Tanor commited on
Commit
066a57e
1 Parent(s): cca44c5

Update spaCy pipeline

Browse files
.gitattributes CHANGED
@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ sr_pln_tesla_dbmu-any-py3-none-any.whl filter=lfs diff=lfs merge=lfs -text
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+ trainable_lemmatizer/model filter=lfs diff=lfs merge=lfs -text
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+ transformer/model filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,78 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ tags:
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+ - spacy
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+ - token-classification
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+ language:
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+ - sr
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+ license: cc-by-sa-3.0
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+ model-index:
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+ - name: sr_pln_tesla_dbmu
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+ results:
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+ - task:
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+ name: NER
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+ type: token-classification
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+ metrics:
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+ - name: NER Precision
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+ type: precision
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+ value: 0.9465813405
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+ - name: NER Recall
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+ type: recall
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+ value: 0.9506156136
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+ - name: NER F Score
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+ type: f_score
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+ value: 0.9485941877
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+ - task:
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+ name: TAG
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+ type: token-classification
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+ metrics:
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+ - name: TAG (XPOS) Accuracy
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+ type: accuracy
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+ value: 0.9815057009
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+ - task:
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+ name: LEMMA
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+ type: token-classification
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+ metrics:
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+ - name: Lemma Accuracy
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+ type: accuracy
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+ value: 0.9797101778
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+ ---
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+ sr_pln_tesla_dbmu is a spaCy model meticulously fine-tuned for Part-of-Speech Tagging, Lemmatization, and Named Entity Recognition in Serbian language texts. This advanced model incorporates a transformer layer based on distilbert/distilbert-base-multilingual-cased, enhancing its analytical capabilities. It is proficient in identifying 7 distinct categories of entities: PERS (persons), ROLE (professions), DEMO (demonyms), ORG (organizations), LOC (locations), WORK (artworks), and EVENT (events). Detailed information about these categories is available in the accompanying table. The development of this model has been made possible through the support of the Science Fund of the Republic of Serbia, under grant #7276, for the project 'Text Embeddings - Serbian Language Applications - TESLA'.
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+
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+ | Feature | Description |
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+ | --- | --- |
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+ | **Name** | `sr_pln_tesla_dbmu` |
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+ | **Version** | `1.0.0` |
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+ | **spaCy** | `>=3.7.2,<3.8.0` |
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+ | **Default Pipeline** | `transformer`, `tagger`, `trainable_lemmatizer`, `ner` |
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+ | **Components** | `transformer`, `tagger`, `trainable_lemmatizer`, `ner` |
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+ | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
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+ | **Sources** | n/a |
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+ | **License** | `CC BY-SA 3.0` |
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+ | **Author** | [Milica Ikonić Nešić, Saša Petalinkar, Mihailo Škorić, Ranka Stanković](https://tesla.rgf.bg.ac.rs/) |
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+
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+ ### Label Scheme
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+
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+ <details>
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+
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+ <summary>View label scheme (23 labels for 2 components)</summary>
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+
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+ | Component | Labels |
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+ | --- | --- |
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+ | **`tagger`** | `ADJ`, `ADP`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `SCONJ`, `VERB`, `X` |
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+ | **`ner`** | `DEMO`, `EVENT`, `LOC`, `ORG`, `PERS`, `ROLE`, `WORK` |
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+
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+ </details>
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+
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+ ### Accuracy
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+
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+ | Type | Score |
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+ | --- | --- |
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+ | `TAG_ACC` | 98.15 |
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+ | `LEMMA_ACC` | 97.97 |
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+ | `ENTS_F` | 94.86 |
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+ | `ENTS_P` | 94.66 |
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+ | `ENTS_R` | 95.06 |
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+ | `TRANSFORMER_LOSS` | 604959.76 |
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+ | `TAGGER_LOSS` | 359950.85 |
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+ | `TRAINABLE_LEMMATIZER_LOSS` | 466065.88 |
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+ | `NER_LOSS` | 175653.69 |
config.cfg ADDED
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+ [paths]
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+ train = "./train.spacy"
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+ dev = "./dev.spacy"
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+ vectors = null
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+ init_tok2vec = null
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+
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+ [system]
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+ gpu_allocator = "pytorch"
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+ seed = 0
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+
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+ [nlp]
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+ lang = "sr"
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+ pipeline = ["transformer","tagger","trainable_lemmatizer","ner"]
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+ batch_size = 128
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+ disabled = []
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+ before_creation = null
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+ after_creation = null
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+ after_pipeline_creation = null
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+ tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
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+ vectors = {"@vectors":"spacy.Vectors.v1"}
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+
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+ [components]
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+
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+ [components.ner]
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+ factory = "ner"
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+ incorrect_spans_key = null
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+ moves = null
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+ scorer = {"@scorers":"spacy.ner_scorer.v1"}
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+ update_with_oracle_cut_size = 100
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+
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+ [components.ner.model]
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+ @architectures = "spacy.TransitionBasedParser.v2"
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+ state_type = "ner"
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+ extra_state_tokens = false
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+ hidden_width = 64
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+ maxout_pieces = 2
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+ use_upper = false
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+ nO = null
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+
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+ [components.ner.model.tok2vec]
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+ @architectures = "spacy-transformers.TransformerListener.v1"
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+ grad_factor = 1.0
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+ pooling = {"@layers":"reduce_mean.v1"}
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+ upstream = "*"
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+
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+ [components.tagger]
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+ factory = "tagger"
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+ label_smoothing = 0.0
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+ neg_prefix = "!"
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+ overwrite = false
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+ scorer = {"@scorers":"spacy.tagger_scorer.v1"}
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+
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+ [components.tagger.model]
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+ @architectures = "spacy.Tagger.v2"
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+ nO = null
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+ normalize = false
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+
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+ [components.tagger.model.tok2vec]
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+ @architectures = "spacy-transformers.TransformerListener.v1"
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+ grad_factor = 1.0
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+ pooling = {"@layers":"reduce_mean.v1"}
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+ upstream = "*"
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+
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+ [components.trainable_lemmatizer]
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+ factory = "trainable_lemmatizer"
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+ backoff = "orth"
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+ min_tree_freq = 3
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+ overwrite = false
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+ scorer = {"@scorers":"spacy.lemmatizer_scorer.v1"}
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+ top_k = 1
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+
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+ [components.trainable_lemmatizer.model]
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+ @architectures = "spacy.Tagger.v2"
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+ nO = null
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+ normalize = false
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+
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+ [components.trainable_lemmatizer.model.tok2vec]
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+ @architectures = "spacy-transformers.TransformerListener.v1"
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+ grad_factor = 1.0
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+ pooling = {"@layers":"reduce_mean.v1"}
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+ upstream = "*"
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+
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+ [components.transformer]
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+ factory = "transformer"
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+ max_batch_items = 4096
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+ set_extra_annotations = {"@annotation_setters":"spacy-transformers.null_annotation_setter.v1"}
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+
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+ [components.transformer.model]
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+ @architectures = "spacy-transformers.TransformerModel.v3"
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+ name = "distilbert-base-multilingual-cased"
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+ mixed_precision = false
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+
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+ [components.transformer.model.get_spans]
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+ @span_getters = "spacy-transformers.strided_spans.v1"
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+ window = 128
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+ stride = 96
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+
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+ [components.transformer.model.grad_scaler_config]
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+
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+ [components.transformer.model.tokenizer_config]
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+ use_fast = true
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+
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+ [components.transformer.model.transformer_config]
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+
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+ [corpora]
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+
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+ [corpora.dev]
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+ @readers = "spacy.Corpus.v1"
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+ path = ${paths.dev}
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+ max_length = 0
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+ gold_preproc = false
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+ limit = 0
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+ augmenter = null
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+
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+ [corpora.train]
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+ @readers = "spacy.Corpus.v1"
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+ path = ${paths.train}
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+ max_length = 0
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+ gold_preproc = false
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+ limit = 0
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+ augmenter = null
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+
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+ [training]
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+ accumulate_gradient = 3
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+ dev_corpus = "corpora.dev"
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+ train_corpus = "corpora.train"
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+ annotating_components = ["tagger","trainable_lemmatizer"]
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+ seed = ${system.seed}
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+ gpu_allocator = ${system.gpu_allocator}
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+ dropout = 0.1
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+ patience = 1600
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+ max_epochs = 0
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+ max_steps = 20000
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+ eval_frequency = 200
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+ frozen_components = []
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+ before_to_disk = null
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+ before_update = null
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+
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+ [training.batcher]
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+ @batchers = "spacy.batch_by_padded.v1"
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+ discard_oversize = true
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+ size = 2000
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+ buffer = 256
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+ get_length = null
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+
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+ [training.logger]
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+ @loggers = "spacy.ConsoleLogger.v1"
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+ progress_bar = false
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+
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+ [training.optimizer]
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+ @optimizers = "Adam.v1"
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+ beta1 = 0.9
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+ beta2 = 0.999
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+ L2_is_weight_decay = true
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+ L2 = 0.01
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+ grad_clip = 1.0
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+ use_averages = false
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+ eps = 0.00000001
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+
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+ [training.optimizer.learn_rate]
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+ @schedules = "warmup_linear.v1"
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+ warmup_steps = 250
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+ total_steps = 20000
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+ initial_rate = 0.00005
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+
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+ [training.score_weights]
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+ tag_acc = 0.33
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+ lemma_acc = 0.33
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+ ents_f = 0.33
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+ ents_p = 0.0
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+ ents_r = 0.0
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+ ents_per_type = null
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+
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+ [pretraining]
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+
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+ [initialize]
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+ vectors = ${paths.vectors}
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+ init_tok2vec = ${paths.init_tok2vec}
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+ vocab_data = null
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+ lookups = null
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+ before_init = null
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+ after_init = null
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+
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+ [initialize.components]
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+
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+ [initialize.tokenizer]
meta.json ADDED
@@ -0,0 +1,116 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "lang":"sr",
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+ "name":"pln_tesla_dbmu",
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+ "version":"1.0.0",
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+ "description":"sr_pln_tesla_dbmu is a spaCy model meticulously fine-tuned for Part-of-Speech Tagging, Lemmatization, and Named Entity Recognition in Serbian language texts. This advanced model incorporates a transformer layer based on distilbert/distilbert-base-multilingual-cased, enhancing its analytical capabilities. It is proficient in identifying 7 distinct categories of entities: PERS (persons), ROLE (professions), DEMO (demonyms), ORG (organizations), LOC (locations), WORK (artworks), and EVENT (events). Detailed information about these categories is available in the accompanying table. The development of this model has been made possible through the support of the Science Fund of the Republic of Serbia, under grant #7276, for the project 'Text Embeddings - Serbian Language Applications - TESLA'.",
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+ "author":"Milica Ikoni\u0107 Ne\u0161i\u0107, Sa\u0161a Petalinkar, Mihailo \u0160kori\u0107, Ranka Stankovi\u0107",
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+ "email":"",
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+ "url":"https://tesla.rgf.bg.ac.rs/",
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+ "license":"CC BY-SA 3.0",
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+ "spacy_version":">=3.7.2,<3.8.0",
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+ "spacy_git_version":"a89eae928",
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+ "ADJ",
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+ "ADP",
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+ "ADV",
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+ "AUX",
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+ "CCONJ",
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+ "DET",
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+ "INTJ",
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+ "NOUN",
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+ "NUM",
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+ "PART",
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+ "PRON",
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+ "PROPN",
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+ "PUNCT",
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+ "SCONJ",
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+ "VERB",
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+ "X"
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+ ],
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+ "ner":[
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+ "DEMO",
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+ "EVENT",
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+ "LOC",
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+ "ORG",
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+ "PERS",
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+ "ROLE",
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+ "WORK"
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+ "pipeline":[
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+ "ner"
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+ "trainable_lemmatizer",
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+ "ner"
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+ "performance":{
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+ "ents_per_type":{
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+ "ROLE":{
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+ "LOC":{
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+ "transformer_loss":6049.5975609321,
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+ "trainable_lemmatizer_loss":4660.6587796062,
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+ "ner_loss":1756.5368891107
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+ },
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+ "requirements":[
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+ "spacy-transformers>=1.3.4,<1.4.0"
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+ ]
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+ }
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+ "beam_update_prob":0.0,
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+ "incorrect_spans_key":null
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+ {
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+ "labels":[
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+ "ADJ",
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+ "ADP",
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+ "ADV",
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+ "AUX",
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+ "CCONJ",
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+ "DET",
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+ "INTJ",
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+ "NUM",
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+ "PART",
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+ "VERB",
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tokenizer ADDED
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