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  1. .gitattributes +1 -0
  2. both_models/sentence_transformer_model/1_Pooling/config.json +7 -0
  3. both_models/sentence_transformer_model/README.md +88 -0
  4. both_models/sentence_transformer_model/config.json +24 -0
  5. both_models/sentence_transformer_model/config_sentence_transformers.json +7 -0
  6. both_models/sentence_transformer_model/modules.json +20 -0
  7. both_models/sentence_transformer_model/pytorch_model.bin +3 -0
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  14. both_models/spacy_ner_model/README.md +34 -0
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  16. both_models/spacy_ner_model/en_pipeline.egg-info/PKG-INFO +41 -0
  17. both_models/spacy_ner_model/en_pipeline.egg-info/SOURCES.txt +29 -0
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  41. both_models/spacy_ner_model/meta.json +68 -0
  42. both_models/spacy_ner_model/setup.py +72 -0
.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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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ ---
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+ pipeline_tag: sentence-similarity
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+ tags:
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+ - sentence-transformers
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+ - feature-extraction
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+ - sentence-similarity
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+
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+ ---
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+
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+ # {MODEL_NAME}
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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+
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+ <!--- Describe your model here -->
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+
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+ ## Usage (Sentence-Transformers)
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+
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+ Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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+
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+ ```
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+ pip install -U sentence-transformers
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+ ```
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+
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+ Then you can use the model like this:
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+
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+ ```python
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+ from sentence_transformers import SentenceTransformer
28
+ sentences = ["This is an example sentence", "Each sentence is converted"]
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+
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+ model = SentenceTransformer('{MODEL_NAME}')
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+ embeddings = model.encode(sentences)
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+ print(embeddings)
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+ ```
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+
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+
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+
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+ ## Evaluation Results
38
+
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+ <!--- Describe how your model was evaluated -->
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+
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+ For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
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+
43
+
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+ ## Training
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+ The model was trained with the parameters:
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+
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+ **DataLoader**:
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+
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+ `torch.utils.data.dataloader.DataLoader` of length 1 with parameters:
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+ ```
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+ {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
52
+ ```
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+
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+ **Loss**:
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+
56
+ `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
57
+
58
+ Parameters of the fit()-Method:
59
+ ```
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+ {
61
+ "epochs": 2,
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+ "evaluation_steps": 1000,
63
+ "evaluator": "NoneType",
64
+ "max_grad_norm": 1,
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+ "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
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+ "optimizer_params": {
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+ "lr": 2e-05
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+ },
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+ "scheduler": "WarmupLinear",
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+ "steps_per_epoch": null,
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+ "warmup_steps": 100,
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+ "weight_decay": 0.01
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+ }
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+ ```
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+
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+
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+ ## Full Model Architecture
78
+ ```
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+ SentenceTransformer(
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+ (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
81
+ (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
82
+ (2): Normalize()
83
+ )
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+ ```
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+
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+ ## Citing & Authors
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+
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+ <!--- Describe where people can find more information -->
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+ "_name_or_path": "/Users/bharathkumarnune/.cache/torch/sentence_transformers/sentence-transformers_all-mpnet-base-v2/",
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+ "MPNetModel"
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.30.2",
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+ "vocab_size": 30527
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+ }
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+ ]
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both_models/spacy_ner_model/MANIFEST.in ADDED
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+ include meta.json
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+ include LICENSE
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+ include LICENSES_SOURCES
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+ include README.md
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@@ -0,0 +1,34 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ | Feature | Description |
2
+ | --- | --- |
3
+ | **Name** | `en_pipeline` |
4
+ | **Version** | `0.0.0` |
5
+ | **spaCy** | `>=3.6.0,<3.7.0` |
6
+ | **Default Pipeline** | `tok2vec`, `entity_ruler`, `ner` |
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+ | **Components** | `tok2vec`, `entity_ruler`, `ner` |
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+ | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
9
+ | **Sources** | n/a |
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+ | **License** | n/a |
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+ | **Author** | [n/a]() |
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+
13
+ ### Label Scheme
14
+
15
+ <details>
16
+
17
+ <summary>View label scheme (6 labels for 2 components)</summary>
18
+
19
+ | Component | Labels |
20
+ | --- | --- |
21
+ | **`entity_ruler`** | `STATE`, `STATE_CODE`, `ZIPCODE` |
22
+ | **`ner`** | `CITY`, `STATE`, `VERTICAL` |
23
+
24
+ </details>
25
+
26
+ ### Accuracy
27
+
28
+ | Type | Score |
29
+ | --- | --- |
30
+ | `ENTS_F` | 99.82 |
31
+ | `ENTS_P` | 99.91 |
32
+ | `ENTS_R` | 99.73 |
33
+ | `TOK2VEC_LOSS` | 20878.29 |
34
+ | `NER_LOSS` | 2762.75 |
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+ Metadata-Version: 2.1
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+ Name: en-pipeline
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+ Version: 0.0.0
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+ Home-page:
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+ Author:
6
+ Author-email:
7
+
8
+ | Feature | Description |
9
+ | --- | --- |
10
+ | **Name** | `en_pipeline` |
11
+ | **Version** | `0.0.0` |
12
+ | **spaCy** | `>=3.6.0,<3.7.0` |
13
+ | **Default Pipeline** | `tok2vec`, `entity_ruler`, `ner` |
14
+ | **Components** | `tok2vec`, `entity_ruler`, `ner` |
15
+ | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
16
+ | **Sources** | n/a |
17
+ | **License** | n/a |
18
+ | **Author** | [n/a]() |
19
+
20
+ ### Label Scheme
21
+
22
+ <details>
23
+
24
+ <summary>View label scheme (6 labels for 2 components)</summary>
25
+
26
+ | Component | Labels |
27
+ | --- | --- |
28
+ | **`entity_ruler`** | `STATE`, `STATE_CODE`, `ZIPCODE` |
29
+ | **`ner`** | `CITY`, `STATE`, `VERTICAL` |
30
+
31
+ </details>
32
+
33
+ ### Accuracy
34
+
35
+ | Type | Score |
36
+ | --- | --- |
37
+ | `ENTS_F` | 99.82 |
38
+ | `ENTS_P` | 99.91 |
39
+ | `ENTS_R` | 99.73 |
40
+ | `TOK2VEC_LOSS` | 20878.29 |
41
+ | `NER_LOSS` | 2762.75 |
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+ README.md
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+ meta.json
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+ setup.py
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+ en_pipeline/meta.json
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+ en_pipeline/en_pipeline-0.0.0/tokenizer
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+ en_pipeline/en_pipeline-0.0.0/entity_ruler/cfg
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+ en_pipeline/en_pipeline-0.0.0/entity_ruler/patterns.jsonl
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+ en_pipeline/en_pipeline-0.0.0/ner/cfg
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+ en_pipeline/en_pipeline-0.0.0/ner/model
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+ en_pipeline/en_pipeline-0.0.0/ner/moves
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1
+
both_models/spacy_ner_model/en_pipeline.egg-info/entry_points.txt ADDED
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1
+ [spacy_models]
2
+ en_pipeline = en_pipeline
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@@ -0,0 +1 @@
 
 
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+
both_models/spacy_ner_model/en_pipeline.egg-info/requires.txt ADDED
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+ spacy<3.7.0,>=3.6.0
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1
+ en_pipeline
both_models/spacy_ner_model/en_pipeline/__init__.py ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ from pathlib import Path
2
+ from spacy.util import load_model_from_init_py, get_model_meta
3
+
4
+
5
+
6
+ __version__ = get_model_meta(Path(__file__).parent)['version']
7
+
8
+
9
+ def load(**overrides):
10
+ return load_model_from_init_py(__file__, **overrides)
both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/README.md ADDED
@@ -0,0 +1,34 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ | Feature | Description |
2
+ | --- | --- |
3
+ | **Name** | `en_pipeline` |
4
+ | **Version** | `0.0.0` |
5
+ | **spaCy** | `>=3.6.0,<3.7.0` |
6
+ | **Default Pipeline** | `tok2vec`, `entity_ruler`, `ner` |
7
+ | **Components** | `tok2vec`, `entity_ruler`, `ner` |
8
+ | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
9
+ | **Sources** | n/a |
10
+ | **License** | n/a |
11
+ | **Author** | [n/a]() |
12
+
13
+ ### Label Scheme
14
+
15
+ <details>
16
+
17
+ <summary>View label scheme (6 labels for 2 components)</summary>
18
+
19
+ | Component | Labels |
20
+ | --- | --- |
21
+ | **`entity_ruler`** | `STATE`, `STATE_CODE`, `ZIPCODE` |
22
+ | **`ner`** | `CITY`, `STATE`, `VERTICAL` |
23
+
24
+ </details>
25
+
26
+ ### Accuracy
27
+
28
+ | Type | Score |
29
+ | --- | --- |
30
+ | `ENTS_F` | 99.82 |
31
+ | `ENTS_P` | 99.91 |
32
+ | `ENTS_R` | 99.73 |
33
+ | `TOK2VEC_LOSS` | 20878.29 |
34
+ | `NER_LOSS` | 2762.75 |
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1
+ [paths]
2
+ train = "./train.spacy"
3
+ dev = "./test.spacy"
4
+ vectors = null
5
+ init_tok2vec = null
6
+
7
+ [system]
8
+ gpu_allocator = null
9
+ seed = 0
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+ [nlp]
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+ lang = "en"
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+ batch_size = 1000
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+ disabled = []
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+ after_pipeline_creation = null
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+
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+ [components]
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+
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+ [components.entity_ruler]
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+ ent_id_sep = "||"
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+ matcher_fuzzy_compare = {"@misc":"spacy.levenshtein_compare.v1"}
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+ overwrite_ents = false
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+ phrase_matcher_attr = null
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+ scorer = {"@scorers":"spacy.entity_ruler_scorer.v1"}
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+ validate = false
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+
32
+ [components.ner]
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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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+
48
+ [components.ner.model.tok2vec]
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+ @architectures = "spacy.Tok2VecListener.v1"
50
+ width = ${components.tok2vec.model.encode.width}
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+ upstream = "*"
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+
53
+ [components.tok2vec]
54
+ factory = "tok2vec"
55
+
56
+ [components.tok2vec.model]
57
+ @architectures = "spacy.Tok2Vec.v2"
58
+
59
+ [components.tok2vec.model.embed]
60
+ @architectures = "spacy.MultiHashEmbed.v2"
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+ width = ${components.tok2vec.model.encode.width}
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+ attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
63
+ rows = [5000,1000,2500,2500]
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+ include_static_vectors = false
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+
66
+ [components.tok2vec.model.encode]
67
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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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+
73
+ [corpora]
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+
75
+ [corpora.dev]
76
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+ [corpora.train]
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+ @readers = "spacy.Corpus.v1"
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+ max_length = 0
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+ dev_corpus = "corpora.dev"
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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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+ t = 0.0
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+ [pretraining]
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+
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+ [initialize]
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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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+
151
+ [initialize.components]
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+
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+ [initialize.tokenizer]
both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/entity_ruler/cfg ADDED
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+ {
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+ "overwrite":false,
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+ "ent_id_sep":"||"
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+ "MN",
570
+ "MO",
571
+ "MS",
572
+ "MT",
573
+ "Ma'am",
574
+ "Madison",
575
+ "Maintain",
576
+ "Make",
577
+ "Manage",
578
+ "Mar",
579
+ "Mar.",
580
+ "March",
581
+ "Mass",
582
+ "Mass.",
583
+ "Massachusetts",
584
+ "May",
585
+ "Ma\u2019am",
586
+ "Md",
587
+ "Md.",
588
+ "Memphis",
589
+ "Mesa",
590
+ "Messrs",
591
+ "Messrs.",
592
+ "Miami",
593
+ "Mich",
594
+ "Mich.",
595
+ "Michigan",
596
+ "Might",
597
+ "Milwaukee",
598
+ "Minimize",
599
+ "Minn",
600
+ "Minn.",
601
+ "Minneapolis",
602
+ "Minnesota",
603
+ "Miss",
604
+ "Miss.",
605
+ "Mississippi",
606
+ "Mitigate",
607
+ "Mo",
608
+ "Mo.",
609
+ "Modesto",
610
+ "Moines",
611
+ "Mont",
612
+ "Mont.",
613
+ "Mount",
614
+ "Mr",
615
+ "Mr.",
616
+ "Mrs",
617
+ "Mrs.",
618
+ "Ms",
619
+ "Ms.",
620
+ "Mt",
621
+ "Mt.",
622
+ "Must",
623
+ "N",
624
+ "N.C.",
625
+ "N.D.",
626
+ "N.H.",
627
+ "N.J.",
628
+ "N.M.",
629
+ "N.Y.",
630
+ "NC",
631
+ "ND",
632
+ "NE",
633
+ "NH",
634
+ "NJ",
635
+ "NM",
636
+ "NV",
637
+ "NY",
638
+ "Nashville",
639
+ "Neb",
640
+ "Neb.",
641
+ "Nebr",
642
+ "Nebr.",
643
+ "Nebraska",
644
+ "Need",
645
+ "Nev",
646
+ "Nev.",
647
+ "Nevada",
648
+ "New",
649
+ "New Hampshire",
650
+ "New Jersey",
651
+ "New Mexico",
652
+ "New York",
653
+ "Newark",
654
+ "Norfolk",
655
+ "North",
656
+ "North Carolina",
657
+ "North Dakota",
658
+ "Not",
659
+ "Nothin",
660
+ "Nothin'",
661
+ "Nothin\u2019",
662
+ "Nov",
663
+ "Nov.",
664
+ "November",
665
+ "Nuthin",
666
+ "Nuthin'",
667
+ "Nuthin\u2019",
668
+ "O",
669
+ "O'clock",
670
+ "O.O",
671
+ "O.o",
672
+ "ODE",
673
+ "OH",
674
+ "OK",
675
+ "OR",
676
+ "O_O",
677
+ "O_o",
678
+ "Oakland",
679
+ "Oct",
680
+ "Oct.",
681
+ "October",
682
+ "Okla",
683
+ "Okla.",
684
+ "Oklahoma",
685
+ "Ol",
686
+ "Ol'",
687
+ "Ol\u2019",
688
+ "Omaha",
689
+ "Opt",
690
+ "Ore",
691
+ "Ore.",
692
+ "Oregon",
693
+ "Orlando",
694
+ "Orleans",
695
+ "Ought",
696
+ "O\u2019clock",
697
+ "P",
698
+ "PA",
699
+ "Pa",
700
+ "Pa.",
701
+ "Paso",
702
+ "Pennsylvania",
703
+ "Petersburg",
704
+ "Ph",
705
+ "Ph.D.",
706
+ "Philadelphia",
707
+ "Phoenix",
708
+ "Pittsburgh",
709
+ "Plano",
710
+ "Portland",
711
+ "Preparing",
712
+ "Prof",
713
+ "Prof.",
714
+ "R",
715
+ "RI",
716
+ "ROOT",
717
+ "Raleigh",
718
+ "Rapids",
719
+ "Reach",
720
+ "Reconciliation",
721
+ "Reduce",
722
+ "Rely",
723
+ "Reno",
724
+ "Rep",
725
+ "Rep.",
726
+ "Rev",
727
+ "Rev.",
728
+ "Richmond",
729
+ "Riverside",
730
+ "Rouge",
731
+ "S",
732
+ "S.C.",
733
+ "SC",
734
+ "SD",
735
+ "STATE",
736
+ "STATE_CODE",
737
+ "Sacramento",
738
+ "Salem",
739
+ "San",
740
+ "Santa",
741
+ "Scottsdale",
742
+ "Searching",
743
+ "Seattle",
744
+ "Secure",
745
+ "Seek",
746
+ "Seeking",
747
+ "Selling",
748
+ "Sen",
749
+ "Sen.",
750
+ "Sep",
751
+ "Sep.",
752
+ "Sept",
753
+ "Sept.",
754
+ "September",
755
+ "Sha",
756
+ "She",
757
+ "She's",
758
+ "She\u2019s",
759
+ "Should",
760
+ "Shreveport",
761
+ "Somethin",
762
+ "Somethin'",
763
+ "Somethin\u2019",
764
+ "South Carolina",
765
+ "Spokane",
766
+ "Springs",
767
+ "St",
768
+ "St.",
769
+ "Staging",
770
+ "Stay",
771
+ "Stockton",
772
+ "Streamline",
773
+ "T",
774
+ "TN",
775
+ "TX",
776
+ "Tacoma",
777
+ "Take",
778
+ "Tallahassee",
779
+ "Tampa",
780
+ "Tenn",
781
+ "Tenn.",
782
+ "Tennessee",
783
+ "That",
784
+ "That's",
785
+ "That\u2019s",
786
+ "The",
787
+ "There",
788
+ "There's",
789
+ "There\u2019s",
790
+ "These",
791
+ "They",
792
+ "This",
793
+ "This's",
794
+ "This\u2019s",
795
+ "Those",
796
+ "Toledo",
797
+ "Top",
798
+ "Tucson",
799
+ "Tulsa",
800
+ "U",
801
+ "UT",
802
+ "Understand",
803
+ "Understanding",
804
+ "V",
805
+ "V.V",
806
+ "VA",
807
+ "VERTICAL",
808
+ "VT",
809
+ "V_V",
810
+ "Va",
811
+ "Va.",
812
+ "Vegas",
813
+ "Virginia",
814
+ "Vista",
815
+ "W",
816
+ "WA",
817
+ "WI",
818
+ "WV",
819
+ "WY",
820
+ "Was",
821
+ "Wash",
822
+ "Wash.",
823
+ "Washington",
824
+ "Wayne",
825
+ "We",
826
+ "Were",
827
+ "What",
828
+ "What's",
829
+ "What\u2019s",
830
+ "When",
831
+ "When's",
832
+ "When\u2019s",
833
+ "Where",
834
+ "Where's",
835
+ "Where\u2019s",
836
+ "Who",
837
+ "Who's",
838
+ "Who\u2019s",
839
+ "Why",
840
+ "Why's",
841
+ "Why\u2019s",
842
+ "Wichita",
843
+ "Winston",
844
+ "Wis",
845
+ "Wis.",
846
+ "Wisconsin",
847
+ "Wo",
848
+ "Working",
849
+ "Worth",
850
+ "Would",
851
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852
+ "X'xxxx",
853
+ "X++",
854
+ "X.",
855
+ "X.X",
856
+ "X.X.",
857
+ "X.x",
858
+ "X.x.",
859
+ "XD",
860
+ "XDD",
861
+ "XX",
862
+ "XXX",
863
+ "XXXX",
864
+ "XXXX_XXXX",
865
+ "X_X",
866
+ "X_x",
867
+ "Xx",
868
+ "Xx'",
869
+ "Xx'x",
870
+ "Xx'xx",
871
+ "Xx.",
872
+ "Xx.X.",
873
+ "Xxx",
874
+ "Xxx'x",
875
+ "Xxx.",
876
+ "Xxxx",
877
+ "Xxxx'",
878
+ "Xxxx'x",
879
+ "Xxxx.",
880
+ "Xxxxx",
881
+ "Xxxxx'",
882
+ "Xxxxx'x",
883
+ "Xxxxx.",
884
+ "Xxxxx\u2019",
885
+ "Xxxxx\u2019x",
886
+ "Xxxx\u2019",
887
+ "Xxxx\u2019x",
888
+ "Xxx\u2019x",
889
+ "Xx\u2019",
890
+ "Xx\u2019x",
891
+ "Xx\u2019xx",
892
+ "X\u2019x",
893
+ "X\u2019xxxx",
894
+ "Y",
895
+ "Yonkers",
896
+ "York",
897
+ "You",
898
+ "ZIPCODE",
899
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900
+ "[-:",
901
+ "[:",
902
+ "[=",
903
+ "\\",
904
+ "\\\")",
905
+ "\\n",
906
+ "\\t",
907
+ "\\x",
908
+ "]",
909
+ "]=",
910
+ "^",
911
+ "^_^",
912
+ "^__^",
913
+ "^___^",
914
+ "_*)",
915
+ "_-)",
916
+ "_.)",
917
+ "_<)",
918
+ "_^)",
919
+ "__-",
920
+ "__^",
921
+ "_\u00ac)",
922
+ "_\u0ca0)",
923
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924
+ "a.",
925
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926
+ "a.m.",
927
+ "aap",
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+ "about",
929
+ "accountants",
930
+ "accounting",
931
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932
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934
+ "accurately",
935
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936
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937
+ "ack",
938
+ "act",
939
+ "actuaries",
940
+ "actuary",
941
+ "acumen",
942
+ "ada",
943
+ "adhering",
944
+ "adjustable",
945
+ "adm",
946
+ "adm.",
947
+ "ado",
948
+ "advice",
949
+ "advised",
950
+ "advisor",
951
+ "advisors",
952
+ "advisory",
953
+ "ady",
954
+ "afloat",
955
+ "aft",
956
+ "age",
957
+ "agencies",
958
+ "agency",
959
+ "agents",
960
+ "ago",
961
+ "agreement",
962
+ "aha",
963
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964
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965
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966
+ "aid",
967
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968
+ "ail",
969
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970
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971
+ "ak",
972
+ "ak.",
973
+ "ake",
974
+ "akron",
975
+ "al",
976
+ "ala",
977
+ "ala.",
978
+ "alabama",
979
+ "alaska",
980
+ "albuquerque",
981
+ "ale",
982
+ "all",
983
+ "alo",
984
+ "als",
985
+ "am",
986
+ "ama",
987
+ "amarillo",
988
+ "ami",
989
+ "amount",
990
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991
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992
+ "an.",
993
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994
+ "anaheim",
995
+ "analysis",
996
+ "anchorage",
997
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998
+ "and/or",
999
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1000
+ "angeles",
1001
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1002
+ "annuity",
1003
+ "ano",
1004
+ "ans",
1005
+ "ant",
1006
+ "antonio",
1007
+ "any",
1008
+ "anyone",
1009
+ "appeal",
1010
+ "application",
1011
+ "apr",
1012
+ "apr.",
1013
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1014
+ "ar.",
1015
+ "ard",
1016
+ "are",
1017
+ "ariz",
1018
+ "ariz.",
1019
+ "arizona",
1020
+ "ark",
1021
+ "ark.",
1022
+ "arkansas",
1023
+ "arlington",
1024
+ "arn",
1025
+ "art",
1026
+ "ary",
1027
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1028
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1029
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1030
+ "asp",
1031
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1032
+ "assessment",
1033
+ "assets",
1034
+ "assist",
1035
+ "assistance",
1036
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1037
+ "asy",
1038
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1039
+ "ata",
1040
+ "ate",
1041
+ "ati",
1042
+ "atlanta",
1043
+ "attend",
1044
+ "audit",
1045
+ "aug",
1046
+ "aug.",
1047
+ "aurora",
1048
+ "austin",
1049
+ "avail",
1050
+ "available",
1051
+ "ave",
1052
+ "average",
1053
+ "avoid",
1054
+ "ays",
1055
+ "az",
1056
+ "b",
1057
+ "b.",
1058
+ "back",
1059
+ "bakersfield",
1060
+ "balance",
1061
+ "baltimore",
1062
+ "base",
1063
+ "basics",
1064
+ "basis",
1065
+ "baton",
1066
+ "be",
1067
+ "beach",
1068
+ "because",
1069
+ "become",
1070
+ "before",
1071
+ "beneficiary",
1072
+ "benefit",
1073
+ "benefits",
1074
+ "bernardino",
1075
+ "best",
1076
+ "better",
1077
+ "birmingham",
1078
+ "ble",
1079
+ "bly",
1080
+ "boise",
1081
+ "books",
1082
+ "boost",
1083
+ "boston",
1084
+ "bout",
1085
+ "br.",
1086
+ "broker",
1087
+ "brokers",
1088
+ "bros",
1089
+ "bros.",
1090
+ "buffalo",
1091
+ "build",
1092
+ "building",
1093
+ "bus",
1094
+ "business",
1095
+ "businesses",
1096
+ "buyer",
1097
+ "buying",
1098
+ "by",
1099
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1100
+ "c'm",
1101
+ "c++",
1102
+ "c.",
1103
+ "ca",
1104
+ "cal",
1105
+ "calculate",
1106
+ "calculating",
1107
+ "calculation",
1108
+ "calif",
1109
+ "calif.",
1110
+ "california",
1111
+ "can",
1112
+ "carolina",
1113
+ "case",
1114
+ "cash",
1115
+ "caters",
1116
+ "cause",
1117
+ "ce>",
1118
+ "ced",
1119
+ "ces",
1120
+ "ch.",
1121
+ "chandler",
1122
+ "charlotte",
1123
+ "chesapeake",
1124
+ "chicago",
1125
+ "choose",
1126
+ "choosing",
1127
+ "christi",
1128
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1129
+ "cincinnati",
1130
+ "city",
1131
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1132
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1133
+ "claims",
1134
+ "clarity",
1135
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1136
+ "clauses",
1137
+ "clean",
1138
+ "clearly",
1139
+ "cleveland",
1140
+ "closing",
1141
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1142
+ "co.",
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+ "colo",
1144
+ "colo.",
1145
+ "colorado",
1146
+ "columbus",
1147
+ "come",
1148
+ "commission",
1149
+ "companies",
1150
+ "company",
1151
+ "compensation",
1152
+ "competitive",
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+ "complete",
1154
+ "complex",
1155
+ "compliance",
1156
+ "compliant",
1157
+ "component",
1158
+ "components",
1159
+ "comprehensive",
1160
+ "conn",
1161
+ "conn.",
1162
+ "connect",
1163
+ "connecticut",
1164
+ "consider",
1165
+ "considering",
1166
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1167
+ "consultancy",
1168
+ "consultants",
1169
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1170
+ "consulting",
1171
+ "contact",
1172
+ "contract",
1173
+ "control",
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1175
+ "corp.",
1176
+ "corpus",
1177
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1178
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1179
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1180
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1181
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1182
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1183
+ "coz",
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1185
+ "crafting",
1186
+ "create",
1187
+ "creating",
1188
+ "credible",
1189
+ "credit",
1190
+ "creditor",
1191
+ "creditors",
1192
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1193
+ "ct",
1194
+ "ct.",
1195
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1196
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1197
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1198
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1199
+ "c\u2019m",
1200
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1201
+ "d)",
1202
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1203
+ "d-)",
1204
+ "d-X",
1205
+ "d.",
1206
+ "d.c.",
1207
+ "d.d",
1208
+ "d.x",
1209
+ "dX",
1210
+ "d_d",
1211
+ "d_x",
1212
+ "dakota",
1213
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1214
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+ "data",
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+ "day",
1217
+ "dd",
1218
+ "ddd",
1219
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1220
+ "ddx.x.",
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+ "deal",
1223
+ "deals",
1224
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1225
+ "debtors",
1226
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1227
+ "dec.",
1228
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1229
+ "deductible",
1230
+ "deductibles",
1231
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1232
+ "del",
1233
+ "del.",
1234
+ "delaware",
1235
+ "denver",
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1241
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1242
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1251
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1252
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1253
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1254
+ "dm.",
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1257
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+ "doin'",
1259
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1260
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1262
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1263
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1264
+ "dream",
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1266
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1267
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1301
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1302
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+ "scottsdale",
2139
+ "scratch",
2140
+ "scrutinize",
2141
+ "sd",
2142
+ "seamless",
2143
+ "seamlessly",
2144
+ "searching",
2145
+ "seasoned",
2146
+ "seattle",
2147
+ "secrets",
2148
+ "secure",
2149
+ "secures",
2150
+ "sed",
2151
+ "see",
2152
+ "seek",
2153
+ "seeking",
2154
+ "seller",
2155
+ "selling",
2156
+ "seminar",
2157
+ "sen",
2158
+ "sen.",
2159
+ "sep",
2160
+ "sep.",
2161
+ "sept",
2162
+ "sept.",
2163
+ "seriously",
2164
+ "service",
2165
+ "services",
2166
+ "ses",
2167
+ "sey",
2168
+ "sh.",
2169
+ "sha",
2170
+ "shall",
2171
+ "she",
2172
+ "she's",
2173
+ "sheet",
2174
+ "she\u2019s",
2175
+ "should",
2176
+ "shreveport",
2177
+ "significantly",
2178
+ "sin",
2179
+ "sis",
2180
+ "ska",
2181
+ "sle",
2182
+ "sly",
2183
+ "small",
2184
+ "smart",
2185
+ "smooth",
2186
+ "sno",
2187
+ "solutions",
2188
+ "somethin",
2189
+ "somethin'",
2190
+ "something",
2191
+ "somethin\u2019",
2192
+ "son",
2193
+ "sor",
2194
+ "sound",
2195
+ "south",
2196
+ "space",
2197
+ "specialists",
2198
+ "specialized",
2199
+ "spokane",
2200
+ "springs",
2201
+ "srs",
2202
+ "ss.",
2203
+ "st",
2204
+ "st.",
2205
+ "sta",
2206
+ "stability",
2207
+ "stable",
2208
+ "staging",
2209
+ "standards",
2210
+ "standing",
2211
+ "start",
2212
+ "startup",
2213
+ "state",
2214
+ "state_code",
2215
+ "statement",
2216
+ "stay",
2217
+ "steadily",
2218
+ "step",
2219
+ "sti",
2220
+ "sto",
2221
+ "stockton",
2222
+ "strategically",
2223
+ "strategies",
2224
+ "streamline",
2225
+ "streamlined",
2226
+ "strong",
2227
+ "structures",
2228
+ "sts",
2229
+ "substantial",
2230
+ "successful",
2231
+ "suggest",
2232
+ "suggestions",
2233
+ "suitable",
2234
+ "suits",
2235
+ "surprises",
2236
+ "sustainable",
2237
+ "sustainably",
2238
+ "systematically",
2239
+ "s\u2019s",
2240
+ "t",
2241
+ "t's",
2242
+ "t.",
2243
+ "ta",
2244
+ "tab",
2245
+ "tacoma",
2246
+ "tah",
2247
+ "tailored",
2248
+ "take",
2249
+ "taken",
2250
+ "tal",
2251
+ "tallahassee",
2252
+ "tampa",
2253
+ "tax",
2254
+ "tay",
2255
+ "tch",
2256
+ "td.",
2257
+ "teach",
2258
+ "team",
2259
+ "ted",
2260
+ "tee",
2261
+ "tem",
2262
+ "tenn",
2263
+ "tenn.",
2264
+ "tennessee",
2265
+ "tep",
2266
+ "ter",
2267
+ "term",
2268
+ "terms",
2269
+ "tes",
2270
+ "texas",
2271
+ "that",
2272
+ "that's",
2273
+ "that\u2019s",
2274
+ "the",
2275
+ "their",
2276
+ "them",
2277
+ "there",
2278
+ "there's",
2279
+ "there\u2019s",
2280
+ "these",
2281
+ "they",
2282
+ "thinking",
2283
+ "this",
2284
+ "this's",
2285
+ "this\u2019s",
2286
+ "those",
2287
+ "thrive",
2288
+ "through",
2289
+ "thy",
2290
+ "time",
2291
+ "tin",
2292
+ "tips",
2293
+ "tle",
2294
+ "tly",
2295
+ "tn",
2296
+ "to",
2297
+ "today",
2298
+ "toledo",
2299
+ "ton",
2300
+ "top",
2301
+ "tor",
2302
+ "touch",
2303
+ "towards",
2304
+ "track",
2305
+ "transaction",
2306
+ "transactions",
2307
+ "transparency",
2308
+ "transparent",
2309
+ "true",
2310
+ "trusted",
2311
+ "trustworthy",
2312
+ "try",
2313
+ "trying",
2314
+ "tte",
2315
+ "tts",
2316
+ "tucson",
2317
+ "tulsa",
2318
+ "tup",
2319
+ "tx",
2320
+ "t\u2019s",
2321
+ "u",
2322
+ "u.",
2323
+ "ual",
2324
+ "uce",
2325
+ "uch",
2326
+ "uff",
2327
+ "ug.",
2328
+ "uge",
2329
+ "ugh",
2330
+ "uis",
2331
+ "ul.",
2332
+ "ula",
2333
+ "uld",
2334
+ "ult",
2335
+ "ulu",
2336
+ "umbrella",
2337
+ "un.",
2338
+ "und",
2339
+ "understand",
2340
+ "understanding",
2341
+ "understands",
2342
+ "underwater",
2343
+ "underwriter",
2344
+ "underwriters",
2345
+ "underwriting",
2346
+ "unnecessary",
2347
+ "unt",
2348
+ "updated",
2349
+ "ure",
2350
+ "urg",
2351
+ "uri",
2352
+ "us",
2353
+ "use",
2354
+ "ust",
2355
+ "ut",
2356
+ "utah",
2357
+ "uth",
2358
+ "uts",
2359
+ "v",
2360
+ "v.",
2361
+ "v.s",
2362
+ "v.s.",
2363
+ "v.v",
2364
+ "v_v",
2365
+ "va",
2366
+ "va.",
2367
+ "valuation",
2368
+ "value",
2369
+ "various",
2370
+ "vast",
2371
+ "ve",
2372
+ "ved",
2373
+ "vegas",
2374
+ "ven",
2375
+ "ver",
2376
+ "vermont",
2377
+ "versed",
2378
+ "vertical",
2379
+ "very",
2380
+ "vin",
2381
+ "virginia",
2382
+ "vista",
2383
+ "vital",
2384
+ "vs",
2385
+ "vs.",
2386
+ "vt",
2387
+ "w",
2388
+ "w's",
2389
+ "w.",
2390
+ "w/o",
2391
+ "wa",
2392
+ "want",
2393
+ "was",
2394
+ "wash",
2395
+ "wash.",
2396
+ "washington",
2397
+ "way",
2398
+ "wayne",
2399
+ "ways",
2400
+ "we",
2401
+ "wed",
2402
+ "well",
2403
+ "wellbeing",
2404
+ "were",
2405
+ "west",
2406
+ "what",
2407
+ "what's",
2408
+ "what\u2019s",
2409
+ "when",
2410
+ "when's",
2411
+ "when\u2019s",
2412
+ "where",
2413
+ "where's",
2414
+ "where\u2019s",
2415
+ "who",
2416
+ "who's",
2417
+ "whole",
2418
+ "who\u2019s",
2419
+ "why",
2420
+ "why's",
2421
+ "why\u2019s",
2422
+ "wi",
2423
+ "wichita",
2424
+ "wide",
2425
+ "will",
2426
+ "winston",
2427
+ "wis",
2428
+ "wis.",
2429
+ "wisconsin",
2430
+ "with",
2431
+ "without",
2432
+ "wo",
2433
+ "work",
2434
+ "working",
2435
+ "works",
2436
+ "workshop",
2437
+ "workshops",
2438
+ "worth",
2439
+ "would",
2440
+ "wv",
2441
+ "wy",
2442
+ "wyoming",
2443
+ "w\u2019s",
2444
+ "x",
2445
+ "x'",
2446
+ "x'x",
2447
+ "x'xxxx",
2448
+ "x.",
2449
+ "x.X",
2450
+ "x.d",
2451
+ "x.x",
2452
+ "x.x.",
2453
+ "x/x",
2454
+ "xD",
2455
+ "xDD",
2456
+ "xX",
2457
+ "xXX",
2458
+ "x_X",
2459
+ "x_d",
2460
+ "x_x",
2461
+ "xas",
2462
+ "xd",
2463
+ "xdd",
2464
+ "xed",
2465
+ "xx",
2466
+ "xx'",
2467
+ "xx'x",
2468
+ "xx'xx",
2469
+ "xx.",
2470
+ "xxx",
2471
+ "xxx'x",
2472
+ "xxx/xx",
2473
+ "xxxx",
2474
+ "xxxx'",
2475
+ "xxxx'x",
2476
+ "xxxx\u2019",
2477
+ "xxxx\u2019x",
2478
+ "xxx\u2019x",
2479
+ "xx\u2019",
2480
+ "xx\u2019x",
2481
+ "xx\u2019xx",
2482
+ "x\u2019",
2483
+ "x\u2019x",
2484
+ "x\u2019xxxx",
2485
+ "x\ufe35x",
2486
+ "y",
2487
+ "y'",
2488
+ "y's",
2489
+ "y.",
2490
+ "yer",
2491
+ "yne",
2492
+ "yonkers",
2493
+ "york",
2494
+ "you",
2495
+ "your",
2496
+ "y\u2019",
2497
+ "y\u2019s",
2498
+ "z",
2499
+ "z.",
2500
+ "zed",
2501
+ "|",
2502
+ "}",
2503
+ "\u00a0",
2504
+ "\u00ac",
2505
+ "\u00ac_\u00ac",
2506
+ "\u00af",
2507
+ "\u00af\\(x)/\u00af",
2508
+ "\u00af\\(\u30c4)/\u00af",
2509
+ "\u00b0",
2510
+ "\u00b0C.",
2511
+ "\u00b0F.",
2512
+ "\u00b0K.",
2513
+ "\u00b0X.",
2514
+ "\u00b0c.",
2515
+ "\u00b0f.",
2516
+ "\u00b0k.",
2517
+ "\u00b0x.",
2518
+ "\u00e4",
2519
+ "\u00e4.",
2520
+ "\u00f6",
2521
+ "\u00f6.",
2522
+ "\u00fc",
2523
+ "\u00fc.",
2524
+ "\u0ca0",
2525
+ "\u0ca0_\u0ca0",
2526
+ "\u0ca0\ufe35\u0ca0",
2527
+ "\u2013",
2528
+ "\u2014",
2529
+ "\u2018",
2530
+ "\u2018S",
2531
+ "\u2018X",
2532
+ "\u2018s",
2533
+ "\u2018x",
2534
+ "\u2019",
2535
+ "\u2019-(",
2536
+ "\u2019-)",
2537
+ "\u2019Cause",
2538
+ "\u2019Cos",
2539
+ "\u2019Coz",
2540
+ "\u2019Cuz",
2541
+ "\u2019S",
2542
+ "\u2019X",
2543
+ "\u2019Xxx",
2544
+ "\u2019Xxxxx",
2545
+ "\u2019am",
2546
+ "\u2019bout",
2547
+ "\u2019cause",
2548
+ "\u2019cos",
2549
+ "\u2019coz",
2550
+ "\u2019cuz",
2551
+ "\u2019d",
2552
+ "\u2019em",
2553
+ "\u2019ll",
2554
+ "\u2019m",
2555
+ "\u2019nuff",
2556
+ "\u2019re",
2557
+ "\u2019s",
2558
+ "\u2019ve",
2559
+ "\u2019x",
2560
+ "\u2019xx",
2561
+ "\u2019xxx",
2562
+ "\u2019xxxx",
2563
+ "\u2019y",
2564
+ "\u2019\u2019",
2565
+ "\u2501",
2566
+ "\u253b",
2567
+ "\u253b\u2501\u253b",
2568
+ "\u256f",
2569
+ "\u25a1",
2570
+ "\ufe35",
2571
+ "\uff09"
2572
+ ]
both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/vocab/vectors ADDED
Binary file (128 Bytes). View file
 
both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/vocab/vectors.cfg ADDED
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+ {
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+ "mode":"default"
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+ }
both_models/spacy_ner_model/en_pipeline/meta.json ADDED
@@ -0,0 +1,68 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+ "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_version":">=3.6.0,<3.7.0",
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+ "spacy_git_version":"6fc153a26",
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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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+ },
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+ "labels":{
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+ "tok2vec":[
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+
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+ ],
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+ "entity_ruler":[
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+ "STATE",
24
+ "STATE_CODE",
25
+ "ZIPCODE"
26
+ ],
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+ "ner":[
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+ "CITY",
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+ "STATE",
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+ "VERTICAL"
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+ ]
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+ },
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+ "pipeline":[
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+ "tok2vec",
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+ "entity_ruler",
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+ "ner"
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+ ],
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+ "components":[
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+ "tok2vec",
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+ "entity_ruler",
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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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+ "performance":{
47
+ "ents_f":0.9981834696,
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+ "ents_p":0.9990909091,
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+ "ents_r":0.997277677,
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+ "ents_per_type":{
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+ "VERTICAL":{
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+ "p":1.0,
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+ "r":0.9966499162,
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+ "f":0.9983221477
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+ },
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+ "CITY":{
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+ "p":0.998019802,
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+ "r":0.998019802,
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+ "f":0.998019802
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+ }
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+ },
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+ "tok2vec_loss":208.7829439196,
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+ "ner_loss":27.6274758303
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+ },
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+ "requirements":[
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+
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+ ]
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+ }
both_models/spacy_ner_model/meta.json ADDED
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1
+ {
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+ "lang":"en",
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+ "name":"pipeline",
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+ "version":"0.0.0",
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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_version":">=3.6.0,<3.7.0",
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+ "spacy_git_version":"6fc153a26",
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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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+ },
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+ "labels":{
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+ "tok2vec":[
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+
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+ ],
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+ "entity_ruler":[
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+ "STATE",
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+ "STATE_CODE",
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+ "ZIPCODE"
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+ ],
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+ "ner":[
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+ "CITY",
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+ "STATE",
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+ "VERTICAL"
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+ ]
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+ },
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+ "pipeline":[
34
+ "tok2vec",
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+ "entity_ruler",
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+ "ner"
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+ ],
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+ "components":[
39
+ "tok2vec",
40
+ "entity_ruler",
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+ "ner"
42
+ ],
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+ "disabled":[
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+
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+ ],
46
+ "performance":{
47
+ "ents_f":0.9981834696,
48
+ "ents_p":0.9990909091,
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+ "ents_r":0.997277677,
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+ "ents_per_type":{
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+ "VERTICAL":{
52
+ "p":1.0,
53
+ "r":0.9966499162,
54
+ "f":0.9983221477
55
+ },
56
+ "CITY":{
57
+ "p":0.998019802,
58
+ "r":0.998019802,
59
+ "f":0.998019802
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+ }
61
+ },
62
+ "tok2vec_loss":208.7829439196,
63
+ "ner_loss":27.6274758303
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+ },
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+ "requirements":[
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+
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+ ]
68
+ }
both_models/spacy_ner_model/setup.py ADDED
@@ -0,0 +1,72 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python
2
+ import io
3
+ import json
4
+ from os import path, walk
5
+ from shutil import copy
6
+ from setuptools import setup
7
+
8
+
9
+ def load_meta(fp):
10
+ with io.open(fp, encoding='utf8') as f:
11
+ return json.load(f)
12
+
13
+
14
+ def load_readme(fp):
15
+ if path.exists(fp):
16
+ with io.open(fp, encoding='utf8') as f:
17
+ return f.read()
18
+ return ""
19
+
20
+
21
+ def list_files(data_dir):
22
+ output = []
23
+ for root, _, filenames in walk(data_dir):
24
+ for filename in filenames:
25
+ if not filename.startswith('.'):
26
+ output.append(path.join(root, filename))
27
+ output = [path.relpath(p, path.dirname(data_dir)) for p in output]
28
+ output.append('meta.json')
29
+ return output
30
+
31
+
32
+ def list_requirements(meta):
33
+ parent_package = meta.get('parent_package', 'spacy')
34
+ requirements = [parent_package + meta['spacy_version']]
35
+ if 'setup_requires' in meta:
36
+ requirements += meta['setup_requires']
37
+ if 'requirements' in meta:
38
+ requirements += meta['requirements']
39
+ return requirements
40
+
41
+
42
+ def setup_package():
43
+ root = path.abspath(path.dirname(__file__))
44
+ meta_path = path.join(root, 'meta.json')
45
+ meta = load_meta(meta_path)
46
+ readme_path = path.join(root, 'README.md')
47
+ readme = load_readme(readme_path)
48
+ model_name = str(meta['lang'] + '_' + meta['name'])
49
+ model_dir = path.join(model_name, model_name + '-' + meta['version'])
50
+
51
+ copy(meta_path, path.join(model_name))
52
+ copy(meta_path, model_dir)
53
+
54
+ setup(
55
+ name=model_name,
56
+ description=meta.get('description'),
57
+ long_description=readme,
58
+ author=meta.get('author'),
59
+ author_email=meta.get('email'),
60
+ url=meta.get('url'),
61
+ version=meta['version'],
62
+ license=meta.get('license'),
63
+ packages=[model_name],
64
+ package_data={model_name: list_files(model_dir)},
65
+ install_requires=list_requirements(meta),
66
+ zip_safe=False,
67
+ entry_points={'spacy_models': ['{m} = {m}'.format(m=model_name)]}
68
+ )
69
+
70
+
71
+ if __name__ == '__main__':
72
+ setup_package()