Update spaCy pipeline

.gitattributes

@@ -39,3 +39,4 @@ trainable_lemmatizer/trees filter=lfs diff=lfs merge=lfs -text
 trainable_lemmatizer/model filter=lfs diff=lfs merge=lfs -text
 morphologizer/model filter=lfs diff=lfs merge=lfs -text
 tagger/model filter=lfs diff=lfs merge=lfs -text
+frequency_lemmatizer/table.json filter=lfs diff=lfs merge=lfs -text

config.cfg

@@ -1,9 +1,10 @@
 [paths]
-train = "corpus/train.spacy"
-test = "corpus/test.spacy"
-dev = "corpus/dev.spacy"
+train = "corpus/ner/train.spacy"
+test = null
+dev = "corpus/ner/dev.spacy"
 vectors = null
 init_tok2vec = null
+base_model = "grc_dep_treebanks_trf"
 
 [system]
 gpu_allocator = "pytorch"
@@ -11,7 +12,7 @@ seed = 0
 
 [nlp]
 lang = "grc"
-pipeline = ["transformer","tagger","morphologizer","parser","senter","trainable_lemmatizer","entity_ruler"]
+pipeline = ["transformer","tagger","morphologizer","parser","trainable_lemmatizer","frequency_lemmatizer","ner"]
 batch_size = 1024
 disabled = []
 before_creation = null
@@ -21,13 +22,10 @@ tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
 
 [components]
 
-[components.entity_ruler]
-factory = "entity_ruler"
-ent_id_sep = "||"
-overwrite_ents = false
-phrase_matcher_attr = null
-scorer = {"@scorers":"spacy.entity_ruler_scorer.v1"}
-validate = false
+[components.frequency_lemmatizer]
+factory = "frequency_lemmatizer"
+fallback_priority = "lookup"
+overwrite = true
 
 [components.morphologizer]
 factory = "morphologizer"
@@ -46,6 +44,28 @@ grad_factor = 1.0
 pooling = {"@layers":"reduce_mean.v1"}
 upstream = "*"
 
+[components.ner]
+factory = "ner"
+incorrect_spans_key = null
+moves = null
+scorer = {"@scorers":"spacy.ner_scorer.v1"}
+update_with_oracle_cut_size = 100
+
+[components.ner.model]
+@architectures = "spacy.TransitionBasedParser.v2"
+state_type = "ner"
+extra_state_tokens = false
+hidden_width = 64
+maxout_pieces = 2
+use_upper = false
+nO = null
+
+[components.ner.model.tok2vec]
+@architectures = "spacy-transformers.TransformerListener.v1"
+grad_factor = 1.0
+pooling = {"@layers":"reduce_mean.v1"}
+upstream = "*"
+
 [components.parser]
 factory = "parser"
 learn_tokens = false
@@ -60,7 +80,7 @@ state_type = "parser"
 extra_state_tokens = false
 hidden_width = 128
 maxout_pieces = 3
-use_upper = false
+use_upper = true
 nO = null
 
 [components.parser.model.tok2vec]
@@ -69,26 +89,6 @@ grad_factor = 1.0
 pooling = {"@layers":"reduce_mean.v1"}
 upstream = "*"
 
-[components.senter]
-factory = "senter"
-overwrite = false
-scorer = {"@scorers":"spacy.senter_scorer.v1"}
-
-[components.senter.model]
-@architectures = "spacy.Tagger.v2"
-nO = null
-normalize = false
-
-[components.senter.model.tok2vec]
-@architectures = "spacy.HashEmbedCNN.v2"
-pretrained_vectors = null
-width = 12
-depth = 1
-embed_size = 2000
-window_size = 1
-maxout_pieces = 2
-subword_features = true
-
 [components.tagger]
 factory = "tagger"
 neg_prefix = "!"
@@ -179,6 +179,7 @@ eval_frequency = 200
 frozen_components = []
 annotating_components = []
 before_to_disk = null
+before_update = null
 
 [training.batcher]
 @batchers = "spacy.batch_by_padded.v1"
@@ -213,7 +214,7 @@ total_steps = 20000
 initial_rate = 0.00005
 
 [training.score_weights]
-tag_acc = 0.13
+tag_acc = 0.14
 pos_acc = 0.06
 morph_acc = 0.06
 morph_per_feat = null
@@ -223,10 +224,10 @@ dep_las_per_type = null
 sents_p = null
 sents_r = null
 sents_f = 0.0
-lemma_acc = 0.13
-ents_f = 0.5
+lemma_acc = 0.28
+ents_f = 0.28
 ents_p = 0.0
-ents_r = 0.0
+ents_r = 0.06
 ents_per_type = null
 
 [pretraining]
@@ -241,11 +242,15 @@ after_init = null
 
 [initialize.components]
 
-[initialize.components.entity_ruler]
+[initialize.components.frequency_lemmatizer]
+
+[initialize.components.frequency_lemmatizer.lookup]
+@readers = "srsly.read_json.v1"
+path = "assets/lemmas/lemma_lookup.json"
 
-[initialize.components.entity_ruler.patterns]
+[initialize.components.frequency_lemmatizer.table]
 @readers = "srsly.read_jsonl.v1"
-path = "assets/named_entities/patterns.jsonl"
+path = "assets/lemmas/lemma_table.jsonl"
 skip = false
 
 [initialize.tokenizer]

frequency_lemmatizer/config.json

@@ -0,0 +1 @@
+{"overwrite": true, "fallback_priority": "lookup"}

frequency_lemmatizer/table.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9b4678164e1be8f5de58fea33c1a25f57c348012986f69762dc6fde547f955ad
+size 26885581

grc_homercy_treebanks_trf-any-py3-none-any.whl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:749132ceee19f8e8a2f631f585488e5c5bdd135de2c66478c7389ceed6f5d57e
-size 497112265
+oid sha256:892bd8cc5e7b588a6e5ac2821830b0ba0da6f726857347d86b4189f2d600d774
+size 497741351

lemmatizer.py

@@ -0,0 +1,271 @@
+import os
+import json
+from pathlib import Path
+from typing import Dict, List, Literal, Optional, Union, Iterable
+from typing_extensions import TypedDict, NotRequired
+
+from spacy.language import Language
+from spacy.pipeline import Pipe
+from spacy.pipeline.lemmatizer import lemmatizer_score
+from spacy.util import ensure_path
+from spacy.tokens import Doc, Token
+
+MATCH_ORDER = [
+    "upos",
+    "Tense",
+    "VerbForm",
+    "Voice",
+    "Case",
+    "Gender",
+    "Number",
+    "Degree",
+    "Mood",
+    "Person",
+    "Aspect",
+    "Definite",
+    "PronType",
+    "Polarity",
+    "Poss",
+    "Reflex",
+]
+
+
+class TableEntry(TypedDict):
+    form: str
+    lemma: str
+    upos: str
+    frequency: int
+    Tense: NotRequired[str]
+    VerbForm: NotRequired[str]
+    Voice: NotRequired[str]
+    Case: NotRequired[str]
+    Gender: NotRequired[str]
+    Number: NotRequired[str]
+    Degree: NotRequired[str]
+    Mood: NotRequired[str]
+    Person: NotRequired[str]
+    Aspect: NotRequired[str]
+    Definite: NotRequired[str]
+    PronType: NotRequired[str]
+    Polarity: NotRequired[str]
+    Poss: NotRequired[str]
+    Reflex: NotRequired[str]
+
+
+FrequencyTable = Dict[str, List[TableEntry]]
+
+LookupTable = Dict[str, str]
+
+
+@Language.factory(
+    "frequency_lemmatizer",
+    assigns=["token.lemma"],
+    default_config={
+        "overwrite": True,
+        "fallback_priority": "lookup",
+    },
+    default_score_weights={"lemma_acc": 1.0},
+)
+def make_lemmatizer(
+    nlp: Language,
+    name: str,
+    overwrite: bool,
+    fallback_priority: Literal["lemma", "lookup"],
+):
+    return FrequencyLemmatizer(
+        nlp=nlp,
+        name=name,
+        overwrite=overwrite,
+        fallback_priority=fallback_priority,
+    )  # type: ignore
+
+
+def max_freq_lemma(entries: List[TableEntry]) -> str:
+    """Returns lemma with highest frequency from the given entries."""
+    max_index = 0
+    n_entries = len(entries)
+    for index in range(1, n_entries):
+        if entries[index]["frequency"] > entries[max_index]["frequency"]:
+            max_index = index
+    return entries[max_index]["lemma"]
+
+
+def match_lemma(
+    token_entry: TableEntry, table: FrequencyTable
+) -> Optional[str]:
+    """Returns a lemma for a token if it
+    can be found in the frequency table.
+    """
+    # Tries to find the entries associated with the token in the table
+    match = table.get(token_entry["form"], [])
+    if not match:
+        return None
+    # We go through all the properties to be matched
+    for match_property in MATCH_ORDER:
+        match_new = [
+            entry
+            for entry in match
+            if entry.get(match_property, "")
+            == token_entry.get(match_property, "")
+        ]
+        if not match_new:
+            return max_freq_lemma(entries=match)
+        match = match_new
+    return max_freq_lemma(entries=match)
+
+
+def read_json(path: str) -> Dict:
+    with open(path) as file:
+        res = json.load(file)
+    return res
+
+
+def write_json(object: Dict, path: str) -> None:
+    with open(path, "w") as file:
+        json.dump(object, file)
+
+
+class FrequencyLemmatizer(Pipe):
+    """
+    Part-of-speech and morphology, and frequency
+    sensitive rule-based lemmatizer.
+
+    Parameters
+    ----------
+    overwrite: bool, default True
+        Specifies whether the frequency lemmatizer should overwrite
+        already assigned lemmas.
+    fallback_priority: 'lemma' or 'lookup', default 'lookup'
+        Specifies which fallback should have higher priority
+            if the lemma is not found in
+        the primary table.
+    """
+
+    def __init__(
+        self,
+        nlp: Language,
+        name: str = "freq_lemmatizer",
+        *,
+        overwrite: bool = True,
+        fallback_priority: Literal["lemma", "lookup"] = "lookup",
+    ):
+        self.name = name
+        self.overwrite = overwrite
+        self.scorer = lemmatizer_score
+        self.fallback_priority = fallback_priority
+
+    def initialize(
+        self,
+        get_examples=None,
+        *,
+        nlp=None,
+        table: Optional[FrequencyTable] = None,
+        lookup: Optional[LookupTable] = None,
+    ) -> None:
+        """Initializes the frequency lemmatizer from given lemma table and lookup.
+
+        Parameters
+        ----------
+        table: iterable of entries or None, default None
+            Iterable of all entries in the lemma table
+            with pos tags morph features and frequencies.
+        lookup: dict of str to str or None, default None
+            Backoff lookup table for simple token-lemma lookup.
+        """
+        if table is None:
+            self.table = None
+        else:
+            self.table = table
+        self.lookup = lookup
+
+    def backoff(self, token: Token) -> str:
+        """Gets backoff token based on priority."""
+        orth = token.orth_.lower()
+        lookup = self.lookup
+        in_lookup = (lookup is not None) and (orth in lookup)
+        priority = self.fallback_priority
+        has_lemma = (token.lemma != 0) and (token.lemma_ != token.orth_)
+        if in_lookup:
+            if priority == "lookup":
+                return lookup[orth]  # type: ignore
+            else:
+                if has_lemma:
+                    return token.lemma_
+                else:
+                    return token.orth_
+        else:
+            if has_lemma:
+                return token.lemma_
+            else:
+                return token.orth_
+
+    def lemmatize(self, token: Token) -> str:
+        """Lemmatizes token."""
+        backoff = self.backoff(token)
+        orth = token.orth_.lower()
+        # If the table is empty we early return
+        if self.table is None:
+            return backoff
+        # I only add frequency for type compatibility
+        token_entry: TableEntry = TableEntry(
+            form=orth, upos=token.pos_, frequency=-1, **token.morph.to_dict()
+        )
+        lemma = match_lemma(token_entry=token_entry, table=self.table)
+        if lemma is None:
+            return backoff
+        else:
+            return lemma
+
+    def __call__(self, doc: Doc) -> Doc:
+        """Apply the lemmatization to a document."""
+        error_handler = self.get_error_handler()
+        try:
+            for token in doc:
+                if self.overwrite or token.lemma == 0:
+                    token.lemma_ = self.lemmatize(token)
+            return doc
+        except Exception as e:
+            error_handler(self.name, self, [doc], e)
+
+    def to_disk(
+        self, path: Union[str, Path], *, exclude: Iterable[str] = tuple()
+    ):
+        """Save frequency lemmatizer data to a directory."""
+        path = ensure_path(path)
+        Path(path).mkdir(parents=True, exist_ok=True)
+        config = dict(
+            overwrite=self.overwrite, fallback_priority=self.fallback_priority
+        )
+        with open(os.path.join(path, "config.json"), "w") as config_file:
+            json.dump(config, config_file)
+        if self.table is not None:
+            table_path = os.path.join(path, "table.json")
+            write_json(self.table, path=table_path)
+        if self.lookup is not None:
+            lookup_path = os.path.join(path, "lookup.json")
+            write_json(self.lookup, path=lookup_path)
+
+    def from_disk(
+        self, path: Union[str, Path], *, exclude: Iterable[str] = tuple()
+    ) -> "FrequencyLemmatizer":
+        """Load component from disk."""
+        path = ensure_path(path)
+        config = read_json(os.path.join(path, "config.json"))
+        self.overwrite = config.get("overwrite", self.overwrite)
+        self.fallback_priority = config.get(
+            "fallback_priority", self.fallback_priority
+        )
+        try:
+            table: Optional[FrequencyTable] = read_json(
+                os.path.join(path, "table.json")
+            )
+        except FileNotFoundError:
+            table = None
+        try:
+            lookup: Optional[LookupTable] = read_json(
+                os.path.join(path, "lookup.json")
+            )
+        except FileNotFoundError:
+            lookup = None
+        self.initialize(table=table, lookup=lookup)
+        return self

meta.json

@@ -1,13 +1,13 @@
 {
   "lang":"grc",
   "name":"homercy_treebanks_trf",
-  "version":"0.0.0",
+  "version":"0.0.1",
   "description":"",
-  "author":"marton&jan",
+  "author":"",
   "email":"",
   "url":"",
   "license":"",
-  "spacy_version":">=3.4.4,<3.5.0",
+  "spacy_version":">=3.5.0,<3.6.0",
   "spacy_git_version":"Unknown",
   "vectors":{
     "width":0,
@@ -2324,10 +2324,13 @@
       "vocative",
       "xcomp"
     ],
-    "entity_ruler":[
-      "ETHNICITY",
-      "LOCATION",
-      "PERSON"
+    "frequency_lemmatizer":[
+
+    ],
+    "ner":[
+      "GROUP",
+      "PERSON",
+      "PLACE"
     ]
   },
   "pipeline":[
@@ -2335,291 +2338,60 @@
     "tagger",
     "morphologizer",
     "parser",
-    "senter",
     "trainable_lemmatizer",
-    "entity_ruler"
+    "frequency_lemmatizer",
+    "ner"
   ],
   "components":[
     "transformer",
     "tagger",
     "morphologizer",
     "parser",
-    "senter",
     "trainable_lemmatizer",
-    "entity_ruler"
+    "frequency_lemmatizer",
+    "ner"
   ],
   "disabled":[
 
   ],
   "performance":{
-    "tag_acc":0.94086886,
-    "pos_acc":0.9713782352,
-    "morph_acc":0.9377368252,
-    "morph_per_feat":{
-      "Case":{
-        "p":0.9781124603,
-        "r":0.9743549811,
-        "f":0.9762301051
-      },
-      "Gender":{
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-        "r":0.9498877695,
-        "f":0.9499621519
-      },
-      "Number":{
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-        "r":0.9872758067,
-        "f":0.9889507569
-      },
-      "Person":{
-        "p":0.981749528,
-        "r":0.9739854318,
-        "f":0.9778520685
-      },
-      "PronType":{
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-        "r":0.9831168831,
-        "f":0.9805699482
-      },
-      "Polarity":{
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-        "r":0.9740932642,
-        "f":0.9842931937
-      },
-      "Aspect":{
-        "p":0.9578220859,
-        "r":0.9670925281,
-        "f":0.9624349836
-      },
-      "Mood":{
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-        "r":0.9808714134,
-        "f":0.9813928761
-      },
-      "Tense":{
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-        "r":0.9704461495,
-        "f":0.9712741752
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-      "VerbForm":{
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-        "r":0.9911931818,
-        "f":0.9921797242
-      },
-      "Voice":{
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-        "r":0.9731343284,
-        "f":0.9737572008
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-      "Degree":{
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-        "r":0.9256017505,
-        "f":0.9096774194
-      },
-      "Definite":{
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-        "r":0.998919503,
-        "f":0.9951560818
-      },
-      "Reflex":{
-        "p":1.0,
-        "r":1.0,
-        "f":1.0
-      },
-      "Poss":{
-        "p":1.0,
-        "r":0.6842105263,
-        "f":0.8125
-      }
-    },
-    "dep_uas":0.8082404613,
-    "dep_las":0.7619958171,
-    "dep_las_per_type":{
-      "nsubj":{
-        "p":0.752774079,
-        "r":0.7726651481,
-        "f":0.7625899281
-      },
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-        "r":0.7608695652,
-        "f":0.7486631016
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-      "orphan":{
-        "p":0.3043478261,
-        "r":0.1627906977,
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-        "r":0.3373493976,
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-        "f":0.0
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-      "aux:pass":{
-        "p":0.0,
-        "r":0.0,
-        "f":0.0
-      },
-      "fixed":{
-        "p":0.7142857143,
-        "r":0.5,
-        "f":0.5882352941
+    "tag_acc":0.0,
+    "pos_acc":0.0,
+    "morph_acc":0.0,
+    "morph_per_feat":0.0,
+    "dep_uas":0.0,
+    "dep_las":0.0,
+    "dep_las_per_type":0.0,
+    "sents_p":0.0,
+    "sents_r":0.0,
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