bash +python -m spacy init fill-config config-v3.0.cfg config-v3.1.cfg +``` + +In many cases (`spacy train`, `spacy.load()`), the new defaults will be filled +in automatically, but you'll need to fill in the new settings to run +[`debug config`](/api/cli#debug) and [`debug data`](/api/cli#debug-data). + ### Sourcing pipeline components with vectors {#source-vectors} If you're sourcing a pipeline component that requires static vectors (for From 5bb7fe4b4197160b4e9e6a1fc1346961f85dff73 Mon Sep 17 00:00:00 2001 From: Ines Montani Date: Tue, 6 Jul 2021 19:30:59 +1000 Subject: [PATCH 13/13] Update with HF hub integration [ci skip] --- website/docs/api/cli.md | 47 +++++++++++++ website/docs/images/huggingface_hub.jpg | Bin 0 -> 112649 bytes website/docs/usage/projects.md | 66 +++++++++++++++++++ website/docs/usage/v3-1.md | 35 ++++++++++ website/src/components/code.js | 2 +- website/src/images/logos/huggingface_hub.svg | 66 +++++++++++++++++++ website/src/widgets/integration.js | 2 + 7 files changed, 217 insertions(+), 1 deletion(-) create mode 100644 website/docs/images/huggingface_hub.jpg create mode 100644 website/src/images/logos/huggingface_hub.svg diff --git a/website/docs/api/cli.md b/website/docs/api/cli.md index 685f998ff..62e1b22e6 100644 --- a/website/docs/api/cli.md +++ b/website/docs/api/cli.md @@ -16,6 +16,7 @@ menu: - ['package', 'package'] - ['project', 'project'] - ['ray', 'ray'] + - ['huggingface-hub', 'huggingface-hub'] --- spaCy's CLI provides a range of helpful commands for downloading and training @@ -1273,3 +1274,49 @@ $ python -m spacy ray train [config_path] [--code] [--output] [--n-workers] [--a | `--verbose`, `-V` | Display more information for debugging purposes. ~~bool (flag)~~ | | `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ | | overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.train ./train.spacy`. ~~Any (option/flag)~~ | + +## huggingface-hub {#huggingface-hub new="3.1"} + +The `spacy huggingface-cli` CLI includes commands for uploading your trained +spaCy pipelines to the [Hugging Face Hub](https://huggingface.co/). + +> #### Installation +> +> ```cli +> $ pip install spacy-huggingface-hub +> $ huggingface-cli login +> ``` + + + +To use this command, you need the +[`spacy-huggingface-hub`](https://github.com/explosion/spacy-huggingface-hub) +package installed. Installing the package will automatically add the +`huggingface-hub` command to the spaCy CLI. + + + +### huggingface-hub push {#huggingface-hub-push tag="command"} + +Push a spaCy pipeline to the Hugging Face Hub. Expects a `.whl` file packaged +with [`spacy package`](/api/cli#package) and `--build wheel`. For more details, +see the spaCy project [integration](/usage/projects#huggingface_hub). + +```cli +$ python -m spacy huggingface-hub push [whl_path] [--org] [--msg] [--local-repo] [--verbose] +``` + +> #### Example +> +> ```cli +> $ python -m spacy huggingface-hub push en_ner_fashion-0.0.0-py3-none-any.whl +> ``` + +| Name | Description | +| -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------- | +| `whl_path` | The path to the `.whl` file packaged with [`spacy package`](https://spacy.io/api/cli#package). ~~Path(positional)~~ | +| `--org`, `-o` | Optional name of organization to which the pipeline should be uploaded. ~~str (option)~~ | +| `--msg`, `-m` | Commit message to use for update. Defaults to `"Update spaCy pipeline"`. ~~str (option)~~ | +| `--local-repo`, `-l` | Local path to the model repository (will be created if it doesn't exist). Defaults to `hub` in the current working directory. ~~Path (option)~~ | +| `--verbose`, `-V` | Output additional info for debugging, e.g. the full generated hub metadata. ~~bool (flag)~~  | +| **UPLOADS** | The pipeline to the hub. | diff --git a/website/docs/images/huggingface_hub.jpg b/website/docs/images/huggingface_hub.jpg new file mode 100644 index 0000000000000000000000000000000000000000..5618df020a70d26fda0f744623ad71414f5e2a89 GIT binary patch literal 112649 zcmeFZcU%+O*DpLFbi_zk5Q3n9NU_kQBq9O=2SHF!S`H#5M2Iwr5)vy_;MhPxh=>RX zh)PogBK=tDARv$g6=^b2%1GKBJmvSh&wc*5@BO^*=ic|ZXD~A>d)8iit+n^;{at%a z6ucKOfOU>`4t4+n0RX?je}I4qoV6w4g8;zU8PEj)KpYT3$O1^1Lco6jLKzVGNdtf* zLgjDT4WaWVj}QPP;sN15dA#83>H$k$t^Ma!=#J1|9LSG%kblvL$W>MWQ({v{bo5Dc zL&NYW1OMa1V}S+%#4tmW|4G9g2HOn*ODyT6e*hscTK!mHFh1N$WBxf^LmhwIO2gCG zdAsvTo4^qK{xgw*?q?1h3OGXu*nM0Bi&3{EnUlg!h6P6ZtCPY`gh!c^tTcWKH;3uf zVnYq}pDfV?D~%)0uIe_#$Ut=ygY5>}HQ?Hj$Aiq>_SyYe7yf3Y@u!lpv9SiRy9|ht 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Serve your models and host APIs Distributed and parallel training Track your experiments and results +Upload your pipelines to the Hugging Face Hub ### 1. Clone a project template {#clone} @@ -1013,3 +1014,68 @@ creating variants of the config for a simple hyperparameter grid search and logging the results. + +--- + +### Hugging Face Hub {#huggingface_hub} + +The [Hugging Face Hub](https://huggingface.co/) lets you upload models and share +them with others. It hosts models as Git-based repositories which are storage +spaces that can contain all your files. It support versioning, branches and +custom metadata out-of-the-box, and provides browser-based visualizers for +exploring your models interactively, as well as an API for production use. The +[`spacy-huggingface-hub`](https://github.com/explosion/spacy-huggingface-hub) +package automatically adds the `huggingface-hub` command to your `spacy` CLI if +it's installed. + +> #### Installation +> +> ```cli +> $ pip install spacy-huggingface-hub +> # Check that the CLI is registered +> $ python -m spacy huggingface-hub --help +> ``` + +You can then upload any pipeline packaged with +[`spacy package`](/api/cli#package). Make sure to set `--build wheel` to output +a binary `.whl` file. The uploader will read all metadata from the pipeline +package, including the auto-generated pretty `README.md` and the model details +available in the `meta.json`. For examples, check out the +[spaCy pipelines](https://huggingface.co/spacy) we've uploaded. + +```cli +$ huggingface-cli login +$ python -m spacy package ./en_ner_fashion ./output --build wheel +$ cd ./output/en_ner_fashion-0.0.0/dist +$ python -m spacy huggingface-hub push en_ner_fashion-0.0.0-py3-none-any.whl +``` + +After uploading, you will see the live URL of your pipeline packages, as well as +the direct URL to the model wheel you can install via `pip install`. You'll also +be able to test your pipeline interactively from your browser: + +![Screenshot: interactive NER visualizer](../images/huggingface_hub.jpg) + +In your `project.yml`, you can add a command that uploads your trained and +packaged pipeline to the hub. You can either run this as a manual step, or +automatically as part of a workflow. Make sure to set `--build wheel` when +running `spacy package` to build a wheel file for your pipeline package. + + +```yaml +### project.yml +- name: "push_to_hub" + help: "Upload the trained model to the Hugging Face Hub" + script: + - "python -m spacy huggingface-hub push packages/en_${vars.name}-${vars.version}/dist/en_${vars.name}-${vars.version}-py3-none-any.whl" + deps: + - "packages/en_${vars.name}-${vars.version}/dist/en_${vars.name}-${vars.version}-py3-none-any.whl" +``` + + + +Get started with uploading your models to the Hugging Face hub using our project +template. It trains a simple pipeline, packages it and uploads it if the +packaged model has changed. This makes it easy to deploy your models end-to-end. + + diff --git a/website/docs/usage/v3-1.md b/website/docs/usage/v3-1.md index 5e3181b36..da6fa6070 100644 --- a/website/docs/usage/v3-1.md +++ b/website/docs/usage/v3-1.md @@ -199,6 +199,41 @@ sure you have the [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) package installed, which provides the relevant tables. +### Upload your pipelines to the Hugging Face Hub {#huggingface-hub} + +The [Hugging Face Hub](https://huggingface.co/) lets you upload models and share +them with others, and it now supports spaCy pipelines out-of-the-box. The new +[`spacy-huggingface-hub`](https://github.com/explosion/spacy-huggingface-hub) +package automatically adds the `huggingface-hub` command to your `spacy` CLI. It +lets you upload any pipelines packaged with [`spacy package`](/api/cli#package) +and `--build wheel` and takes care of auto-generating all required meta +information. + +After uploading, you'll get a live URL for your model page that includes all +details, files and interactive visualizers, as well as a direct URL to the wheel +file that you can install via `pip install`. For examples, check out the +[spaCy pipelines](https://huggingface.co/spacy) we've uploaded. + +```cli +$ pip install spacy-huggingface-hub +$ huggingface-cli login +$ python -m spacy package ./en_ner_fashion ./output --build wheel +$ cd ./output/en_ner_fashion-0.0.0/dist +$ python -m spacy huggingface-hub push en_ner_fashion-0.0.0-py3-none-any.whl +``` + +You can also integrate the upload command into your +[project template](/usage/projects#huggingface_hub) to automatically upload your +packaged pipelines after training. + + + +Get started with uploading your models to the Hugging Face hub using our project +template. It trains a simple pipeline, packages it and uploads it if the +packaged model has changed. This makes it easy to deploy your models end-to-end. + + + ## Notes about upgrading from v3.0 {#upgrading} ### Pipeline package version compatibility {#version-compat} diff --git a/website/src/components/code.js b/website/src/components/code.js index 4dd7a8eb8..6e9f0c22e 100644 --- a/website/src/components/code.js +++ b/website/src/components/code.js @@ -14,7 +14,7 @@ import GitHubCode from './github' import classes from '../styles/code.module.sass' const WRAP_THRESHOLD = 30 -const CLI_GROUPS = ['init', 'debug', 'project', 'ray'] +const CLI_GROUPS = ['init', 'debug', 'project', 'ray', 'huggingface-hub'] export default props => (

diff --git a/website/src/images/logos/huggingface_hub.svg b/website/src/images/logos/huggingface_hub.svg
new file mode 100644
index 000000000..582e89e0d
--- /dev/null
+++ b/website/src/images/logos/huggingface_hub.svg
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diff --git a/website/src/widgets/integration.js b/website/src/widgets/integration.js
index 0de078e0b..cf2320052 100644
--- a/website/src/widgets/integration.js
+++ b/website/src/widgets/integration.js
@@ -8,6 +8,7 @@ import StreamlitLogo from '-!svg-react-loader!../images/logos/streamlit.svg'
 import FastAPILogo from '-!svg-react-loader!../images/logos/fastapi.svg'
 import WandBLogo from '-!svg-react-loader!../images/logos/wandb.svg'
 import RayLogo from '-!svg-react-loader!../images/logos/ray.svg'
+import HuggingFaceHubLogo from '-!svg-react-loader!../images/logos/huggingface_hub.svg'
 
 const LOGOS = {
     dvc: DVCLogo,
@@ -16,6 +17,7 @@ const LOGOS = {
     fastapi: FastAPILogo,
     wandb: WandBLogo,
     ray: RayLogo,
+    huggingface_hub: HuggingFaceHubLogo,
 }
 
 export const IntegrationLogo = ({ name, title, width, height, maxWidth, align, ...props }) => {