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+/spacy-experimental-0.6.2.tar.gz
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+%global _empty_manifest_terminate_build 0
+Name:		python-spacy-experimental
+Version:	0.6.2
+Release:	1
+Summary:	Cutting-edge experimental spaCy components and features
+License:	MIT
+URL:		https://github.com/explosion/spacy-experimental
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/20/8c/b02147635d4d398e7a0b70be847bacac57123f52e74366e7105846caf67b/spacy-experimental-0.6.2.tar.gz
+
+Requires:	python3-spacy
+Requires:	python3-torch
+
+%description
+<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
+
+# spacy-experimental: Cutting-edge experimental spaCy components and features
+
+This package includes experimental components and features for
+[spaCy](https://spacy.io) v3.x, for example model architectures, pipeline
+components and utilities.
+
+[![Azure Pipelines](https://img.shields.io/azure-devops/build/explosion-ai/public/26/master.svg?logo=azure-pipelines&style=flat-square&label=build)](https://dev.azure.com/explosion-ai/public/_build?definitionId=26)
+[![pypi Version](https://img.shields.io/pypi/v/spacy-experimental.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.org/project/spacy-experimental/)
+
+## Installation
+
+Install with `pip`:
+
+```bash
+python -m pip install -U pip setuptools wheel
+python -m pip install spacy-experimental
+```
+
+## Using spacy-experimental
+
+Components and features may be modified or removed in any release, so always
+specify the exact version as a package requirement if you're experimenting with
+a particular component, e.g.:
+
+```
+spacy-experimental==0.147.0
+```
+
+Then you can add the experimental components to your config or import from
+`spacy_experimental`:
+
+```ini
+[components.experimental_char_ner_tokenizer]
+factory = "experimental_char_ner_tokenizer"
+```
+
+## Components
+
+### Trainable character-based tokenizers
+
+Two trainable tokenizers represent tokenization as a sequence tagging problem
+over individual characters and use the existing spaCy tagger and NER
+architectures to perform the tagging.
+
+In the spaCy pipeline, a simple "pretokenizer" is applied as the pipeline
+tokenizer to split each doc into individual characters and the trainable
+tokenizer is a pipeline component that retokenizes the doc. The pretokenizer
+needs to be configured manually in the config or with `spacy.blank()`:
+
+```python
+nlp = spacy.blank(
+    "en",
+    config={
+        "nlp": {
+            "tokenizer": {"@tokenizers": "spacy-experimental.char_pretokenizer.v1"}
+        }
+    },
+)
+```
+
+The two tokenizers currently reset any existing tag or entity annotation
+respectively in the process of retokenizing.
+
+#### Character-based tagger tokenizer
+
+In the tagger version `experimental_char_tagger_tokenizer`, the tagging problem
+is represented internally with character-level tags for token start (`T`),
+token internal (`I`), and outside a token (`O`). This representation comes from
+[Elephant: Sequence Labeling for Word and Sentence
+Segmentation](https://aclanthology.org/D13-1146/) (Evang et al., 2013).
+
+```none
+This is a sentence.
+TIIIOTIOTOTIIIIIIIT
+```
+
+With the option `annotate_sents`, `S` replaces `T` for the first token in each
+sentence and the component predicts both token and sentence boundaries.
+
+```none
+This is a sentence.
+SIIIOTIOTOTIIIIIIIT
+```
+
+A config excerpt for `experimental_char_tagger_tokenizer`:
+
+```ini
+[nlp]
+pipeline = ["experimental_char_tagger_tokenizer"]
+tokenizer = {"@tokenizers":"spacy-experimental.char_pretokenizer.v1"}
+
+[components]
+
+[components.experimental_char_tagger_tokenizer]
+factory = "experimental_char_tagger_tokenizer"
+annotate_sents = true
+scorer = {"@scorers":"spacy-experimental.tokenizer_senter_scorer.v1"}
+
+[components.experimental_char_tagger_tokenizer.model]
+@architectures = "spacy.Tagger.v1"
+nO = null
+
+[components.experimental_char_tagger_tokenizer.model.tok2vec]
+@architectures = "spacy.Tok2Vec.v2"
+
+[components.experimental_char_tagger_tokenizer.model.tok2vec.embed]
+@architectures = "spacy.MultiHashEmbed.v2"
+width = 128
+attrs = ["ORTH","LOWER","IS_DIGIT","IS_ALPHA","IS_SPACE","IS_PUNCT"]
+rows = [1000,500,50,50,50,50]
+include_static_vectors = false
+
+[components.experimental_char_tagger_tokenizer.model.tok2vec.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 128
+depth = 4
+window_size = 4
+maxout_pieces = 2
+```
+
+#### Character-based NER tokenizer
+
+In the NER version, each character in a token is part of an entity:
+
+```none
+T	B-TOKEN
+h	I-TOKEN
+i	I-TOKEN
+s	I-TOKEN
+ 	O
+i	B-TOKEN
+s	I-TOKEN
+	O
+a	B-TOKEN
+ 	O
+s	B-TOKEN
+e	I-TOKEN
+n	I-TOKEN
+t	I-TOKEN
+e	I-TOKEN
+n	I-TOKEN
+c	I-TOKEN
+e	I-TOKEN
+.	B-TOKEN
+```
+
+A config excerpt for `experimental_char_ner_tokenizer`:
+
+```ini
+[nlp]
+pipeline = ["experimental_char_ner_tokenizer"]
+tokenizer = {"@tokenizers":"spacy-experimental.char_pretokenizer.v1"}
+
+[components]
+
+[components.experimental_char_ner_tokenizer]
+factory = "experimental_char_ner_tokenizer"
+scorer = {"@scorers":"spacy-experimental.tokenizer_scorer.v1"}
+
+[components.experimental_char_ner_tokenizer.model]
+@architectures = "spacy.TransitionBasedParser.v2"
+state_type = "ner"
+extra_state_tokens = false
+hidden_width = 64
+maxout_pieces = 2
+use_upper = true
+nO = null
+
+[components.experimental_char_ner_tokenizer.model.tok2vec]
+@architectures = "spacy.Tok2Vec.v2"
+
+[components.experimental_char_ner_tokenizer.model.tok2vec.embed]
+@architectures = "spacy.MultiHashEmbed.v2"
+width = 128
+attrs = ["ORTH","LOWER","IS_DIGIT","IS_ALPHA","IS_SPACE","IS_PUNCT"]
+rows = [1000,500,50,50,50,50]
+include_static_vectors = false
+
+[components.experimental_char_ner_tokenizer.model.tok2vec.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 128
+depth = 4
+window_size = 4
+maxout_pieces = 2
+```
+
+The NER version does not currently support sentence boundaries, but it would be
+easy to extend using a `B-SENT` entity type.
+
+### Biaffine parser
+
+A biaffine dependency parser, similar to that proposed in [Deep Biaffine
+Attention for Neural Dependency Parsing](Deep Biaffine Attention for Neural
+Dependency Parsing) (Dozat & Manning, 2016). The parser consists of two parts:
+an edge predicter and an edge labeler. For example:
+
+```ini
+[components.experimental_arc_predicter]
+factory = "experimental_arc_predicter"
+
+[components.experimental_arc_labeler]
+factory = "experimental_arc_labeler"
+```
+
+The arc predicter requires that a previous component (such as `senter`) sets
+sentence boundaries during training. Therefore, such a component must be
+added to `annotating_components`:
+
+```ini
+[training]
+annotating_components = ["senter"]
+```
+
+The [biaffine parser sample project](projects/biaffine_parser) provides an
+example biaffine parser pipeline.
+
+### Span Finder
+
+The SpanFinder is a new experimental component that identifies span boundaries
+by tagging potential start and end tokens. It's an ML approach to suggest
+candidate spans with higher precision.
+
+`SpanFinder` uses the following parameters:
+
+- `threshold`: Probability threshold for predicted spans.
+- `predicted_key`: Name of the [SpanGroup](https://spacy.io/api/spangroup) the predicted spans are saved to.
+- `training_key`: Name of the [SpanGroup](https://spacy.io/api/spangroup) the training spans are read from.
+- `max_length`: Max length of the predicted spans. No limit when set to `0`. Defaults to `0`.
+- `min_length`: Min length of the predicted spans. No limit when set to `0`. Defaults to `0`.
+
+Here is a config excerpt for the `SpanFinder` together with a `SpanCategorizer`:
+
+```ini
+[nlp]
+lang = "en"
+pipeline = ["tok2vec","span_finder","spancat"]
+batch_size = 128
+disabled = []
+before_creation = null
+after_creation = null
+after_pipeline_creation = null
+tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
+
+[components]
+
+[components.tok2vec]
+factory = "tok2vec"
+
+[components.tok2vec.model]
+@architectures = "spacy.Tok2Vec.v1"
+
+[components.tok2vec.model.embed]
+@architectures = "spacy.MultiHashEmbed.v2"
+width = ${components.tok2vec.model.encode.width}
+attrs = ["ORTH", "SHAPE"]
+rows = [5000, 2500]
+include_static_vectors = false
+
+[components.tok2vec.model.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 96
+depth = 4
+window_size = 1
+maxout_pieces = 3
+
+[components.span_finder]
+factory = "experimental_span_finder"
+threshold = 0.35
+predicted_key = "span_candidates"
+training_key = ${vars.spans_key}
+min_length = 0
+max_length = 0
+
+[components.span_finder.scorer]
+@scorers = "spacy-experimental.span_finder_scorer.v1"
+predicted_key = ${components.span_finder.predicted_key}
+training_key = ${vars.spans_key}
+
+[components.span_finder.model]
+@architectures = "spacy-experimental.SpanFinder.v1"
+
+[components.span_finder.model.scorer]
+@layers = "spacy.LinearLogistic.v1"
+nO=2
+
+[components.span_finder.model.tok2vec]
+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode.width}
+
+[components.spancat]
+factory = "spancat"
+max_positive = null
+spans_key = ${vars.spans_key}
+threshold = 0.5
+
+[components.spancat.model]
+@architectures = "spacy.SpanCategorizer.v1"
+
+[components.spancat.model.reducer]
+@layers = "spacy.mean_max_reducer.v1"
+hidden_size = 128
+
+[components.spancat.model.scorer]
+@layers = "spacy.LinearLogistic.v1"
+nO = null
+nI = null
+
+[components.spancat.model.tok2vec]
+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode.width}
+
+[components.spancat.suggester]
+@misc = "spacy-experimental.span_finder_suggester.v1"
+predicted_key = ${components.span_finder.predicted_key}
+```
+
+This package includes a [spaCy project](./projects/span_finder) which shows how to train and use the `SpanFinder` together with `SpanCategorizer`.
+
+### Coreference Components
+
+The [CoreferenceResolver](https://spacy.io/api/coref) and [SpanResolver](https://spacy.io/api/span-resolver) are designed to be used together to build a corerefence pipeline, which allows you to identify which spans in a document refer to the same thing. Each component also includes an architecture and scorer. For more details, see their pages in the main spaCy docs.
+
+For an example of how to build a pipeline with the components, see the [example coref project](https://github.com/explosion/projects/tree/v3/experimental/coref).
+
+## Architectures
+
+None currently.
+
+## Other
+
+### Tokenizers
+
+- `spacy-experimental.char_pretokenizer.v1`: Tokenize a text into individual
+  characters.
+
+### Scorers
+
+- `spacy-experimental.tokenizer_scorer.v1`: Score tokenization.
+- `spacy-experimental.tokenizer_senter_scorer.v1`: Score tokenization and
+  sentence segmentation.
+
+### Misc
+
+Suggester functions for spancat:
+
+**Subtree suggester**: Uses dependency annotation to suggest tokens with their syntactic descendants.
+
+- `spacy-experimental.subtree_suggester.v1`
+- `spacy-experimental.ngram_subtree_suggester.v1`
+
+**Chunk suggester**: Suggests noun chunks using the noun chunk iterator, which requires POS and dependency annotation.
+
+- `spacy-experimental.chunk_suggester.v1`
+- `spacy-experimental.ngram_chunk_suggester.v1`
+
+**Sentence suggester**: Uses sentence boundaries to suggest sentence spans.
+
+- `spacy-experimental.sentence_suggester.v1`
+- `spacy-experimental.ngram_sentence_suggester.v1`
+
+The package also contains a [`merge_suggesters`](spacy_experimental/span_suggesters/merge_suggesters.py) function which can be used to combine suggestions from multiple suggesters.
+
+Here are two config excerpts for using the `subtree suggester` with and without the ngram functionality:
+
+```
+[components.spancat.suggester]
+@misc = "spacy-experimental.subtree_suggester.v1"
+```
+
+```
+[components.spancat.suggester]
+@misc = "spacy-experimental.ngram_subtree_suggester.v1"
+sizes = [1, 2, 3]
+```
+
+Note that all the suggester functions are registered in `@misc`.
+
+## Bug reports and issues
+
+Please report bugs in the [spaCy issue
+tracker](https://github.com/explosion/spaCy/issues) or open a new thread on the
+[discussion board](https://github.com/explosion/spaCy/discussions) for other
+issues.
+
+## Older documentation
+
+See the READMEs in earlier [tagged
+versions](https://github.com/explosion/spacy-experimental/tags) for details
+about components in earlier releases.
+
+
+%package -n python3-spacy-experimental
+Summary:	Cutting-edge experimental spaCy components and features
+Provides:	python-spacy-experimental
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+BuildRequires:	python3-cffi
+BuildRequires:	gcc
+BuildRequires:	gdb
+%description -n python3-spacy-experimental
+<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
+
+# spacy-experimental: Cutting-edge experimental spaCy components and features
+
+This package includes experimental components and features for
+[spaCy](https://spacy.io) v3.x, for example model architectures, pipeline
+components and utilities.
+
+[![Azure Pipelines](https://img.shields.io/azure-devops/build/explosion-ai/public/26/master.svg?logo=azure-pipelines&style=flat-square&label=build)](https://dev.azure.com/explosion-ai/public/_build?definitionId=26)
+[![pypi Version](https://img.shields.io/pypi/v/spacy-experimental.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.org/project/spacy-experimental/)
+
+## Installation
+
+Install with `pip`:
+
+```bash
+python -m pip install -U pip setuptools wheel
+python -m pip install spacy-experimental
+```
+
+## Using spacy-experimental
+
+Components and features may be modified or removed in any release, so always
+specify the exact version as a package requirement if you're experimenting with
+a particular component, e.g.:
+
+```
+spacy-experimental==0.147.0
+```
+
+Then you can add the experimental components to your config or import from
+`spacy_experimental`:
+
+```ini
+[components.experimental_char_ner_tokenizer]
+factory = "experimental_char_ner_tokenizer"
+```
+
+## Components
+
+### Trainable character-based tokenizers
+
+Two trainable tokenizers represent tokenization as a sequence tagging problem
+over individual characters and use the existing spaCy tagger and NER
+architectures to perform the tagging.
+
+In the spaCy pipeline, a simple "pretokenizer" is applied as the pipeline
+tokenizer to split each doc into individual characters and the trainable
+tokenizer is a pipeline component that retokenizes the doc. The pretokenizer
+needs to be configured manually in the config or with `spacy.blank()`:
+
+```python
+nlp = spacy.blank(
+    "en",
+    config={
+        "nlp": {
+            "tokenizer": {"@tokenizers": "spacy-experimental.char_pretokenizer.v1"}
+        }
+    },
+)
+```
+
+The two tokenizers currently reset any existing tag or entity annotation
+respectively in the process of retokenizing.
+
+#### Character-based tagger tokenizer
+
+In the tagger version `experimental_char_tagger_tokenizer`, the tagging problem
+is represented internally with character-level tags for token start (`T`),
+token internal (`I`), and outside a token (`O`). This representation comes from
+[Elephant: Sequence Labeling for Word and Sentence
+Segmentation](https://aclanthology.org/D13-1146/) (Evang et al., 2013).
+
+```none
+This is a sentence.
+TIIIOTIOTOTIIIIIIIT
+```
+
+With the option `annotate_sents`, `S` replaces `T` for the first token in each
+sentence and the component predicts both token and sentence boundaries.
+
+```none
+This is a sentence.
+SIIIOTIOTOTIIIIIIIT
+```
+
+A config excerpt for `experimental_char_tagger_tokenizer`:
+
+```ini
+[nlp]
+pipeline = ["experimental_char_tagger_tokenizer"]
+tokenizer = {"@tokenizers":"spacy-experimental.char_pretokenizer.v1"}
+
+[components]
+
+[components.experimental_char_tagger_tokenizer]
+factory = "experimental_char_tagger_tokenizer"
+annotate_sents = true
+scorer = {"@scorers":"spacy-experimental.tokenizer_senter_scorer.v1"}
+
+[components.experimental_char_tagger_tokenizer.model]
+@architectures = "spacy.Tagger.v1"
+nO = null
+
+[components.experimental_char_tagger_tokenizer.model.tok2vec]
+@architectures = "spacy.Tok2Vec.v2"
+
+[components.experimental_char_tagger_tokenizer.model.tok2vec.embed]
+@architectures = "spacy.MultiHashEmbed.v2"
+width = 128
+attrs = ["ORTH","LOWER","IS_DIGIT","IS_ALPHA","IS_SPACE","IS_PUNCT"]
+rows = [1000,500,50,50,50,50]
+include_static_vectors = false
+
+[components.experimental_char_tagger_tokenizer.model.tok2vec.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 128
+depth = 4
+window_size = 4
+maxout_pieces = 2
+```
+
+#### Character-based NER tokenizer
+
+In the NER version, each character in a token is part of an entity:
+
+```none
+T	B-TOKEN
+h	I-TOKEN
+i	I-TOKEN
+s	I-TOKEN
+ 	O
+i	B-TOKEN
+s	I-TOKEN
+	O
+a	B-TOKEN
+ 	O
+s	B-TOKEN
+e	I-TOKEN
+n	I-TOKEN
+t	I-TOKEN
+e	I-TOKEN
+n	I-TOKEN
+c	I-TOKEN
+e	I-TOKEN
+.	B-TOKEN
+```
+
+A config excerpt for `experimental_char_ner_tokenizer`:
+
+```ini
+[nlp]
+pipeline = ["experimental_char_ner_tokenizer"]
+tokenizer = {"@tokenizers":"spacy-experimental.char_pretokenizer.v1"}
+
+[components]
+
+[components.experimental_char_ner_tokenizer]
+factory = "experimental_char_ner_tokenizer"
+scorer = {"@scorers":"spacy-experimental.tokenizer_scorer.v1"}
+
+[components.experimental_char_ner_tokenizer.model]
+@architectures = "spacy.TransitionBasedParser.v2"
+state_type = "ner"
+extra_state_tokens = false
+hidden_width = 64
+maxout_pieces = 2
+use_upper = true
+nO = null
+
+[components.experimental_char_ner_tokenizer.model.tok2vec]
+@architectures = "spacy.Tok2Vec.v2"
+
+[components.experimental_char_ner_tokenizer.model.tok2vec.embed]
+@architectures = "spacy.MultiHashEmbed.v2"
+width = 128
+attrs = ["ORTH","LOWER","IS_DIGIT","IS_ALPHA","IS_SPACE","IS_PUNCT"]
+rows = [1000,500,50,50,50,50]
+include_static_vectors = false
+
+[components.experimental_char_ner_tokenizer.model.tok2vec.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 128
+depth = 4
+window_size = 4
+maxout_pieces = 2
+```
+
+The NER version does not currently support sentence boundaries, but it would be
+easy to extend using a `B-SENT` entity type.
+
+### Biaffine parser
+
+A biaffine dependency parser, similar to that proposed in [Deep Biaffine
+Attention for Neural Dependency Parsing](Deep Biaffine Attention for Neural
+Dependency Parsing) (Dozat & Manning, 2016). The parser consists of two parts:
+an edge predicter and an edge labeler. For example:
+
+```ini
+[components.experimental_arc_predicter]
+factory = "experimental_arc_predicter"
+
+[components.experimental_arc_labeler]
+factory = "experimental_arc_labeler"
+```
+
+The arc predicter requires that a previous component (such as `senter`) sets
+sentence boundaries during training. Therefore, such a component must be
+added to `annotating_components`:
+
+```ini
+[training]
+annotating_components = ["senter"]
+```
+
+The [biaffine parser sample project](projects/biaffine_parser) provides an
+example biaffine parser pipeline.
+
+### Span Finder
+
+The SpanFinder is a new experimental component that identifies span boundaries
+by tagging potential start and end tokens. It's an ML approach to suggest
+candidate spans with higher precision.
+
+`SpanFinder` uses the following parameters:
+
+- `threshold`: Probability threshold for predicted spans.
+- `predicted_key`: Name of the [SpanGroup](https://spacy.io/api/spangroup) the predicted spans are saved to.
+- `training_key`: Name of the [SpanGroup](https://spacy.io/api/spangroup) the training spans are read from.
+- `max_length`: Max length of the predicted spans. No limit when set to `0`. Defaults to `0`.
+- `min_length`: Min length of the predicted spans. No limit when set to `0`. Defaults to `0`.
+
+Here is a config excerpt for the `SpanFinder` together with a `SpanCategorizer`:
+
+```ini
+[nlp]
+lang = "en"
+pipeline = ["tok2vec","span_finder","spancat"]
+batch_size = 128
+disabled = []
+before_creation = null
+after_creation = null
+after_pipeline_creation = null
+tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
+
+[components]
+
+[components.tok2vec]
+factory = "tok2vec"
+
+[components.tok2vec.model]
+@architectures = "spacy.Tok2Vec.v1"
+
+[components.tok2vec.model.embed]
+@architectures = "spacy.MultiHashEmbed.v2"
+width = ${components.tok2vec.model.encode.width}
+attrs = ["ORTH", "SHAPE"]
+rows = [5000, 2500]
+include_static_vectors = false
+
+[components.tok2vec.model.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 96
+depth = 4
+window_size = 1
+maxout_pieces = 3
+
+[components.span_finder]
+factory = "experimental_span_finder"
+threshold = 0.35
+predicted_key = "span_candidates"
+training_key = ${vars.spans_key}
+min_length = 0
+max_length = 0
+
+[components.span_finder.scorer]
+@scorers = "spacy-experimental.span_finder_scorer.v1"
+predicted_key = ${components.span_finder.predicted_key}
+training_key = ${vars.spans_key}
+
+[components.span_finder.model]
+@architectures = "spacy-experimental.SpanFinder.v1"
+
+[components.span_finder.model.scorer]
+@layers = "spacy.LinearLogistic.v1"
+nO=2
+
+[components.span_finder.model.tok2vec]
+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode.width}
+
+[components.spancat]
+factory = "spancat"
+max_positive = null
+spans_key = ${vars.spans_key}
+threshold = 0.5
+
+[components.spancat.model]
+@architectures = "spacy.SpanCategorizer.v1"
+
+[components.spancat.model.reducer]
+@layers = "spacy.mean_max_reducer.v1"
+hidden_size = 128
+
+[components.spancat.model.scorer]
+@layers = "spacy.LinearLogistic.v1"
+nO = null
+nI = null
+
+[components.spancat.model.tok2vec]
+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode.width}
+
+[components.spancat.suggester]
+@misc = "spacy-experimental.span_finder_suggester.v1"
+predicted_key = ${components.span_finder.predicted_key}
+```
+
+This package includes a [spaCy project](./projects/span_finder) which shows how to train and use the `SpanFinder` together with `SpanCategorizer`.
+
+### Coreference Components
+
+The [CoreferenceResolver](https://spacy.io/api/coref) and [SpanResolver](https://spacy.io/api/span-resolver) are designed to be used together to build a corerefence pipeline, which allows you to identify which spans in a document refer to the same thing. Each component also includes an architecture and scorer. For more details, see their pages in the main spaCy docs.
+
+For an example of how to build a pipeline with the components, see the [example coref project](https://github.com/explosion/projects/tree/v3/experimental/coref).
+
+## Architectures
+
+None currently.
+
+## Other
+
+### Tokenizers
+
+- `spacy-experimental.char_pretokenizer.v1`: Tokenize a text into individual
+  characters.
+
+### Scorers
+
+- `spacy-experimental.tokenizer_scorer.v1`: Score tokenization.
+- `spacy-experimental.tokenizer_senter_scorer.v1`: Score tokenization and
+  sentence segmentation.
+
+### Misc
+
+Suggester functions for spancat:
+
+**Subtree suggester**: Uses dependency annotation to suggest tokens with their syntactic descendants.
+
+- `spacy-experimental.subtree_suggester.v1`
+- `spacy-experimental.ngram_subtree_suggester.v1`
+
+**Chunk suggester**: Suggests noun chunks using the noun chunk iterator, which requires POS and dependency annotation.
+
+- `spacy-experimental.chunk_suggester.v1`
+- `spacy-experimental.ngram_chunk_suggester.v1`
+
+**Sentence suggester**: Uses sentence boundaries to suggest sentence spans.
+
+- `spacy-experimental.sentence_suggester.v1`
+- `spacy-experimental.ngram_sentence_suggester.v1`
+
+The package also contains a [`merge_suggesters`](spacy_experimental/span_suggesters/merge_suggesters.py) function which can be used to combine suggestions from multiple suggesters.
+
+Here are two config excerpts for using the `subtree suggester` with and without the ngram functionality:
+
+```
+[components.spancat.suggester]
+@misc = "spacy-experimental.subtree_suggester.v1"
+```
+
+```
+[components.spancat.suggester]
+@misc = "spacy-experimental.ngram_subtree_suggester.v1"
+sizes = [1, 2, 3]
+```
+
+Note that all the suggester functions are registered in `@misc`.
+
+## Bug reports and issues
+
+Please report bugs in the [spaCy issue
+tracker](https://github.com/explosion/spaCy/issues) or open a new thread on the
+[discussion board](https://github.com/explosion/spaCy/discussions) for other
+issues.
+
+## Older documentation
+
+See the READMEs in earlier [tagged
+versions](https://github.com/explosion/spacy-experimental/tags) for details
+about components in earlier releases.
+
+
+%package help
+Summary:	Development documents and examples for spacy-experimental
+Provides:	python3-spacy-experimental-doc
+%description help
+<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
+
+# spacy-experimental: Cutting-edge experimental spaCy components and features
+
+This package includes experimental components and features for
+[spaCy](https://spacy.io) v3.x, for example model architectures, pipeline
+components and utilities.
+
+[![Azure Pipelines](https://img.shields.io/azure-devops/build/explosion-ai/public/26/master.svg?logo=azure-pipelines&style=flat-square&label=build)](https://dev.azure.com/explosion-ai/public/_build?definitionId=26)
+[![pypi Version](https://img.shields.io/pypi/v/spacy-experimental.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.org/project/spacy-experimental/)
+
+## Installation
+
+Install with `pip`:
+
+```bash
+python -m pip install -U pip setuptools wheel
+python -m pip install spacy-experimental
+```
+
+## Using spacy-experimental
+
+Components and features may be modified or removed in any release, so always
+specify the exact version as a package requirement if you're experimenting with
+a particular component, e.g.:
+
+```
+spacy-experimental==0.147.0
+```
+
+Then you can add the experimental components to your config or import from
+`spacy_experimental`:
+
+```ini
+[components.experimental_char_ner_tokenizer]
+factory = "experimental_char_ner_tokenizer"
+```
+
+## Components
+
+### Trainable character-based tokenizers
+
+Two trainable tokenizers represent tokenization as a sequence tagging problem
+over individual characters and use the existing spaCy tagger and NER
+architectures to perform the tagging.
+
+In the spaCy pipeline, a simple "pretokenizer" is applied as the pipeline
+tokenizer to split each doc into individual characters and the trainable
+tokenizer is a pipeline component that retokenizes the doc. The pretokenizer
+needs to be configured manually in the config or with `spacy.blank()`:
+
+```python
+nlp = spacy.blank(
+    "en",
+    config={
+        "nlp": {
+            "tokenizer": {"@tokenizers": "spacy-experimental.char_pretokenizer.v1"}
+        }
+    },
+)
+```
+
+The two tokenizers currently reset any existing tag or entity annotation
+respectively in the process of retokenizing.
+
+#### Character-based tagger tokenizer
+
+In the tagger version `experimental_char_tagger_tokenizer`, the tagging problem
+is represented internally with character-level tags for token start (`T`),
+token internal (`I`), and outside a token (`O`). This representation comes from
+[Elephant: Sequence Labeling for Word and Sentence
+Segmentation](https://aclanthology.org/D13-1146/) (Evang et al., 2013).
+
+```none
+This is a sentence.
+TIIIOTIOTOTIIIIIIIT
+```
+
+With the option `annotate_sents`, `S` replaces `T` for the first token in each
+sentence and the component predicts both token and sentence boundaries.
+
+```none
+This is a sentence.
+SIIIOTIOTOTIIIIIIIT
+```
+
+A config excerpt for `experimental_char_tagger_tokenizer`:
+
+```ini
+[nlp]
+pipeline = ["experimental_char_tagger_tokenizer"]
+tokenizer = {"@tokenizers":"spacy-experimental.char_pretokenizer.v1"}
+
+[components]
+
+[components.experimental_char_tagger_tokenizer]
+factory = "experimental_char_tagger_tokenizer"
+annotate_sents = true
+scorer = {"@scorers":"spacy-experimental.tokenizer_senter_scorer.v1"}
+
+[components.experimental_char_tagger_tokenizer.model]
+@architectures = "spacy.Tagger.v1"
+nO = null
+
+[components.experimental_char_tagger_tokenizer.model.tok2vec]
+@architectures = "spacy.Tok2Vec.v2"
+
+[components.experimental_char_tagger_tokenizer.model.tok2vec.embed]
+@architectures = "spacy.MultiHashEmbed.v2"
+width = 128
+attrs = ["ORTH","LOWER","IS_DIGIT","IS_ALPHA","IS_SPACE","IS_PUNCT"]
+rows = [1000,500,50,50,50,50]
+include_static_vectors = false
+
+[components.experimental_char_tagger_tokenizer.model.tok2vec.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 128
+depth = 4
+window_size = 4
+maxout_pieces = 2
+```
+
+#### Character-based NER tokenizer
+
+In the NER version, each character in a token is part of an entity:
+
+```none
+T	B-TOKEN
+h	I-TOKEN
+i	I-TOKEN
+s	I-TOKEN
+ 	O
+i	B-TOKEN
+s	I-TOKEN
+	O
+a	B-TOKEN
+ 	O
+s	B-TOKEN
+e	I-TOKEN
+n	I-TOKEN
+t	I-TOKEN
+e	I-TOKEN
+n	I-TOKEN
+c	I-TOKEN
+e	I-TOKEN
+.	B-TOKEN
+```
+
+A config excerpt for `experimental_char_ner_tokenizer`:
+
+```ini
+[nlp]
+pipeline = ["experimental_char_ner_tokenizer"]
+tokenizer = {"@tokenizers":"spacy-experimental.char_pretokenizer.v1"}
+
+[components]
+
+[components.experimental_char_ner_tokenizer]
+factory = "experimental_char_ner_tokenizer"
+scorer = {"@scorers":"spacy-experimental.tokenizer_scorer.v1"}
+
+[components.experimental_char_ner_tokenizer.model]
+@architectures = "spacy.TransitionBasedParser.v2"
+state_type = "ner"
+extra_state_tokens = false
+hidden_width = 64
+maxout_pieces = 2
+use_upper = true
+nO = null
+
+[components.experimental_char_ner_tokenizer.model.tok2vec]
+@architectures = "spacy.Tok2Vec.v2"
+
+[components.experimental_char_ner_tokenizer.model.tok2vec.embed]
+@architectures = "spacy.MultiHashEmbed.v2"
+width = 128
+attrs = ["ORTH","LOWER","IS_DIGIT","IS_ALPHA","IS_SPACE","IS_PUNCT"]
+rows = [1000,500,50,50,50,50]
+include_static_vectors = false
+
+[components.experimental_char_ner_tokenizer.model.tok2vec.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 128
+depth = 4
+window_size = 4
+maxout_pieces = 2
+```
+
+The NER version does not currently support sentence boundaries, but it would be
+easy to extend using a `B-SENT` entity type.
+
+### Biaffine parser
+
+A biaffine dependency parser, similar to that proposed in [Deep Biaffine
+Attention for Neural Dependency Parsing](Deep Biaffine Attention for Neural
+Dependency Parsing) (Dozat & Manning, 2016). The parser consists of two parts:
+an edge predicter and an edge labeler. For example:
+
+```ini
+[components.experimental_arc_predicter]
+factory = "experimental_arc_predicter"
+
+[components.experimental_arc_labeler]
+factory = "experimental_arc_labeler"
+```
+
+The arc predicter requires that a previous component (such as `senter`) sets
+sentence boundaries during training. Therefore, such a component must be
+added to `annotating_components`:
+
+```ini
+[training]
+annotating_components = ["senter"]
+```
+
+The [biaffine parser sample project](projects/biaffine_parser) provides an
+example biaffine parser pipeline.
+
+### Span Finder
+
+The SpanFinder is a new experimental component that identifies span boundaries
+by tagging potential start and end tokens. It's an ML approach to suggest
+candidate spans with higher precision.
+
+`SpanFinder` uses the following parameters:
+
+- `threshold`: Probability threshold for predicted spans.
+- `predicted_key`: Name of the [SpanGroup](https://spacy.io/api/spangroup) the predicted spans are saved to.
+- `training_key`: Name of the [SpanGroup](https://spacy.io/api/spangroup) the training spans are read from.
+- `max_length`: Max length of the predicted spans. No limit when set to `0`. Defaults to `0`.
+- `min_length`: Min length of the predicted spans. No limit when set to `0`. Defaults to `0`.
+
+Here is a config excerpt for the `SpanFinder` together with a `SpanCategorizer`:
+
+```ini
+[nlp]
+lang = "en"
+pipeline = ["tok2vec","span_finder","spancat"]
+batch_size = 128
+disabled = []
+before_creation = null
+after_creation = null
+after_pipeline_creation = null
+tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
+
+[components]
+
+[components.tok2vec]
+factory = "tok2vec"
+
+[components.tok2vec.model]
+@architectures = "spacy.Tok2Vec.v1"
+
+[components.tok2vec.model.embed]
+@architectures = "spacy.MultiHashEmbed.v2"
+width = ${components.tok2vec.model.encode.width}
+attrs = ["ORTH", "SHAPE"]
+rows = [5000, 2500]
+include_static_vectors = false
+
+[components.tok2vec.model.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 96
+depth = 4
+window_size = 1
+maxout_pieces = 3
+
+[components.span_finder]
+factory = "experimental_span_finder"
+threshold = 0.35
+predicted_key = "span_candidates"
+training_key = ${vars.spans_key}
+min_length = 0
+max_length = 0
+
+[components.span_finder.scorer]
+@scorers = "spacy-experimental.span_finder_scorer.v1"
+predicted_key = ${components.span_finder.predicted_key}
+training_key = ${vars.spans_key}
+
+[components.span_finder.model]
+@architectures = "spacy-experimental.SpanFinder.v1"
+
+[components.span_finder.model.scorer]
+@layers = "spacy.LinearLogistic.v1"
+nO=2
+
+[components.span_finder.model.tok2vec]
+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode.width}
+
+[components.spancat]
+factory = "spancat"
+max_positive = null
+spans_key = ${vars.spans_key}
+threshold = 0.5
+
+[components.spancat.model]
+@architectures = "spacy.SpanCategorizer.v1"
+
+[components.spancat.model.reducer]
+@layers = "spacy.mean_max_reducer.v1"
+hidden_size = 128
+
+[components.spancat.model.scorer]
+@layers = "spacy.LinearLogistic.v1"
+nO = null
+nI = null
+
+[components.spancat.model.tok2vec]
+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode.width}
+
+[components.spancat.suggester]
+@misc = "spacy-experimental.span_finder_suggester.v1"
+predicted_key = ${components.span_finder.predicted_key}
+```
+
+This package includes a [spaCy project](./projects/span_finder) which shows how to train and use the `SpanFinder` together with `SpanCategorizer`.
+
+### Coreference Components
+
+The [CoreferenceResolver](https://spacy.io/api/coref) and [SpanResolver](https://spacy.io/api/span-resolver) are designed to be used together to build a corerefence pipeline, which allows you to identify which spans in a document refer to the same thing. Each component also includes an architecture and scorer. For more details, see their pages in the main spaCy docs.
+
+For an example of how to build a pipeline with the components, see the [example coref project](https://github.com/explosion/projects/tree/v3/experimental/coref).
+
+## Architectures
+
+None currently.
+
+## Other
+
+### Tokenizers
+
+- `spacy-experimental.char_pretokenizer.v1`: Tokenize a text into individual
+  characters.
+
+### Scorers
+
+- `spacy-experimental.tokenizer_scorer.v1`: Score tokenization.
+- `spacy-experimental.tokenizer_senter_scorer.v1`: Score tokenization and
+  sentence segmentation.
+
+### Misc
+
+Suggester functions for spancat:
+
+**Subtree suggester**: Uses dependency annotation to suggest tokens with their syntactic descendants.
+
+- `spacy-experimental.subtree_suggester.v1`
+- `spacy-experimental.ngram_subtree_suggester.v1`
+
+**Chunk suggester**: Suggests noun chunks using the noun chunk iterator, which requires POS and dependency annotation.
+
+- `spacy-experimental.chunk_suggester.v1`
+- `spacy-experimental.ngram_chunk_suggester.v1`
+
+**Sentence suggester**: Uses sentence boundaries to suggest sentence spans.
+
+- `spacy-experimental.sentence_suggester.v1`
+- `spacy-experimental.ngram_sentence_suggester.v1`
+
+The package also contains a [`merge_suggesters`](spacy_experimental/span_suggesters/merge_suggesters.py) function which can be used to combine suggestions from multiple suggesters.
+
+Here are two config excerpts for using the `subtree suggester` with and without the ngram functionality:
+
+```
+[components.spancat.suggester]
+@misc = "spacy-experimental.subtree_suggester.v1"
+```
+
+```
+[components.spancat.suggester]
+@misc = "spacy-experimental.ngram_subtree_suggester.v1"
+sizes = [1, 2, 3]
+```
+
+Note that all the suggester functions are registered in `@misc`.
+
+## Bug reports and issues
+
+Please report bugs in the [spaCy issue
+tracker](https://github.com/explosion/spaCy/issues) or open a new thread on the
+[discussion board](https://github.com/explosion/spaCy/discussions) for other
+issues.
+
+## Older documentation
+
+See the READMEs in earlier [tagged
+versions](https://github.com/explosion/spacy-experimental/tags) for details
+about components in earlier releases.
+
+
+%prep
+%autosetup -n spacy-experimental-0.6.2
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-spacy-experimental -f filelist.lst
+%dir %{python3_sitearch}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.2-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..c2d96d2
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+46aaa58c5aa4ceced2d55432d6eec33e  spacy-experimental-0.6.2.tar.gz