Update spaCy pipeline

.gitattributes

@@ -25,3 +25,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.whl filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*strings.json filter=lfs diff=lfs merge=lfs -text
+vectors filter=lfs diff=lfs merge=lfs -text
+model filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,45 @@
+---
+tags:
+- spacy
+- token-classification
+language:
+- sv
+license: mit
+model-index:
+- name: sv_pipeline
+  results:
+  - task:
+      name: POS
+      type: token-classification
+    metrics:
+    - name: POS Accuracy
+      type: accuracy
+      value: 0.9818079056
+  - task:
+      name: SENTER
+      type: token-classification
+    metrics:
+    - name: SENTER Precision
+      type: precision
+      value: 0.9212548015
+    - name: SENTER Recall
+      type: recall
+      value: 0.9368489583
+    - name: SENTER F Score
+      type: f_score
+      value: 0.9289864429
+  - task:
+      name: UNLABELED_DEPENDENCIES
+      type: token-classification
+    metrics:
+    - name: Unlabeled Dependencies Accuracy
+      type: accuracy
+      value: 0.9198832946
+  - task:
+      name: LABELED_DEPENDENCIES
+      type: token-classification
+    metrics:
+    - name: Labeled Dependencies Accuracy
+      type: accuracy
+      value: 0.9198832946
+---

config.cfg

@@ -0,0 +1,207 @@
+[paths]
+train = "./data/sv_merged-upos-train.spacy"
+dev = "./data/sv_merged-upos-dev.spacy"
+vectors = null
+init_tok2vec = null
+
+[system]
+gpu_allocator = "pytorch"
+seed = 0
+
+[nlp]
+lang = "sv"
+pipeline = ["transformer","tagger","parser","transformer_ner","ner","attribute_ruler","lemmatizer"]
+batch_size = 128
+disabled = []
+before_creation = null
+after_creation = null
+after_pipeline_creation = null
+tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
+
+[components]
+
+[components.attribute_ruler]
+factory = "attribute_ruler"
+validate = false
+
+[components.lemmatizer]
+factory = "lemmatizer"
+mode = "lookup"
+model = null
+overwrite = false
+
+[components.ner]
+factory = "ner"
+moves = null
+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
+min_action_freq = 30
+moves = null
+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 = false
+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"
+
+[components.tagger.model]
+@architectures = "spacy.Tagger.v1"
+nO = null
+
+[components.tagger.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.v1"
+name = "KB/bert-base-swedish-cased"
+
+[components.transformer.model.get_spans]
+@span_getters = "spacy-transformers.strided_spans.v1"
+window = 128
+stride = 96
+
+[components.transformer.model.tokenizer_config]
+use_fast = true
+
+[components.transformer_ner]
+factory = "transformer"
+max_batch_items = 4096
+set_extra_annotations = {"@annotation_setters":"spacy-transformers.null_annotation_setter.v1"}
+
+[components.transformer_ner.model]
+@architectures = "spacy-transformers.TransformerModel.v1"
+name = "KB/bert-base-swedish-cased"
+
+[components.transformer_ner.model.get_spans]
+@span_getters = "spacy-transformers.strided_spans.v1"
+window = 128
+stride = 96
+
+[components.transformer_ner.model.tokenizer_config]
+use_fast = true
+
+[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 = 500
+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 = []
+before_to_disk = null
+
+[training.batcher]
+@batchers = "spacy.batch_by_padded.v1"
+discard_oversize = true
+size = 2000
+buffer = 256
+get_length = null
+
+[training.logger]
+@loggers = "spacy.ConsoleLogger.v1"
+progress_bar = false
+
+[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 = false
+eps = 0.00000001
+
+[training.optimizer.learn_rate]
+@schedules = "warmup_linear.v1"
+warmup_steps = 250
+total_steps = 20000
+initial_rate = 0.00005
+
+[training.score_weights]
+dep_las_per_type = null
+sents_p = null
+sents_r = null
+ents_per_type = null
+tag_acc = 0.25
+dep_uas = 0.12
+dep_las = 0.12
+sents_f = 0.0
+ents_f = 0.25
+ents_p = 0.0
+ents_r = 0.0
+lemma_acc = 0.25
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+[pretraining]
+
+[initialize]
+vectors = null
+init_tok2vec = ${paths.init_tok2vec}
+vocab_data = null
+lookups = null
+before_init = null
+after_init = null
+
+[initialize.components]
+
+[initialize.tokenizer]

lemmatizer/lookups/lookups.bin

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meta.json

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+{
+  "lang":"sv",
+  "name":"pipeline",
+  "version":"0.0.0",
+  "description":"This is a transformer-based pipeline for Swedish, which includes tagger, parser, ner and lemmatizer modules. The model was trained on UD Treebanks and the SUC 3.0 dataset at the National Library of Sweden by KBLab. Warning: the lemmatizer is not based on a statistical model and its performance is a bit erratic, so use at your own risk!",
+  "author":"KBLab",
+  "email":"elena.fano@kb.se",
+  "url":"https://kb-labb.github.io/",
+  "license":"mit",
+  "spacy_version":">=3.0.6,<3.1.0",
+  "spacy_git_version":"91a3cab1c",
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+    "name":null
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+      "ADJ",
+      "ADP",
+      "ADV",
+      "AUX",
+      "CCONJ",
+      "DET",
+      "INTJ",
+      "NOUN",
+      "NUM",
+      "PART",
+      "PRON",
+      "PROPN",
+      "PUNCT",
+      "SCONJ",
+      "SYM",
+      "VERB",
+      "X"
+    ],
+    "parser":[
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ner/cfg

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ner/moves

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parser/cfg

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tagger/cfg

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+{
+  "labels":[
+    "ADJ",
+    "ADP",
+    "ADV",
+    "AUX",
+    "CCONJ",
+    "DET",
+    "INTJ",
+    "NOUN",
+    "NUM",
+    "PART",
+    "PRON",
+    "PROPN",
+    "PUNCT",
+    "SCONJ",
+    "SYM",
+    "VERB",
+    "X"
+  ]
+}

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tokenizer

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