Update spaCy pipeline

.gitattributes

@@ -32,3 +32,11 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+frequency_lemmatizer/table.json filter=lfs diff=lfs merge=lfs -text
+grc_odycy_joint_trf-any-py3-none-any.whl filter=lfs diff=lfs merge=lfs -text
+morphologizer/model filter=lfs diff=lfs merge=lfs -text
+parser/model filter=lfs diff=lfs merge=lfs -text
+tagger/model filter=lfs diff=lfs merge=lfs -text
+trainable_lemmatizer/model filter=lfs diff=lfs merge=lfs -text
+trainable_lemmatizer/trees filter=lfs diff=lfs merge=lfs -text
+transformer/model filter=lfs diff=lfs merge=lfs -text

config.cfg

@@ -0,0 +1,228 @@
+[paths]
+train = "corpus/joint/train.spacy"
+test = "corpus/test.spacy"
+dev = "corpus/joint/dev.spacy"
+vectors = null
+init_tok2vec = null
+
+[system]
+gpu_allocator = "pytorch"
+seed = 0
+
+[nlp]
+lang = "grc"
+pipeline = ["transformer","tagger","morphologizer","parser","trainable_lemmatizer","frequency_lemmatizer"]
+batch_size = 1024
+disabled = []
+before_creation = null
+after_creation = null
+after_pipeline_creation = null
+tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
+
+[components]
+
+[components.frequency_lemmatizer]
+factory = "frequency_lemmatizer"
+fallback_priority = "lookup"
+overwrite = true
+
+[components.morphologizer]
+factory = "morphologizer"
+extend = false
+overwrite = true
+scorer = {"@scorers":"spacy.morphologizer_scorer.v1"}
+
+[components.morphologizer.model]
+@architectures = "spacy.Tagger.v2"
+nO = null
+normalize = false
+
+[components.morphologizer.model.tok2vec]
+@architectures = "spacy-transformers.TransformerListener.v1"
+grad_factor = 1.0
+pooling = {"@layers":"reduce_mean.v1"}
+upstream = "*"
+
+[components.parser]
+factory = "parser"
+learn_tokens = false
+min_action_freq = 30
+moves = null
+scorer = {"@scorers":"spacy.parser_scorer.v1"}
+update_with_oracle_cut_size = 100
+
+[components.parser.model]
+@architectures = "spacy.TransitionBasedParser.v2"
+state_type = "parser"
+extra_state_tokens = false
+hidden_width = 128
+maxout_pieces = 3
+use_upper = true
+nO = null
+
+[components.parser.model.tok2vec]
+@architectures = "spacy-transformers.TransformerListener.v1"
+grad_factor = 1.0
+pooling = {"@layers":"reduce_mean.v1"}
+upstream = "*"
+
+[components.tagger]
+factory = "tagger"
+neg_prefix = "!"
+overwrite = false
+scorer = {"@scorers":"spacy.tagger_scorer.v1"}
+
+[components.tagger.model]
+@architectures = "spacy.Tagger.v2"
+nO = null
+normalize = false
+
+[components.tagger.model.tok2vec]
+@architectures = "spacy-transformers.TransformerListener.v1"
+grad_factor = 1.0
+pooling = {"@layers":"reduce_mean.v1"}
+upstream = "*"
+
+[components.trainable_lemmatizer]
+factory = "trainable_lemmatizer"
+backoff = "orth"
+min_tree_freq = 1
+overwrite = false
+scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
+top_k = 3
+
+[components.trainable_lemmatizer.model]
+@architectures = "spacy.Tagger.v2"
+nO = null
+normalize = false
+
+[components.trainable_lemmatizer.model.tok2vec]
+@architectures = "spacy-transformers.TransformerListener.v1"
+grad_factor = 1.0
+pooling = {"@layers":"reduce_mean.v1"}
+upstream = "*"
+
+[components.transformer]
+factory = "transformer"
+max_batch_items = 4096
+set_extra_annotations = {"@annotation_setters":"spacy-transformers.null_annotation_setter.v1"}
+
+[components.transformer.model]
+@architectures = "spacy-transformers.TransformerModel.v3"
+name = "pranaydeeps/Ancient-Greek-BERT"
+mixed_precision = false
+
+[components.transformer.model.get_spans]
+@span_getters = "spacy-transformers.strided_spans.v1"
+window = 128
+stride = 96
+
+[components.transformer.model.grad_scaler_config]
+
+[components.transformer.model.tokenizer_config]
+use_fast = true
+
+[components.transformer.model.transformer_config]
+
+[corpora]
+
+[corpora.dev]
+@readers = "spacy.Corpus.v1"
+path = ${paths.dev}
+max_length = 0
+gold_preproc = false
+limit = 0
+augmenter = null
+
+[corpora.train]
+@readers = "spacy.Corpus.v1"
+path = ${paths.train}
+max_length = 0
+gold_preproc = false
+limit = 0
+augmenter = null
+
+[training]
+accumulate_gradient = 3
+dev_corpus = "corpora.dev"
+train_corpus = "corpora.train"
+seed = ${system.seed}
+gpu_allocator = ${system.gpu_allocator}
+dropout = 0.1
+patience = 1600
+max_epochs = 0
+max_steps = 20000
+eval_frequency = 200
+frozen_components = []
+annotating_components = []
+before_to_disk = null
+before_update = null
+
+[training.batcher]
+@batchers = "spacy.batch_by_padded.v1"
+discard_oversize = true
+size = 2000
+buffer = 256
+get_length = null
+
+[training.logger]
+@loggers = "spacy.WandbLogger.v3"
+project_name = "homerCy"
+remove_config_values = []
+model_log_interval = null
+log_dataset_dir = null
+entity = null
+run_name = null
+
+[training.optimizer]
+@optimizers = "Adam.v1"
+beta1 = 0.9
+beta2 = 0.999
+L2_is_weight_decay = true
+L2 = 0.01
+grad_clip = 1.0
+use_averages = true
+eps = 0.00000001
+
+[training.optimizer.learn_rate]
+@schedules = "warmup_linear.v1"
+warmup_steps = 250
+total_steps = 20000
+initial_rate = 0.00005
+
+[training.score_weights]
+tag_acc = 0.21
+pos_acc = 0.1
+morph_acc = 0.1
+morph_per_feat = null
+dep_uas = 0.1
+dep_las = 0.1
+dep_las_per_type = null
+sents_p = null
+sents_r = null
+sents_f = 0.0
+lemma_acc = 0.4
+
+[pretraining]
+
+[initialize]
+vectors = ${paths.vectors}
+init_tok2vec = ${paths.init_tok2vec}
+vocab_data = null
+lookups = null
+before_init = null
+after_init = null
+
+[initialize.components]
+
+[initialize.components.frequency_lemmatizer]
+
+[initialize.components.frequency_lemmatizer.lookup]
+@readers = "srsly.read_json.v1"
+path = "assets/lemmas/lemma_lookup.json"
+
+[initialize.components.frequency_lemmatizer.table]
+@readers = "srsly.read_json.v1"
+path = "assets/lemmas/lemma_table.json"
+
+[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_odycy_joint_trf-any-py3-none-any.whl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e72e0180d0f56c1ace27f38f3c147c04190263fecc8d31d030662dfcc0f83164
+size 497308444

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

morphologizer/model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0eeff40f67250aacdbb0c93c0815becad2b1e400f71d06063e672a9994807576
+size 4408561

parser/cfg

@@ -0,0 +1,13 @@
+{
+  "moves":null,
+  "update_with_oracle_cut_size":100,
+  "multitasks":[
+
+  ],
+  "min_action_freq":30,
+  "learn_tokens":false,
+  "beam_width":1,
+  "beam_density":0.0,
+  "beam_update_prob":0.0,
+  "incorrect_spans_key":null
+}

parser/model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e292087fce74cce8ddabe32dcf0e55ff588fcc5caa2b6e460c81aaf79a59ef58
+size 2075321

parser/moves

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tagger/cfg

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+{
+  "labels":[
+    "---------",
+    "--p---fa-",
+    "--s---ma-",
+    "-3paia---",
+    "-3paim---",
+    "-3siia---",
+    "A-",
+    "C-",
+    "Df",
+    "Dq",
+    "Du",
+    "F-",
+    "G-",
+    "I-",
+    "Ma",
+    "Mo",
+    "Nb",
+    "Ne",
+    "Pc",
+    "Pd",
+    "Pi",
+    "Pk",
+    "Pp",
+    "Pr",
+    "Ps",
+    "Px",
+    "R-",
+    "S-",
+    "V-",
+    "a--------",
+    "a-------s",
+    "a-d---fa-",
+    "a-d---fd-",
+    "a-d---fg-",
+    "a-d---fn-",
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+    "a-d---mg-",
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+    "a-d---ng-",
+    "a-d---nn-",
+    "a-p----dc",
+    "a-p---fa-",
+    "a-p---fac",
+    "a-p---fas",
+    "a-p---fd-",
+    "a-p---fdc",
+    "a-p---fds",
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+    "a-p---m--",
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+    "a-p---mac",
+    "a-p---mas",
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+    "p2s---md-",
+    "p2s---mg-",
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+    "p3s---ma-",
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+    "v--an----",
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+    "v1paip---",
+    "v1paoa---",
+    "v1paom---",
+    "v1paop---",
+    "v1pasa---",
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+    "v1pasp---",
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+    "v1pfom---",
+    "v1piia---",
+    "v1piie---",
+    "v1plia---",
+    "v1plie---",
+    "v1ppia---",
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+    "v1ppip---",
+    "v1ppoa---",
+    "v1ppoe---",
+    "v1ppsa---",
+    "v1ppse---",
+    "v1pria---",
+    "v1prie---",
+    "v1prsa---",
+    "v1prse---",
+    "v1ptie---",
+    "v1s-sa---",
+    "v1sa-a---",
+    "v1saia---",
+    "v1saie---",
+    "v1saim---",
+    "v1saip---",
+    "v1sao----",
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+    "v1saoe---",
+    "v1saom---",
+    "v1saop---",
+    "v1sasa---",
+    "v1sase---",
+    "v1sasm---",
+    "v1sasp---",
+    "v1sfi----",
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+    "v1sfim---",
+    "v1sfip---",
+    "v1siia---",
+    "v1siie---",
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+    "v1slie---",
+    "v1slim---",
+    "v1spia---",
+    "v1spie---",
+    "v1spoa---",
+    "v1spoe---",
+    "v1spsa---",
+    "v1spse---",
+    "v1sria---",
+    "v1srie---",
+    "v1sroa---",
+    "v1sroe---",
+    "v1srsa---",
+    "v1stie---",
+    "v1stim---",
+    "v2daia---",
+    "v2dama---",
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+    "v2dase---",
+    "v2dfia---",
+    "v2dfim---",
+    "v2diia---",
+    "v2diie---",
+    "v2dpia---",
+    "v2dpma---",
+    "v2dpme---",
+    "v2dria---",
+    "v2drma---",
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+    "v2paim---",
+    "v2paip---",
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+    "v2paom---",
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+    "v2piia---",
+    "v2piie---",
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+    "v2ppie---",
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+    "v2ppoe---",
+    "v2ppsa---",
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+    "v2prie---",
+    "v2prma---",
+    "v2prmp---",
+    "v2proa---",
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+    "v2saia---",
+    "v2saie---",
+    "v2saim---",
+    "v2saip---",
+    "v2sam----",
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+    "v2samm---",
+    "v2samp---",
+    "v2saoa---",
+    "v2saoe---",
+    "v2saom---",
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+    "v2sasp---",
+    "v2sfi----",
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+    "v2sfip---",
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+    "v2spie---",
+    "v2spma---",
+    "v2spme---",
+    "v2spoa---",
+    "v2spoe---",
+    "v2spsa---",
+    "v2spse---",
+    "v2sria---",
+    "v2srie---",
+    "v2srma---",
+    "v2srme---",
+    "v2sroa---",
+    "v2srsa---",
+    "v2stie---",
+    "v3-roe---",
+    "v3daia---",
+    "v3daim---",
+    "v3daip---",
+    "v3daoa---",
+    "v3dfia---",
+    "v3dfim---",
+    "v3diia---",
+    "v3diie---",
+    "v3dlia---",
+    "v3dlie---",
+    "v3dlim---",
+    "v3dpia---",
+    "v3dpie---",
+    "v3dpma---",
+    "v3dpme---",
+    "v3dpsa---",
+    "v3dria---",
+    "v3pai----",
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+    "v3paip---",
+    "v3pamm---",
+    "v3paoa---",
+    "v3paoe---",
+    "v3paom---",
+    "v3paop---",
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+    "v3pfia---",
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+    "v3piia---",
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+    "v3ppme---",
+    "v3ppoa---",
+    "v3ppoe---",
+    "v3ppsa---",
+    "v3ppse---",
+    "v3pria---",
+    "v3prie---",
+    "v3prip---",
+    "v3sai----",
+    "v3saia---",
+    "v3saie---",
+    "v3saim---",
+    "v3saip---",
+    "v3sama---",
+    "v3samm---",
+    "v3samp---",
+    "v3sana---",
+    "v3sao----",
+    "v3saoa---",
+    "v3saoe---",
+    "v3saom---",
+    "v3saop---",
+    "v3sas----",
+    "v3sasa---",
+    "v3sase---",
+    "v3sasm---",
+    "v3sasp---",
+    "v3sfi----",
+    "v3sfia---",
+    "v3sfie---",
+    "v3sfim---",
+    "v3sfip---",
+    "v3sfoa---",
+    "v3sii----",
+    "v3siia---",
+    "v3siie---",
+    "v3siip---",
+    "v3sli----",
+    "v3slia---",
+    "v3slie---",
+    "v3slim---",
+    "v3slip---",
+    "v3spia---",
+    "v3spie---",
+    "v3spip---",
+    "v3spma---",
+    "v3spme---",
+    "v3spoa---",
+    "v3spoe---",
+    "v3spop---",
+    "v3spsa---",
+    "v3spse---",
+    "v3sria---",
+    "v3srie---",
+    "v3srip---",
+    "v3srma---",
+    "v3sroa---",
+    "v3srsa---",
+    "v3stie---",
+    "v3stim---",
+    "v3stip---",
+    "x--------",
+    "x-p----d-",
+    "x-p---nn-"
+  ],
+  "neg_prefix":"!",
+  "overwrite":false
+}

tagger/model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a0a79e1afe28e4f918648ba940fd9ba2d4cc72dc0e051237590a2f73e90a2af1
+size 2562961

tokenizer

@@ -0,0 +1,4 @@
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