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.gitattributes CHANGED
@@ -33,3 +33,4 @@ 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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+ medical-ner-task3/ner/model filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,28 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ tags:
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+ - spacy
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+ - ner
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+ - medical-ner
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+ library_name: spacy
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+ pipeline_tag: ner
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+ language: en
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+ license: mit
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+ ---
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+
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+ # Medical NER Model - medical-ner-task3
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+
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+ This model is trained to recognize medical entities including treatments, chronic diseases, cancers, and allergies.
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+
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+ ## Model Details
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+ - Task: medical-ner-task3
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+ - Framework: spaCy
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+ - Entity Types: TREATMENT, CHRONIC DISEASE, CANCER, ALLERGY, OTHER
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+
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+ ## Usage
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+ ```python
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+ import spacy
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+ nlp = spacy.load("nitinyadav/continual_learning_ner_task3")
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+ doc = nlp("Patient has been diagnosed with Type 2 Diabetes")
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+ for ent in doc.ents:
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+ print(ent.text, ent.label_)
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+ ```
medical-ner-task3/config.cfg ADDED
@@ -0,0 +1,130 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ [paths]
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+ train = null
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+ dev = null
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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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+ seed = 0
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+ gpu_allocator = null
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+
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+ [nlp]
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+ lang = "en"
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+ pipeline = ["ner"]
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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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+ batch_size = 1000
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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 = true
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+ nO = null
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+
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+ [components.ner.model.tok2vec]
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+ @architectures = "spacy.HashEmbedCNN.v2"
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+ pretrained_vectors = null
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+ width = 96
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+ depth = 4
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+ embed_size = 2000
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+ window_size = 1
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+ maxout_pieces = 3
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+ subword_features = true
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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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+ gold_preproc = false
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+ max_length = 0
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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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+ gold_preproc = false
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+ max_length = 0
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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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+ seed = ${system.seed}
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+ gpu_allocator = ${system.gpu_allocator}
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+ dropout = 0.1
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+ accumulate_gradient = 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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+ annotating_components = []
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+ dev_corpus = "corpora.dev"
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+ train_corpus = "corpora.train"
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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_words.v1"
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+ discard_oversize = false
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+ tolerance = 0.2
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+ get_length = null
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+
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+ [training.batcher.size]
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+ @schedules = "compounding.v1"
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+ start = 100
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+ stop = 1000
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+ compound = 1.001
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+ t = 0.0
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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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+ learn_rate = 0.001
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+
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+ [training.score_weights]
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+ ents_f = 1.0
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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]
medical-ner-task3/meta.json ADDED
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+ {
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+ "lang":"en",
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+ "name":"pipeline",
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+ "version":"0.0.0",
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+ "spacy_version":">=3.7.5,<3.8.0",
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+ "description":"",
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+ "author":"",
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+ "email":"",
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+ "url":"",
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+ "license":"",
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+ "spacy_git_version":"a6d0fc360",
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+ "vectors":{
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+ "width":0,
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+ "vectors":0,
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+ "keys":0,
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+ "name":null,
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+ "mode":"default"
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+ },
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+ "labels":{
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+ "ner":[
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+ "ALLERGY",
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+ "CANCER",
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+ "CHRONIC DISEASE",
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+ "TREATMENT"
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+ ]
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+ },
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+ "pipeline":[
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+ "ner"
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+ ],
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+ "components":[
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+ "ner"
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+ ],
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+ "disabled":[
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+
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+ ]
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+ }
medical-ner-task3/ner/cfg ADDED
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+ {
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+ "moves":null,
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+ "update_with_oracle_cut_size":100,
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+ "multitasks":[
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+
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+ ],
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+ "min_action_freq":1,
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+ "learn_tokens":false,
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+ "beam_width":1,
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+ "beam_density":0.0,
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+ "beam_update_prob":0.0,
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+ "incorrect_spans_key":null
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+ }
medical-ner-task3/ner/model ADDED
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1106
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1107
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1108
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1109
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1110
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1111
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1112
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1113
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1115
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1116
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1117
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1118
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1120
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1121
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1122
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1125
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1126
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1127
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1177
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1179
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1180
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1181
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1182
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1183
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1185
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1186
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1187
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1188
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1189
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1190
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1191
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1192
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1193
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1194
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1195
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1196
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1197
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1198
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1199
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1200
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1201
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1202
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1220
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1221
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1222
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1224
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1225
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1226
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1227
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1228
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1229
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1230
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1231
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1232
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1233
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1300
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1301
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1302
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1303
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1304
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1305
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1306
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1307
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1309
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1311
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+ "ven",
4846
+ "vena",
4847
+ "veno",
4848
+ "venous",
4849
+ "ventilator",
4850
+ "ventilatory",
4851
+ "ventricular",
4852
+ "ver",
4853
+ "verified",
4854
+ "version",
4855
+ "versus",
4856
+ "ves",
4857
+ "vessel",
4858
+ "vest",
4859
+ "via",
4860
+ "viagra",
4861
+ "vic",
4862
+ "view",
4863
+ "vii",
4864
+ "vin",
4865
+ "violence",
4866
+ "vir",
4867
+ "viral",
4868
+ "virilizing",
4869
+ "virus",
4870
+ "vis",
4871
+ "visceral",
4872
+ "visible",
4873
+ "visit",
4874
+ "visual",
4875
+ "vital",
4876
+ "vitamin",
4877
+ "vitiligo",
4878
+ "vivitrol",
4879
+ "vix",
4880
+ "volume",
4881
+ "volunteers",
4882
+ "vomiting",
4883
+ "voriconazole",
4884
+ "vs",
4885
+ "vs.",
4886
+ "vu",
4887
+ "w",
4888
+ "w's",
4889
+ "w.",
4890
+ "w/o",
4891
+ "wagner",
4892
+ "wait",
4893
+ "waiting",
4894
+ "waldenstr\u00f6m",
4895
+ "war",
4896
+ "warfarin",
4897
+ "warned",
4898
+ "warts",
4899
+ "was",
4900
+ "wash",
4901
+ "wash.",
4902
+ "washout",
4903
+ "way",
4904
+ "wbc",
4905
+ "we",
4906
+ "wed",
4907
+ "wee",
4908
+ "week",
4909
+ "weeks",
4910
+ "weight",
4911
+ "wel",
4912
+ "well",
4913
+ "were",
4914
+ "what",
4915
+ "what's",
4916
+ "what\u2019s",
4917
+ "when",
4918
+ "when's",
4919
+ "when\u2019s",
4920
+ "where",
4921
+ "where's",
4922
+ "where\u2019s",
4923
+ "whether",
4924
+ "which",
4925
+ "whichever",
4926
+ "while",
4927
+ "whiplash",
4928
+ "white",
4929
+ "who",
4930
+ "who's",
4931
+ "whole",
4932
+ "whom",
4933
+ "whose",
4934
+ "who\u2019s",
4935
+ "why",
4936
+ "why's",
4937
+ "why\u2019s",
4938
+ "will",
4939
+ "willing",
4940
+ "wire",
4941
+ "wis",
4942
+ "wis.",
4943
+ "with",
4944
+ "within",
4945
+ "without",
4946
+ "wly",
4947
+ "wo",
4948
+ "wocbp",
4949
+ "wolfe",
4950
+ "women",
4951
+ "work",
4952
+ "working",
4953
+ "worse",
4954
+ "wort",
4955
+ "would",
4956
+ "wound",
4957
+ "wounds",
4958
+ "wpw",
4959
+ "wrist",
4960
+ "wth",
4961
+ "w\u2019s",
4962
+ "x",
4963
+ "x'",
4964
+ "x'x",
4965
+ "x'xxxx",
4966
+ "x.",
4967
+ "x.X",
4968
+ "x.d",
4969
+ "x.x",
4970
+ "x.x.",
4971
+ "x/x",
4972
+ "xD",
4973
+ "xDD",
4974
+ "xX",
4975
+ "xXX",
4976
+ "xXXX",
4977
+ "xXXXX",
4978
+ "x^d",
4979
+ "x_X",
4980
+ "x_d",
4981
+ "x_x",
4982
+ "xam",
4983
+ "xd",
4984
+ "xdd",
4985
+ "xed",
4986
+ "xel",
4987
+ "xen",
4988
+ "xia",
4989
+ "xic",
4990
+ "xin",
4991
+ "xis",
4992
+ "xon",
4993
+ "xx",
4994
+ "xx'",
4995
+ "xx'x",
4996
+ "xx'xx",
4997
+ "xx.",
4998
+ "xx/ddd",
4999
+ "xxXx",
5000
+ "xxd",
5001
+ "xxx",
5002
+ "xxx'x",
5003
+ "xxx/d.dd",
5004
+ "xxx/d.ddxd",
5005
+ "xxx/xx",
5006
+ "xxxdd",
5007
+ "xxxx",
5008
+ "xxxx'",
5009
+ "xxxx'x",
5010
+ "xxxx(x",
5011
+ "xxxx)xxxx",
5012
+ "xxxx-",
5013
+ "xxxx/",
5014
+ "xxxx\u2019",
5015
+ "xxxx\u2019x",
5016
+ "xxx\u2019x",
5017
+ "xx\u2019",
5018
+ "xx\u2019x",
5019
+ "xx\u2019xx",
5020
+ "x\u2019",
5021
+ "x\u2019x",
5022
+ "x\u2019xxxx",
5023
+ "x\ufe35x",
5024
+ "y",
5025
+ "y'",
5026
+ "y's",
5027
+ "y.",
5028
+ "year",
5029
+ "years",
5030
+ "yed",
5031
+ "yes",
5032
+ "yle",
5033
+ "york",
5034
+ "you",
5035
+ "your",
5036
+ "ype",
5037
+ "ysm",
5038
+ "yst",
5039
+ "yte",
5040
+ "y\u2019",
5041
+ "y\u2019s",
5042
+ "z",
5043
+ "z.",
5044
+ "zed",
5045
+ "|",
5046
+ "}",
5047
+ "\u00a0",
5048
+ "\u00ac",
5049
+ "\u00ac_\u00ac",
5050
+ "\u00ae",
5051
+ "\u00af",
5052
+ "\u00af\\(x)/\u00af",
5053
+ "\u00af\\(\u30c4)/\u00af",
5054
+ "\u00b0",
5055
+ "\u00b0C.",
5056
+ "\u00b0F.",
5057
+ "\u00b0K.",
5058
+ "\u00b0X.",
5059
+ "\u00b0c.",
5060
+ "\u00b0f.",
5061
+ "\u00b0k.",
5062
+ "\u00b0x.",
5063
+ "\u00e4",
5064
+ "\u00e4.",
5065
+ "\u00efve",
5066
+ "\u00f6",
5067
+ "\u00f6.",
5068
+ "\u00fc",
5069
+ "\u00fc.",
5070
+ "\u02c3",
5071
+ "\u02c33",
5072
+ "\u02c3d",
5073
+ "\u03bc",
5074
+ "\u03bcL",
5075
+ "\u03bcl",
5076
+ "\u0ca0",
5077
+ "\u0ca0_\u0ca0",
5078
+ "\u0ca0\ufe35\u0ca0",
5079
+ "\u2014",
5080
+ "\u2018",
5081
+ "\u2018S",
5082
+ "\u2018X",
5083
+ "\u2018s",
5084
+ "\u2018x",
5085
+ "\u2019",
5086
+ "\u2019-(",
5087
+ "\u2019-)",
5088
+ "\u2019Cause",
5089
+ "\u2019Cos",
5090
+ "\u2019Coz",
5091
+ "\u2019Cuz",
5092
+ "\u2019S",
5093
+ "\u2019X",
5094
+ "\u2019Xxx",
5095
+ "\u2019Xxxxx",
5096
+ "\u2019am",
5097
+ "\u2019bout",
5098
+ "\u2019cause",
5099
+ "\u2019cos",
5100
+ "\u2019coz",
5101
+ "\u2019cuz",
5102
+ "\u2019d",
5103
+ "\u2019em",
5104
+ "\u2019ll",
5105
+ "\u2019m",
5106
+ "\u2019nuff",
5107
+ "\u2019re",
5108
+ "\u2019s",
5109
+ "\u2019ve",
5110
+ "\u2019x",
5111
+ "\u2019xx",
5112
+ "\u2019xxx",
5113
+ "\u2019xxxx",
5114
+ "\u2019y",
5115
+ "\u2019\u2019",
5116
+ "\u2122",
5117
+ "\u2264",
5118
+ "\u226410",
5119
+ "\u226415",
5120
+ "\u2264dd",
5121
+ "\u2265",
5122
+ "\u22651",
5123
+ "\u22652",
5124
+ "\u226550Gy",
5125
+ "\u226550gy",
5126
+ "\u2265d",
5127
+ "\u2265ddXx",
5128
+ "\u2501",
5129
+ "\u253b",
5130
+ "\u253b\u2501\u253b",
5131
+ "\u256f",
5132
+ "\u25a1",
5133
+ "\ufe35",
5134
+ "\uff09"
5135
+ ]
medical-ner-task3/vocab/vectors ADDED
Binary file (128 Bytes). View file
 
medical-ner-task3/vocab/vectors.cfg ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ {
2
+ "mode":"default"
3
+ }