Update spaCy pipeline
Browse files- .gitattributes +5 -0
- README.md +85 -0
- clausecat/cfg +9 -0
- clausecat/model +3 -0
- config.cfg +240 -0
- en_healthsea-any-py3-none-any.whl +3 -0
- meta.json +102 -0
- ner/cfg +13 -0
- ner/model +3 -0
- ner/moves +1 -0
- sentencizer.json +133 -0
- tok2vec/cfg +3 -0
- tok2vec/model +3 -0
- tokenizer +3 -0
- vocab/key2row +0 -0
- vocab/lookups.bin +3 -0
- vocab/strings.json +3 -0
- vocab/vectors +3 -0
- vocab/vectors.cfg +3 -0
.gitattributes
CHANGED
@@ -25,3 +25,8 @@ 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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*.zstandard 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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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.whl filter=lfs diff=lfs merge=lfs -text
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*.npz filter=lfs diff=lfs merge=lfs -text
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*strings.json filter=lfs diff=lfs merge=lfs -text
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vectors filter=lfs diff=lfs merge=lfs -text
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model filter=lfs diff=lfs merge=lfs -text
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README.md
ADDED
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---
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tags:
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- spacy
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- token-classification
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language:
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- en
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license: mit
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model-index:
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- name: en_healthsea
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results:
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- task:
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name: NER
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type: token-classification
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metrics:
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- name: NER Precision
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type: precision
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value: 0.0
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- name: NER Recall
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type: recall
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value: 0.0
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- name: NER F Score
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type: f_score
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value: 0.0
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- task:
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name: SENTER
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type: token-classification
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metrics:
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- name: SENTER Precision
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type: precision
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value: 1.0
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- name: SENTER Recall
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type: recall
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value: 1.0
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- name: SENTER F Score
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type: f_score
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value: 1.0
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---
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Healthsea pipeline for analyzing reviews to supplement products
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| Feature | Description |
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| --- | --- |
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| **Name** | `en_healthsea` |
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| **Version** | `0.0.1` |
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| **spaCy** | `>=3.2.0,<3.3.0` |
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| **Default Pipeline** | `sentencizer`, `tok2vec`, `ner`, `benepar`, `segmentation`, `clausecat`, `aggregation` |
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| **Components** | `sentencizer`, `tok2vec`, `ner`, `benepar`, `segmentation`, `clausecat`, `aggregation` |
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| **Vectors** | 684830 keys, 684830 unique vectors (300 dimensions) |
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| **Sources** | n/a |
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| **License** | `MIT` |
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| **Author** | [Explosion](explosion.ai) |
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### Label Scheme
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<details>
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<summary>View label scheme (6 labels for 2 components)</summary>
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| Component | Labels |
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| --- | --- |
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| **`ner`** | `BENEFIT`, `CONDITION` |
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| **`clausecat`** | `POSITIVE`, `NEUTRAL`, `NEGATIVE`, `ANAMNESIS` |
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</details>
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### Accuracy
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| Type | Score |
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| --- | --- |
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| `SENTS_F` | 100.00 |
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| `SENTS_P` | 100.00 |
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| `SENTS_R` | 100.00 |
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| `ENTS_F` | 0.00 |
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| `ENTS_P` | 0.00 |
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| `ENTS_R` | 0.00 |
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| `ENTS_PER_TYPE` | 0.00 |
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| `CATS_SCORE` | 74.87 |
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| `CATS_MICRO_P` | 82.39 |
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| `CATS_MICRO_R` | 80.93 |
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| `CATS_MICRO_F` | 81.66 |
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| `CATS_MACRO_P` | 78.43 |
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| `CATS_MACRO_R` | 72.16 |
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| `CATS_MACRO_F` | 74.87 |
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| `CATS_MACRO_AUC` | 92.78 |
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| `CATS_MACRO_AUC_PER_TYPE` | 0.00 |
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| `CLAUSECAT_LOSS` | 339.11 |
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clausecat/cfg
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{
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"labels":[
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"POSITIVE",
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"NEUTRAL",
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"NEGATIVE",
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"ANAMNESIS"
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],
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"threshold":0.5
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}
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clausecat/model
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version https://git-lfs.github.com/spec/v1
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oid sha256:ea7556781149d2f3a1d8078591cf6038725f4bb5dc5b2d53fdfb41c907a80cb4
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size 6897815
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config.cfg
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[paths]
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train = "assets/clausecat/train.spacy"
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dev = "assets/clausecat/dev.spacy"
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vectors = ${paths.ner_model}
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init_tok2vec = "assets/pretrained_weights_clausecat.bin"
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ner_model = "training/ner/config_tok2vec/model-best"
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[system]
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gpu_allocator = "pytorch"
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seed = 0
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[nlp]
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lang = "en"
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pipeline = ["sentencizer","tok2vec","ner","benepar","segmentation","clausecat","aggregation"]
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batch_size = 128
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disabled = []
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before_creation = null
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after_creation = null
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after_pipeline_creation = null
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tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
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[components]
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[components.aggregation]
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factory = "healthsea.aggregation.v1"
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[components.benepar]
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factory = "benepar"
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disable_tagger = false
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model = "benepar_en3"
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subbatch_max_tokens = 500
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[components.clausecat]
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factory = "healthsea.clausecat.v1"
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threshold = 0.5
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[components.clausecat.model]
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@architectures = "healthsea.clausecat_model.v1"
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blinder = {"@layers":"healthsea.blinder.v1"}
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[components.clausecat.model.textcat]
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@architectures = "spacy.TextCatEnsemble.v2"
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nO = null
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[components.clausecat.model.textcat.linear_model]
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@architectures = "spacy.TextCatBOW.v2"
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exclusive_classes = true
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ngram_size = 1
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no_output_layer = false
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nO = null
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[components.clausecat.model.textcat.tok2vec]
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@architectures = "spacy.Tok2Vec.v2"
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[components.clausecat.model.textcat.tok2vec.embed]
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@architectures = "spacy.MultiHashEmbed.v2"
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width = 64
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rows = [2000,2000,1000,1000,1000,1000]
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attrs = ["ORTH","LOWER","PREFIX","SUFFIX","SHAPE","ID"]
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include_static_vectors = false
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[components.clausecat.model.textcat.tok2vec.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v2"
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width = ${components.clausecat.model.textcat.tok2vec.embed.width}
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window_size = 1
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maxout_pieces = 3
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depth = 2
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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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[components.ner.model]
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@architectures = "spacy.TransitionBasedParser.v2"
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state_type = "ner"
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extra_state_tokens = false
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hidden_width = 64
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maxout_pieces = 2
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use_upper = false
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nO = null
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[components.ner.model.tok2vec]
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@architectures = "spacy.Tok2VecListener.v1"
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width = 256
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upstream = "tok2vec"
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[components.segmentation]
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factory = "healthsea.segmentation.v1"
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[components.sentencizer]
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factory = "sentencizer"
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overwrite = false
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punct_chars = null
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scorer = {"@scorers":"spacy.senter_scorer.v1"}
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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 = 256
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108 |
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attrs = ["ORTH","SHAPE"]
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rows = [5000,2500]
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include_static_vectors = true
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[components.tok2vec.model.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v2"
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114 |
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width = 256
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115 |
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depth = 8
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window_size = 1
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maxout_pieces = 3
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[corpora]
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[corpora.dev]
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@readers = "healthsea.clausecat_reader.v1"
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path = ${paths.dev}
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[corpora.train]
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@readers = "healthsea.clausecat_reader.v1"
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path = ${paths.train}
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[training]
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accumulate_gradient = 3
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dev_corpus = "corpora.dev"
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train_corpus = "corpora.train"
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seed = ${system.seed}
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gpu_allocator = ${system.gpu_allocator}
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dropout = 0.1
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136 |
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patience = 1600
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137 |
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max_epochs = 0
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max_steps = 20000
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139 |
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eval_frequency = 200
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140 |
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frozen_components = ["tok2vec","ner"]
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141 |
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annotating_components = []
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142 |
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before_to_disk = null
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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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[training.batcher.size]
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@schedules = "compounding.v1"
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152 |
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start = 100
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153 |
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stop = 1000
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154 |
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compound = 1.001
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t = 0.0
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156 |
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|
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[training.logger]
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@loggers = "spacy.ConsoleLogger.v1"
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159 |
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progress_bar = true
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160 |
+
|
161 |
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[training.optimizer]
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162 |
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@optimizers = "Adam.v1"
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163 |
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beta1 = 0.9
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164 |
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beta2 = 0.999
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165 |
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L2_is_weight_decay = true
|
166 |
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L2 = 0.01
|
167 |
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grad_clip = 1.0
|
168 |
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use_averages = false
|
169 |
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eps = 0.00000001
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170 |
+
|
171 |
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[training.optimizer.learn_rate]
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172 |
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@schedules = "warmup_linear.v1"
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173 |
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warmup_steps = 250
|
174 |
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total_steps = 20000
|
175 |
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initial_rate = 0.00005
|
176 |
+
|
177 |
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[training.score_weights]
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178 |
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sents_f = null
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179 |
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sents_p = null
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180 |
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sents_r = null
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181 |
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ents_f = null
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182 |
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ents_p = null
|
183 |
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ents_r = null
|
184 |
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ents_per_type = null
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185 |
+
cats_score = 1.0
|
186 |
+
cats_score_desc = null
|
187 |
+
cats_micro_p = null
|
188 |
+
cats_micro_r = null
|
189 |
+
cats_micro_f = null
|
190 |
+
cats_macro_p = null
|
191 |
+
cats_macro_r = null
|
192 |
+
cats_macro_f = null
|
193 |
+
cats_macro_auc = null
|
194 |
+
cats_f_per_type = null
|
195 |
+
cats_macro_auc_per_type = null
|
196 |
+
|
197 |
+
[pretraining]
|
198 |
+
max_epochs = 100
|
199 |
+
dropout = 0.2
|
200 |
+
n_save_every = null
|
201 |
+
n_save_epoch = 1
|
202 |
+
component = "clausecat"
|
203 |
+
layer = "tok2vec"
|
204 |
+
corpus = "corpora.pretrain"
|
205 |
+
|
206 |
+
[pretraining.batcher]
|
207 |
+
@batchers = "spacy.batch_by_words.v1"
|
208 |
+
size = 10000
|
209 |
+
discard_oversize = false
|
210 |
+
tolerance = 0.2
|
211 |
+
get_length = null
|
212 |
+
|
213 |
+
[pretraining.objective]
|
214 |
+
@architectures = "spacy.PretrainVectors.v1"
|
215 |
+
maxout_pieces = 3
|
216 |
+
hidden_size = 300
|
217 |
+
loss = "cosine"
|
218 |
+
|
219 |
+
[pretraining.optimizer]
|
220 |
+
@optimizers = "Adam.v1"
|
221 |
+
beta1 = 0.9
|
222 |
+
beta2 = 0.999
|
223 |
+
L2_is_weight_decay = true
|
224 |
+
L2 = 0.01
|
225 |
+
grad_clip = 1.0
|
226 |
+
use_averages = true
|
227 |
+
eps = 0.00000001
|
228 |
+
learn_rate = 0.001
|
229 |
+
|
230 |
+
[initialize]
|
231 |
+
vectors = ${paths.vectors}
|
232 |
+
init_tok2vec = ${paths.init_tok2vec}
|
233 |
+
vocab_data = null
|
234 |
+
lookups = null
|
235 |
+
before_init = null
|
236 |
+
after_init = null
|
237 |
+
|
238 |
+
[initialize.components]
|
239 |
+
|
240 |
+
[initialize.tokenizer]
|
en_healthsea-any-py3-none-any.whl
ADDED
@@ -0,0 +1,3 @@
|
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|
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|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:43a1754395268837ef355876db6f7cee7b7906877ae01d93e970d614043dea0d
|
3 |
+
size 793109847
|
meta.json
ADDED
@@ -0,0 +1,102 @@
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|
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|
|
|
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|
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|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
1 |
+
{
|
2 |
+
"lang":"en",
|
3 |
+
"name":"healthsea",
|
4 |
+
"version":"0.0.1",
|
5 |
+
"description":"Healthsea pipeline for analyzing reviews to supplement products",
|
6 |
+
"author":"Explosion",
|
7 |
+
"email":"edward@explosion.ai",
|
8 |
+
"url":"explosion.ai",
|
9 |
+
"license":"MIT",
|
10 |
+
"spacy_version":">=3.2.0,<3.3.0",
|
11 |
+
"spacy_git_version":"c9baf9d19",
|
12 |
+
"vectors":{
|
13 |
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"width":300,
|
14 |
+
"vectors":684830,
|
15 |
+
"keys":684830,
|
16 |
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"name":"en_vectors"
|
17 |
+
},
|
18 |
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"labels":{
|
19 |
+
"sentencizer":[
|
20 |
+
|
21 |
+
],
|
22 |
+
"tok2vec":[
|
23 |
+
|
24 |
+
],
|
25 |
+
"ner":[
|
26 |
+
"BENEFIT",
|
27 |
+
"CONDITION"
|
28 |
+
],
|
29 |
+
"clausecat":[
|
30 |
+
"POSITIVE",
|
31 |
+
"NEUTRAL",
|
32 |
+
"NEGATIVE",
|
33 |
+
"ANAMNESIS"
|
34 |
+
]
|
35 |
+
},
|
36 |
+
"pipeline":[
|
37 |
+
"sentencizer",
|
38 |
+
"tok2vec",
|
39 |
+
"ner",
|
40 |
+
"benepar",
|
41 |
+
"segmentation",
|
42 |
+
"clausecat",
|
43 |
+
"aggregation"
|
44 |
+
],
|
45 |
+
"components":[
|
46 |
+
"sentencizer",
|
47 |
+
"tok2vec",
|
48 |
+
"ner",
|
49 |
+
"benepar",
|
50 |
+
"segmentation",
|
51 |
+
"clausecat",
|
52 |
+
"aggregation"
|
53 |
+
],
|
54 |
+
"disabled":[
|
55 |
+
|
56 |
+
],
|
57 |
+
"performance":{
|
58 |
+
"sents_f":1.0,
|
59 |
+
"sents_p":1.0,
|
60 |
+
"sents_r":1.0,
|
61 |
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"ents_f":0.0,
|
62 |
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"ents_p":0.0,
|
63 |
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"ents_r":0.0,
|
64 |
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"ents_per_type":0.0,
|
65 |
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"cats_score":0.7487486162,
|
66 |
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"cats_score_desc":"macro F",
|
67 |
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"cats_micro_p":0.8239148239,
|
68 |
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"cats_micro_r":0.8093322607,
|
69 |
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"cats_micro_f":0.8165584416,
|
70 |
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"cats_macro_p":0.7843270037,
|
71 |
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"cats_macro_r":0.72164903,
|
72 |
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"cats_macro_f":0.7487486162,
|
73 |
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"cats_macro_auc":0.9277859819,
|
74 |
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"cats_f_per_type":{
|
75 |
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"POSITIVE":{
|
76 |
+
"p":0.7881508079,
|
77 |
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"r":0.871031746,
|
78 |
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"f":0.8275212064
|
79 |
+
},
|
80 |
+
"NEUTRAL":{
|
81 |
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"p":0.8888888889,
|
82 |
+
"r":0.8183421517,
|
83 |
+
"f":0.8521579431
|
84 |
+
},
|
85 |
+
"NEGATIVE":{
|
86 |
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"p":0.7543859649,
|
87 |
+
"r":0.5972222222,
|
88 |
+
"f":0.6666666667
|
89 |
+
},
|
90 |
+
"ANAMNESIS":{
|
91 |
+
"p":0.7058823529,
|
92 |
+
"r":0.6,
|
93 |
+
"f":0.6486486486
|
94 |
+
}
|
95 |
+
},
|
96 |
+
"cats_macro_auc_per_type":0.0,
|
97 |
+
"clausecat_loss":3.3911235126
|
98 |
+
},
|
99 |
+
"requirements":[
|
100 |
+
"benepar>=0.2.0,<0.3.0"
|
101 |
+
]
|
102 |
+
}
|
ner/cfg
ADDED
@@ -0,0 +1,13 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"moves":null,
|
3 |
+
"update_with_oracle_cut_size":100,
|
4 |
+
"multitasks":[
|
5 |
+
|
6 |
+
],
|
7 |
+
"min_action_freq":1,
|
8 |
+
"learn_tokens":false,
|
9 |
+
"beam_width":1,
|
10 |
+
"beam_density":0.0,
|
11 |
+
"beam_update_prob":0.0,
|
12 |
+
"incorrect_spans_key":null
|
13 |
+
}
|
ner/model
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ae260f860a81ea7040200d3931705028c29fb7d09e9bf5f279802c9d77fdee82
|
3 |
+
size 82142
|
ner/moves
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
��movesٰ{"0":{},"1":{"CONDITION":1955,"BENEFIT":1204},"2":{"CONDITION":1955,"BENEFIT":1204},"3":{"CONDITION":1955,"BENEFIT":1204},"4":{"CONDITION":1955,"BENEFIT":1204,"":1},"5":{"":1}}�cfg��neg_key�
|
sentencizer.json
ADDED
@@ -0,0 +1,133 @@
|
|
|
|
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|
|
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|
|
|
|
|
|
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|
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|
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|
1 |
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{
|
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"punct_chars":[
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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"\ud807\udc41",
|
85 |
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"!",
|
86 |
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|
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"\u1809",
|
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|
89 |
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|
90 |
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|
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|
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|
93 |
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|
94 |
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|
95 |
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|
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|
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|
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|
99 |
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|
100 |
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|
101 |
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|
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|
103 |
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|
104 |
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|
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|
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|
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|
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|
110 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
128 |
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|
129 |
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|
130 |
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|
131 |
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],
|
132 |
+
"overwrite":false
|
133 |
+
}
|
tok2vec/cfg
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
|
3 |
+
}
|
tok2vec/model
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:4d06bb1fab516d8a22392d96802edadbdfae4edbfb6ae622ea587d696f77618a
|
3 |
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size 29276330
|
tokenizer
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
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