Update spaCy pipeline
Browse files- README.md +22 -23
- config.cfg +44 -50
- en_med12_trf-any-py3-none-any.whl +2 -2
- meta.json +65 -71
- ner/model +0 -0
- ner/moves +1 -1
- textcat/cfg +2 -2
- textcat/model +1 -1
- transformer/model +1 -1
- vocab/strings.json +0 -0
README.md
CHANGED
@@ -14,25 +14,25 @@ model-index:
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metrics:
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- name: NER Precision
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type: precision
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value: 0.
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- name: NER Recall
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type: recall
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value: 0.
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- name: NER F Score
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type: f_score
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value: 0.
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---
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| Feature | Description |
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| --- | --- |
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| **Name** | `en_med12_trf` |
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| **Version** | `1` |
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| **spaCy** | `>=3.4.1,<3.5.0` |
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| **Default Pipeline** | `
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| **Components** | `
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| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
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| **Sources** | n/a |
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| **License** |
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| **Author** |
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### Label Scheme
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| Component | Labels |
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| --- | --- |
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| **`ner`** | `Denominator_Unit`, `Denominator_Value`, `Dose_Form`, `Medication_Name`, `NDC`, `Numerator_Unit`, `Numerator_Value`, `Product_Package_Type`, `Product_Package_Type_Value`, `Quantity_Factor_Unit`, `Quantity_Factor_Unit_Value`, `Quantity_Factor_Value` |
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| **`textcat`** | `
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</details>
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| Type | Score |
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| --- | --- |
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| `ENTS_F` |
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| `ENTS_P` |
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| `ENTS_R` |
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| `CATS_SCORE` |
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| `CATS_MICRO_P` |
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| `CATS_MICRO_R` |
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| `CATS_MICRO_F` |
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| `CATS_MACRO_P` |
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| `CATS_MACRO_R` |
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| `CATS_MACRO_F` |
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| `CATS_MACRO_AUC` |
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| `CATS_MACRO_AUC_PER_TYPE` | 0.00 |
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| `
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| `
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| `
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| `TEXTCAT_LOSS` | 1261.44 |
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metrics:
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- name: NER Precision
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type: precision
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value: 0.8398220245
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- name: NER Recall
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type: recall
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value: 0.8445190157
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- name: NER F Score
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type: f_score
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value: 0.842163971
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---
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| Feature | Description |
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| --- | --- |
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| **Name** | `en_med12_trf` |
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| **Version** | `1` |
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| **spaCy** | `>=3.4.1,<3.5.0` |
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| **Default Pipeline** | `transformer`, `ner`, `textcat` |
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| **Components** | `transformer`, `ner`, `textcat` |
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| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
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| **Sources** | n/a |
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| **License** | n/a |
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| **Author** | [n/a]() |
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### Label Scheme
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| Component | Labels |
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| --- | --- |
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| **`ner`** | `Denominator_Unit`, `Denominator_Value`, `Dose_Form`, `Medication_Name`, `NDC`, `Numerator_Unit`, `Numerator_Value`, `Product_Package_Type`, `Product_Package_Type_Value`, `Quantity_Factor_Unit`, `Quantity_Factor_Unit_Value`, `Quantity_Factor_Value` |
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| **`textcat`** | `OTHER`, `MEDICATION` |
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</details>
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| Type | Score |
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| --- | --- |
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| `ENTS_F` | 84.22 |
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| `ENTS_P` | 83.98 |
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| `ENTS_R` | 84.45 |
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| `CATS_SCORE` | 93.88 |
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| `CATS_MICRO_P` | 89.78 |
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| `CATS_MICRO_R` | 97.98 |
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| `CATS_MICRO_F` | 93.70 |
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| `CATS_MACRO_P` | 90.29 |
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| `CATS_MACRO_R` | 97.93 |
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| `CATS_MACRO_F` | 93.88 |
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| `CATS_MACRO_AUC` | 98.53 |
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| `CATS_MACRO_AUC_PER_TYPE` | 0.00 |
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| `TRANSFORMER_LOSS` | 152780.09 |
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| `NER_LOSS` | 69513.43 |
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| `TEXTCAT_LOSS` | 1868.30 |
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config.cfg
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[paths]
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train =
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dev =
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vectors = null
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init_tok2vec = null
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[system]
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gpu_allocator =
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seed =
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[nlp]
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lang = "en"
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pipeline = ["
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batch_size =
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disabled = []
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before_creation = null
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after_creation = null
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pooling = {"@layers":"reduce_mean.v1"}
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upstream = "*"
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[components.tok2vec]
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factory = "tok2vec"
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[components.tok2vec.model]
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@architectures = "spacy.Tok2Vec.v2"
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[components.tok2vec.model.embed]
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@architectures = "spacy.MultiHashEmbed.v2"
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width = ${components.tok2vec.model.encode.width}
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attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
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rows = [5000,2500,2500,2500]
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include_static_vectors = false
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[components.tok2vec.model.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v2"
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width = 96
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depth = 4
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window_size = 1
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maxout_pieces = 3
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[components.transformer]
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factory = "transformer"
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max_batch_items = 4096
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[components.transformer.model.transformer_config]
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[corpora]
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[corpora.
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@readers = "
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[corpora.textcat]
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@readers = "prodigy.TextCatCorpus.v1"
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datasets = ["db-labeled"]
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eval_datasets = []
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exclusive = true
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[training]
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accumulate_gradient = 3
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train_corpus = "corpora.train"
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seed = ${system.seed}
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gpu_allocator = ${system.gpu_allocator}
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dropout = 0.
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patience = 1600
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max_epochs = 0
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max_steps = 20000
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get_length = null
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[training.logger]
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@loggers = "
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[training.optimizer]
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@optimizers = "Adam.v1"
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[initialize.components]
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[initialize.tokenizer]
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[paths]
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train = "100_percent.spacy"
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dev = "rw_test.spacy"
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vectors = null
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init_tok2vec = null
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[system]
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gpu_allocator = "pytorch"
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seed = 42
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[nlp]
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lang = "en"
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pipeline = ["transformer","ner","textcat"]
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batch_size = 256
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disabled = []
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before_creation = null
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after_creation = null
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pooling = {"@layers":"reduce_mean.v1"}
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upstream = "*"
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[components.transformer]
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factory = "transformer"
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max_batch_items = 4096
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[components.transformer.model.transformer_config]
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[corpora]
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[corpora.dev]
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@readers = "spacy.Corpus.v1"
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path = ${paths.dev}
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max_length = 0
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gold_preproc = false
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limit = 0
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augmenter = null
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[corpora.train]
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@readers = "spacy.Corpus.v1"
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path = ${paths.train}
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max_length = 0
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gold_preproc = false
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limit = 0
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augmenter = null
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[training]
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accumulate_gradient = 3
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train_corpus = "corpora.train"
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seed = ${system.seed}
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gpu_allocator = ${system.gpu_allocator}
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dropout = 0.05
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patience = 1600
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max_epochs = 0
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max_steps = 20000
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get_length = null
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[training.logger]
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@loggers = "spacy.WandbLogger.v3"
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project_name = "med12"
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remove_config_values = ["paths.train","paths.dev","corpora.train.path","corpora.dev.path"]
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log_dataset_dir = "./corpus"
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model_log_interval = 1000
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entity = null
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run_name = null
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[training.optimizer]
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@optimizers = "Adam.v1"
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[initialize.components]
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[initialize.components.ner]
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[initialize.components.ner.labels]
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@readers = "spacy.read_labels.v1"
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path = "C:\\Users\\karl2\\Documents\\medication_parsing\\rxnorm_model_development\\models\\roberta_weak_rw\\labels\\ner.json"
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require = false
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[initialize.components.textcat]
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[initialize.components.textcat.labels]
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@readers = "spacy.read_labels.v1"
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path = "C:\\Users\\karl2\\Documents\\medication_parsing\\rxnorm_model_development\\models\\roberta_weak_rw\\labels\\textcat.json"
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require = false
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[initialize.tokenizer]
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en_med12_trf-any-py3-none-any.whl
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:2e0d7e75743984013d22e7c555d824fd77b38f46e6f851bd031695e14a933a34
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size 454351979
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meta.json
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"name":null
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},
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"labels":{
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"tok2vec":[
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],
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"transformer":[
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],
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"Quantity_Factor_Value"
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],
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"textcat":[
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"
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"
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]
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},
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"pipeline":[
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"transformer",
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"ner",
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"textcat"
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],
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"components":[
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"tok2vec",
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"transformer",
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"ner",
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"textcat"
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],
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"performance":{
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"ents_f":0.
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"ents_p":0.
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"ents_r":0.
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"ents_per_type":{
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"Medication_Name":{
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"p":0.
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"r":0.
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"f":0.
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"Numerator_Value":{
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"p":0.9416666667,
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"r":0.9826086957,
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"f":0.9617021277
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},
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"Numerator_Unit":{
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"p":0.9230769231,
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"r":0.9391304348,
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"f":0.9310344828
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},
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"Dose_Form":{
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"p":0.
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"r":0.
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"f":0.
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"Quantity_Factor_Value":{
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"f":0.
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"Quantity_Factor_Unit":{
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"p":0.
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"r":0.
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"f":0.
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"Product_Package_Type":{
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"f":0.824742268
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"p":0.
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"Denominator_Unit":{
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}
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},
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"cats_score":0.
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"cats_score_desc":"macro F",
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"cats_micro_p":0.
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"cats_micro_r":0.
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"cats_micro_f":0.
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"cats_macro_p":0.
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"cats_macro_r":0.
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"cats_macro_f":0.
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"cats_macro_auc":0.
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"cats_f_per_type":{
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"MEDICATION":{
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"p":0.9273913043,
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"r":0.996728972,
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"f":0.9608108108
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"OTHER":{
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}
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"cats_macro_auc_per_type":0.0,
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"
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"textcat_loss":12.6144110119
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},
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"requirements":[
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"spacy-transformers>=1.1.7,<1.2.0"
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"name":null
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},
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"labels":{
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"transformer":[
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],
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"Quantity_Factor_Value"
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],
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"textcat":[
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"OTHER",
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"MEDICATION"
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]
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},
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"pipeline":[
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"transformer",
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"ner",
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"textcat"
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],
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"components":[
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"transformer",
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"ner",
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"textcat"
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],
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"performance":{
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"ents_f":0.842163971,
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"ents_p":0.8398220245,
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"ents_r":0.8445190157,
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"ents_per_type":{
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"Medication_Name":{
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"p":0.8181818182,
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"r":0.8247011952,
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