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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-task1/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-task1
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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-task1
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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_task1")
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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-task1/config.cfg ADDED
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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-task1/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-task1/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-task1/ner/model ADDED
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1003
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1039
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1051
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1095
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1103
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1120
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1121
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1122
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1123
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1124
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1125
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1126
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1127
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1128
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1129
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1132
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1133
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1200
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1301
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1834
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1835
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1836
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1837
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1838
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1841
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1842
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1843
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1844
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1846
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1847
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1848
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1849
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1850
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1851
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1852
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1853
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1854
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1858
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1859
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1860
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1861
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1862
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1863
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1864
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1865
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1866
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1867
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1868
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1869
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1870
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1871
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1872
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1873
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1874
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1875
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1876
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1877
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1878
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1879
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1880
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1881
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1882
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1883
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1884
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1885
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1886
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1887
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1888
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1889
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1890
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1891
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1892
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1893
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1894
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1897
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1901
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1909
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1911
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1912
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1913
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1914
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1915
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1916
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1917
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1918
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1919
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1920
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1921
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1922
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1923
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1924
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1926
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1927
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1928
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1929
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1930
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1931
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1932
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1933
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1934
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1938
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1939
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1951
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1955
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1956
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1957
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1959
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1960
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1962
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1964
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1965
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1966
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1968
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1969
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1970
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1971
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1972
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1973
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1974
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1975
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1976
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1977
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1978
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1979
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1980
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1981
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1982
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1983
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1984
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1985
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1986
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1987
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1988
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1989
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1990
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1991
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1992
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1993
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1994
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1995
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1996
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1997
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1998
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1999
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2000
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2001
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2002
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2003
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2004
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2005
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2006
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2007
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2008
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2009
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2010
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2011
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2012
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2013
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2014
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2015
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2016
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2017
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2018
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2019
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2020
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2021
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2022
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2023
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2024
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2025
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2026
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2027
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2028
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2029
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2030
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2031
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2032
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2033
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2034
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2035
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2036
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2037
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2038
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2039
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2040
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2041
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2042
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2043
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2044
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2045
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2046
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2047
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2048
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2049
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2050
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2051
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2052
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2053
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2054
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2055
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2056
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2057
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2058
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2059
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2060
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2061
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2062
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2063
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2064
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2065
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2066
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2067
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2068
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2069
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2070
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2071
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2072
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2073
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2074
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2075
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2076
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2077
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2078
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2079
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2080
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2081
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2082
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2083
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2084
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2085
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2086
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2087
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2088
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2089
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2090
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2091
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2092
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2093
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2094
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2095
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2096
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2097
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2098
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2099
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2100
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2101
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2102
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2103
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2104
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2105
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2106
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2107
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2108
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2109
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2110
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2111
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2112
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2113
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2114
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2115
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2116
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2117
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2118
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2119
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2120
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2121
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2122
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2123
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2124
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2125
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2126
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2127
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2128
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2129
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2130
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2131
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2132
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2133
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2134
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2135
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2136
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2137
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2138
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2139
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2140
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2141
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2142
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2143
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2144
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2145
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2146
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2147
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2148
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2149
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2150
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2151
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2152
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2153
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2154
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2155
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2156
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2157
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2158
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2159
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2160
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2161
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2162
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2163
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2164
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2165
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2166
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2167
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2168
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2169
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2170
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2171
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2172
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2173
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2174
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2175
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2176
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2177
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2178
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2179
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2180
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2181
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2182
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2183
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2184
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2185
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2186
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2187
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2188
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2189
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2190
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2191
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2192
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2193
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2194
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2195
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2196
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2197
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2198
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2199
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2200
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2201
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2202
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2203
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2204
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2205
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2206
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2207
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2208
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2209
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2210
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2211
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2212
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2213
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2214
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2215
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2216
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2217
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2218
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2219
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2220
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2221
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2222
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2223
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2224
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2225
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2226
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2227
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2228
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2229
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2230
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2231
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2232
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2233
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2234
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2235
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2236
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2237
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2238
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2239
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2240
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2241
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2242
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2243
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2244
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2245
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2246
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2247
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2248
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2249
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2250
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2251
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2252
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2253
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2254
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2255
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2256
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2257
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2258
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2260
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2262
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2263
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2264
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2265
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2266
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2267
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2268
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2269
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2270
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2271
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2272
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2274
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2275
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2276
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2277
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2279
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2280
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2281
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2282
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2283
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2284
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2285
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2286
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2287
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2288
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2289
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2290
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2291
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2292
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2293
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2294
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2297
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2298
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2299
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2300
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2301
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2302
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2303
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2304
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2305
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2306
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2308
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2313
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2315
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2316
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2318
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2319
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2320
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2324
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2325
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2327
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2329
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2330
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2357
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2358
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2360
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2361
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2362
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2363
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2366
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2367
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2368
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2369
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2370
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2371
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2373
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2374
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2375
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2376
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2377
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2378
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2379
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2380
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2381
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2382
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2383
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2384
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2385
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2386
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2387
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2388
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2389
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2390
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2391
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2392
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2393
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2394
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2395
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2396
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2397
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2435
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2436
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2437
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2455
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2456
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2457
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2458
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2462
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2463
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2464
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2465
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2466
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2467
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2468
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2469
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2470
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2471
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2472
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2473
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2474
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2475
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2476
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2478
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2479
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2480
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2481
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2482
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2483
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2484
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2485
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2486
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2487
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2488
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2489
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2490
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2491
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2492
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2493
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2494
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2495
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2496
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2497
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2498
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2499
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2500
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2501
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2502
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2503
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2504
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2505
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2506
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2507
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2508
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2509
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2510
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2511
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2512
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2513
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2514
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2515
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2516
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2517
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2518
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2519
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2520
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2521
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2522
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2523
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2524
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2525
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2526
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2527
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2528
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2529
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2530
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2531
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2532
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2533
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2534
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2535
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2536
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2537
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2538
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2539
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2540
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2541
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2542
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2543
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2544
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2545
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2548
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2549
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2550
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2552
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2553
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2554
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2555
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2556
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2557
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2558
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2559
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2560
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2561
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2562
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2563
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2564
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2565
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2566
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2567
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2568
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2569
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2570
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2571
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2572
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2573
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2574
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2575
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2576
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2577
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2578
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2579
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2580
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2581
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2582
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4082
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4083
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4084
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4085
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4086
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4087
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4088
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4089
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4090
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4091
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4092
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4093
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4094
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4095
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4096
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4097
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4098
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4099
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4100
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4101
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4102
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4103
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4104
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4105
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4106
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4107
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4108
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4109
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4110
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4111
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4112
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4113
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4114
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4115
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4116
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4117
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4118
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4119
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4120
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4121
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4122
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4123
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4124
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4125
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4126
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4132
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4145
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4146
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4147
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4148
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4169
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4171
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4174
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4175
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4177
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4189
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4190
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4191
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4192
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4193
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4194
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4195
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4196
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4197
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4199
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4201
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4202
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4204
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4205
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4206
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4207
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4208
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4209
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4210
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4211
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4212
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4213
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4214
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4215
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4216
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4218
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4219
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4220
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4223
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4224
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4225
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4226
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4227
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4228
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4229
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4230
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4231
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4232
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4233
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4234
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4235
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4236
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4237
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4238
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4239
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4240
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4243
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4244
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4245
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4250
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4251
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4260
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4261
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4264
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4272
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4297
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4298
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4299
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4300
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4301
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4311
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4312
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4317
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4318
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4319
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4320
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4321
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4323
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4324
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4325
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4326
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4327
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4329
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4330
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4331
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4332
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4333
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4334
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4340
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4350
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4411
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4412
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4415
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4417
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4421
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4423
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4424
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4425
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4433
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4436
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4445
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4461
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4462
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4467
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+ "transdermal",
4833
+ "transfer",
4834
+ "transformed",
4835
+ "transfusions",
4836
+ "transient",
4837
+ "transplant",
4838
+ "transplantation",
4839
+ "trauma",
4840
+ "traumatic",
4841
+ "trc102",
4842
+ "treat",
4843
+ "treated",
4844
+ "treating",
4845
+ "treatment",
4846
+ "treprostinil",
4847
+ "tri",
4848
+ "trial",
4849
+ "trials",
4850
+ "tricyclic",
4851
+ "trimethoprim",
4852
+ "triple",
4853
+ "tro",
4854
+ "trus",
4855
+ "try",
4856
+ "tty",
4857
+ "tube",
4858
+ "tuberculosis",
4859
+ "tubes",
4860
+ "tum",
4861
+ "tumor",
4862
+ "tumor(s",
4863
+ "tumors",
4864
+ "tumour",
4865
+ "tus",
4866
+ "two",
4867
+ "tympanostomy",
4868
+ "type",
4869
+ "types",
4870
+ "typical",
4871
+ "t\u2019s",
4872
+ "u",
4873
+ "u.",
4874
+ "ual",
4875
+ "ube",
4876
+ "uc",
4877
+ "ucbt",
4878
+ "ucc",
4879
+ "uce",
4880
+ "uch",
4881
+ "uct",
4882
+ "uda",
4883
+ "ude",
4884
+ "udy",
4885
+ "ued",
4886
+ "ues",
4887
+ "uff",
4888
+ "ug.",
4889
+ "ugh",
4890
+ "ugi",
4891
+ "ugs",
4892
+ "uid",
4893
+ "uk",
4894
+ "ul.",
4895
+ "ula",
4896
+ "ulcer",
4897
+ "ulcerative",
4898
+ "uld",
4899
+ "ule",
4900
+ "ull",
4901
+ "uln",
4902
+ "ult",
4903
+ "ultrasound",
4904
+ "uma",
4905
+ "ume",
4906
+ "un",
4907
+ "un.",
4908
+ "unable",
4909
+ "unacceptable",
4910
+ "uncompensated",
4911
+ "uncontrollable",
4912
+ "uncontrolled",
4913
+ "und",
4914
+ "under",
4915
+ "undergo",
4916
+ "undergoing",
4917
+ "undergone",
4918
+ "underlying",
4919
+ "understanding",
4920
+ "undiagnosed",
4921
+ "une",
4922
+ "unfit",
4923
+ "unforeseeable",
4924
+ "ung",
4925
+ "unilateral",
4926
+ "unipolar",
4927
+ "unit",
4928
+ "units",
4929
+ "university",
4930
+ "unknown",
4931
+ "unless",
4932
+ "unlikely",
4933
+ "unprotected",
4934
+ "unrelated",
4935
+ "unresectable",
4936
+ "unresected",
4937
+ "unresolved",
4938
+ "unstable",
4939
+ "unsuccessfully",
4940
+ "unt",
4941
+ "until",
4942
+ "untreated",
4943
+ "unwilling",
4944
+ "unwillingness",
4945
+ "up",
4946
+ "upon",
4947
+ "upper",
4948
+ "ura",
4949
+ "urd",
4950
+ "ure",
4951
+ "urine",
4952
+ "uro",
4953
+ "uropathogen",
4954
+ "urr",
4955
+ "urs",
4956
+ "ury",
4957
+ "us",
4958
+ "use",
4959
+ "used",
4960
+ "using",
4961
+ "ust",
4962
+ "ute",
4963
+ "uterine",
4964
+ "uveitis",
4965
+ "uzi",
4966
+ "v",
4967
+ "v.",
4968
+ "v.s",
4969
+ "v.s.",
4970
+ "v.v",
4971
+ "v_v",
4972
+ "va",
4973
+ "va.",
4974
+ "vaccination",
4975
+ "vaccine",
4976
+ "vaginal",
4977
+ "val",
4978
+ "valacyclovir",
4979
+ "valproic",
4980
+ "value",
4981
+ "values",
4982
+ "valvular",
4983
+ "vanderbilt",
4984
+ "variegate",
4985
+ "vascular",
4986
+ "vasculitis",
4987
+ "vasectomy",
4988
+ "vasomotor",
4989
+ "ve",
4990
+ "ved",
4991
+ "vein",
4992
+ "vel",
4993
+ "veltri",
4994
+ "ven",
4995
+ "veno",
4996
+ "venous",
4997
+ "ventavis",
4998
+ "ventricular",
4999
+ "ver",
5000
+ "verapamil",
5001
+ "verelan",
5002
+ "verified",
5003
+ "verify",
5004
+ "very",
5005
+ "ves",
5006
+ "vgef",
5007
+ "via",
5008
+ "vic",
5009
+ "vid",
5010
+ "vin",
5011
+ "violate",
5012
+ "vir",
5013
+ "viral",
5014
+ "virus",
5015
+ "vis",
5016
+ "visit",
5017
+ "vismodegib",
5018
+ "visually",
5019
+ "vitamin",
5020
+ "vitro",
5021
+ "vix",
5022
+ "void",
5023
+ "volume",
5024
+ "vomiting",
5025
+ "vorinostat",
5026
+ "vox",
5027
+ "vs",
5028
+ "vs.",
5029
+ "vulnerable",
5030
+ "vumc",
5031
+ "vx-970",
5032
+ "w",
5033
+ "w's",
5034
+ "w.",
5035
+ "w/o",
5036
+ "wafers",
5037
+ "walk",
5038
+ "warfarin",
5039
+ "warts",
5040
+ "was",
5041
+ "wash",
5042
+ "wash.",
5043
+ "washout",
5044
+ "way",
5045
+ "wbc",
5046
+ "we",
5047
+ "weakness",
5048
+ "wed",
5049
+ "week",
5050
+ "weeks",
5051
+ "weigh",
5052
+ "weight",
5053
+ "wel",
5054
+ "wer",
5055
+ "were",
5056
+ "what",
5057
+ "what's",
5058
+ "what\u2019s",
5059
+ "wheat",
5060
+ "wheelchair",
5061
+ "when",
5062
+ "when's",
5063
+ "when\u2019s",
5064
+ "where",
5065
+ "where's",
5066
+ "where\u2019s",
5067
+ "whether",
5068
+ "which",
5069
+ "whichever",
5070
+ "while",
5071
+ "who",
5072
+ "who's",
5073
+ "whom",
5074
+ "who\u2019s",
5075
+ "why",
5076
+ "why's",
5077
+ "why\u2019s",
5078
+ "will",
5079
+ "willing",
5080
+ "willingness",
5081
+ "wilm",
5082
+ "wilson",
5083
+ "wis",
5084
+ "wis.",
5085
+ "with",
5086
+ "within",
5087
+ "without",
5088
+ "wly",
5089
+ "wnt",
5090
+ "wo",
5091
+ "woman",
5092
+ "women",
5093
+ "workup",
5094
+ "would",
5095
+ "wound",
5096
+ "wth",
5097
+ "w\u2019s",
5098
+ "x",
5099
+ "x'",
5100
+ "x'x",
5101
+ "x'xxxx",
5102
+ "x(dd;dd",
5103
+ "x.",
5104
+ "x.X",
5105
+ "x.d",
5106
+ "x.x",
5107
+ "x.x.",
5108
+ "x/x",
5109
+ "xD",
5110
+ "xDD",
5111
+ "xX",
5112
+ "xXX",
5113
+ "xXXX",
5114
+ "xXXXXd/d",
5115
+ "xXx",
5116
+ "x^d",
5117
+ "x_X",
5118
+ "x_d",
5119
+ "x_x",
5120
+ "xam",
5121
+ "xd",
5122
+ "xdd",
5123
+ "xel",
5124
+ "xeroderma",
5125
+ "xic",
5126
+ "xis",
5127
+ "xol",
5128
+ "xor",
5129
+ "xx",
5130
+ "xx'",
5131
+ "xx'x",
5132
+ "xx'xx",
5133
+ "xx)/xxxx",
5134
+ "xx.",
5135
+ "xxX",
5136
+ "xxXx",
5137
+ "xx^d",
5138
+ "xxd",
5139
+ "xxx",
5140
+ "xxx'x",
5141
+ "xxx-",
5142
+ "xxx/d.dd",
5143
+ "xxx/d.ddxd",
5144
+ "xxx/xx",
5145
+ "xxxXXX",
5146
+ "xxxdd",
5147
+ "xxxx",
5148
+ "xxxx'",
5149
+ "xxxx'x",
5150
+ "xxxx(x",
5151
+ "xxxx-",
5152
+ "xxxx-d",
5153
+ "xxxx-ddd",
5154
+ "xxxx/",
5155
+ "xxxxXXXX",
5156
+ "xxxx\u2019",
5157
+ "xxxx\u2019x",
5158
+ "xxx\u2019x",
5159
+ "xx\u2019",
5160
+ "xx\u2019x",
5161
+ "xx\u2019xx",
5162
+ "x\u2019",
5163
+ "x\u2019x",
5164
+ "x\u2019xxxx",
5165
+ "x\ufe35x",
5166
+ "y",
5167
+ "y'",
5168
+ "y's",
5169
+ "y.",
5170
+ "ybe",
5171
+ "year",
5172
+ "years",
5173
+ "yed",
5174
+ "yme",
5175
+ "york",
5176
+ "you",
5177
+ "ype",
5178
+ "ysm",
5179
+ "y\u2019",
5180
+ "y\u2019s",
5181
+ "z",
5182
+ "z.",
5183
+ "zed",
5184
+ "zidovudine",
5185
+ "zil",
5186
+ "zofenopril",
5187
+ "|",
5188
+ "}",
5189
+ "\u00a0",
5190
+ "\u00ac",
5191
+ "\u00ac_\u00ac",
5192
+ "\u00af",
5193
+ "\u00af\\(x)/\u00af",
5194
+ "\u00af\\(\u30c4)/\u00af",
5195
+ "\u00b0",
5196
+ "\u00b0C.",
5197
+ "\u00b0F.",
5198
+ "\u00b0K.",
5199
+ "\u00b0X.",
5200
+ "\u00b0c.",
5201
+ "\u00b0f.",
5202
+ "\u00b0k.",
5203
+ "\u00b0x.",
5204
+ "\u00b5",
5205
+ "\u00b5g",
5206
+ "\u00e4",
5207
+ "\u00e4.",
5208
+ "\u00efve",
5209
+ "\u00f6",
5210
+ "\u00f6.",
5211
+ "\u00fc",
5212
+ "\u00fc.",
5213
+ "\u03b2",
5214
+ "\u0ca0",
5215
+ "\u0ca0_\u0ca0",
5216
+ "\u0ca0\ufe35\u0ca0",
5217
+ "\u2014",
5218
+ "\u2018",
5219
+ "\u2018S",
5220
+ "\u2018X",
5221
+ "\u2018s",
5222
+ "\u2018x",
5223
+ "\u2019",
5224
+ "\u2019-(",
5225
+ "\u2019-)",
5226
+ "\u2019Cause",
5227
+ "\u2019Cos",
5228
+ "\u2019Coz",
5229
+ "\u2019Cuz",
5230
+ "\u2019S",
5231
+ "\u2019X",
5232
+ "\u2019Xxx",
5233
+ "\u2019Xxxxx",
5234
+ "\u2019am",
5235
+ "\u2019bout",
5236
+ "\u2019cause",
5237
+ "\u2019cos",
5238
+ "\u2019coz",
5239
+ "\u2019cuz",
5240
+ "\u2019d",
5241
+ "\u2019em",
5242
+ "\u2019ll",
5243
+ "\u2019m",
5244
+ "\u2019nuff",
5245
+ "\u2019re",
5246
+ "\u2019s",
5247
+ "\u2019ve",
5248
+ "\u2019x",
5249
+ "\u2019xx",
5250
+ "\u2019xxx",
5251
+ "\u2019xxxx",
5252
+ "\u2019y",
5253
+ "\u2019\u2019",
5254
+ "\u2264",
5255
+ "\u2265",
5256
+ "\u22651",
5257
+ "\u2265126",
5258
+ "\u22652",
5259
+ "\u2265450",
5260
+ "\u22655.0",
5261
+ "\u226560",
5262
+ "\u2265Grade",
5263
+ "\u2265Xxxxx",
5264
+ "\u2265d",
5265
+ "\u2265d.d",
5266
+ "\u2265dd",
5267
+ "\u2265ddd",
5268
+ "\u2265grade",
5269
+ "\u2501",
5270
+ "\u253b",
5271
+ "\u253b\u2501\u253b",
5272
+ "\u256f",
5273
+ "\u25a1",
5274
+ "\ufe35",
5275
+ "\uff09"
5276
+ ]
medical-ner-task1/vocab/vectors ADDED
Binary file (128 Bytes). View file
 
medical-ner-task1/vocab/vectors.cfg ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ {
2
+ "mode":"default"
3
+ }