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  1. both_models/sentence_transformer_model/README.md +0 -88
  2. both_models/sentence_transformer_model/config.json +0 -24
  3. both_models/sentence_transformer_model/config_sentence_transformers.json +0 -7
  4. both_models/sentence_transformer_model/modules.json +0 -20
  5. both_models/sentence_transformer_model/pytorch_model.bin +0 -3
  6. both_models/sentence_transformer_model/sentence_bert_config.json +0 -4
  7. both_models/sentence_transformer_model/special_tokens_map.json +0 -15
  8. both_models/sentence_transformer_model/tokenizer.json +0 -0
  9. both_models/sentence_transformer_model/tokenizer_config.json +0 -15
  10. both_models/sentence_transformer_model/vocab.txt +0 -0
  11. both_models/spacy_ner_model/MANIFEST.in +0 -4
  12. both_models/spacy_ner_model/README.md +0 -34
  13. both_models/spacy_ner_model/en_pipeline.egg-info/PKG-INFO +0 -41
  14. both_models/spacy_ner_model/en_pipeline.egg-info/SOURCES.txt +0 -29
  15. both_models/spacy_ner_model/en_pipeline.egg-info/dependency_links.txt +0 -1
  16. both_models/spacy_ner_model/en_pipeline.egg-info/entry_points.txt +0 -2
  17. both_models/spacy_ner_model/en_pipeline.egg-info/not-zip-safe +0 -1
  18. both_models/spacy_ner_model/en_pipeline.egg-info/requires.txt +0 -1
  19. both_models/spacy_ner_model/en_pipeline.egg-info/top_level.txt +0 -1
  20. both_models/spacy_ner_model/en_pipeline/__init__.py +0 -10
  21. both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/README.md +0 -34
  22. both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/config.cfg +0 -153
  23. both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/entity_ruler/cfg +0 -5
  24. both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/entity_ruler/patterns.jsonl +0 -101
  25. both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/meta.json +0 -68
  26. both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/ner/cfg +0 -13
  27. both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/ner/model +0 -0
  28. both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/ner/moves +0 -1
  29. both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/tok2vec/cfg +0 -3
  30. both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/tok2vec/model +0 -3
  31. both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/tokenizer +0 -3
  32. both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/vocab/key2row +0 -1
  33. both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/vocab/lookups.bin +0 -3
  34. both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/vocab/strings.json +0 -2572
  35. both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/vocab/vectors +0 -0
  36. both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/vocab/vectors.cfg +0 -3
  37. both_models/spacy_ner_model/en_pipeline/meta.json +0 -68
  38. both_models/spacy_ner_model/meta.json +0 -68
  39. both_models/spacy_ner_model/setup.py +0 -72
both_models/sentence_transformer_model/README.md DELETED
@@ -1,88 +0,0 @@
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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
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- 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
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-
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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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-
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-
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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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- ```
51
- {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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- ```
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-
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- **Loss**:
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-
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- `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
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-
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- Parameters of the fit()-Method:
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- ```
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- {
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- "epochs": 2,
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- "evaluation_steps": 1000,
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- "evaluator": "NoneType",
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- "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
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- ```
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- SentenceTransformer(
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- (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
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- (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})
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- (2): Normalize()
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- )
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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 -->
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
both_models/sentence_transformer_model/config.json DELETED
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- {
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- "_name_or_path": "/Users/bharathkumarnune/.cache/torch/sentence_transformers/sentence-transformers_all-mpnet-base-v2/",
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- "architectures": [
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- "MPNetModel"
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- ],
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- "attention_probs_dropout_prob": 0.1,
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- "bos_token_id": 0,
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- "eos_token_id": 2,
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- "hidden_act": "gelu",
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- "hidden_dropout_prob": 0.1,
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- "hidden_size": 768,
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- "torch_dtype": "float32",
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- "vocab_size": 30527
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- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
both_models/sentence_transformer_model/config_sentence_transformers.json DELETED
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- {
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- "__version__": {
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- "sentence_transformers": "2.0.0",
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- "transformers": "4.6.1",
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both_models/sentence_transformer_model/tokenizer.json DELETED
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both_models/sentence_transformer_model/tokenizer_config.json DELETED
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both_models/sentence_transformer_model/vocab.txt DELETED
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both_models/spacy_ner_model/MANIFEST.in DELETED
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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
 
 
 
 
 
both_models/spacy_ner_model/README.md DELETED
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- | Feature | Description |
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- | --- | --- |
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- | **Name** | `en_pipeline` |
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- | **Version** | `0.0.0` |
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- | **spaCy** | `>=3.6.0,<3.7.0` |
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- | **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) |
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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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-
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- ### Label Scheme
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-
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- <details>
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-
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- <summary>View label scheme (6 labels for 2 components)</summary>
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-
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- | Component | Labels |
20
- | --- | --- |
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- | **`entity_ruler`** | `STATE`, `STATE_CODE`, `ZIPCODE` |
22
- | **`ner`** | `CITY`, `STATE`, `VERTICAL` |
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-
24
- </details>
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-
26
- ### Accuracy
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-
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- | Type | Score |
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- | --- | --- |
30
- | `ENTS_F` | 99.82 |
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- | `ENTS_P` | 99.91 |
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- | `ENTS_R` | 99.73 |
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- | `TOK2VEC_LOSS` | 20878.29 |
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- | `NER_LOSS` | 2762.75 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
both_models/spacy_ner_model/en_pipeline.egg-info/PKG-INFO DELETED
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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:
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- Author-email:
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-
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- | Feature | Description |
9
- | --- | --- |
10
- | **Name** | `en_pipeline` |
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- | **Version** | `0.0.0` |
12
- | **spaCy** | `>=3.6.0,<3.7.0` |
13
- | **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) |
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 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
both_models/spacy_ner_model/en_pipeline.egg-info/SOURCES.txt DELETED
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- MANIFEST.in
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- README.md
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- meta.json
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- setup.py
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- en_pipeline/__init__.py
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- en_pipeline/meta.json
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- en_pipeline.egg-info/PKG-INFO
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- en_pipeline.egg-info/SOURCES.txt
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- en_pipeline.egg-info/dependency_links.txt
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- en_pipeline.egg-info/entry_points.txt
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- en_pipeline.egg-info/not-zip-safe
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- en_pipeline.egg-info/requires.txt
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- en_pipeline.egg-info/top_level.txt
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- en_pipeline/en_pipeline-0.0.0/README.md
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- en_pipeline/en_pipeline-0.0.0/config.cfg
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- en_pipeline/en_pipeline-0.0.0/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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- en_pipeline/en_pipeline-0.0.0/tok2vec/cfg
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- en_pipeline/en_pipeline-0.0.0/tok2vec/model
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- en_pipeline/en_pipeline-0.0.0/vocab/key2row
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- en_pipeline/en_pipeline-0.0.0/vocab/lookups.bin
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- en_pipeline/en_pipeline-0.0.0/vocab/strings.json
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- en_pipeline/en_pipeline-0.0.0/vocab/vectors
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- en_pipeline/en_pipeline-0.0.0/vocab/vectors.cfg
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
both_models/spacy_ner_model/en_pipeline.egg-info/dependency_links.txt DELETED
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-
 
 
both_models/spacy_ner_model/en_pipeline.egg-info/entry_points.txt DELETED
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- [spacy_models]
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- en_pipeline = en_pipeline
 
 
 
both_models/spacy_ner_model/en_pipeline.egg-info/not-zip-safe DELETED
@@ -1 +0,0 @@
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-
 
 
both_models/spacy_ner_model/en_pipeline.egg-info/requires.txt DELETED
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- spacy<3.7.0,>=3.6.0
 
 
both_models/spacy_ner_model/en_pipeline.egg-info/top_level.txt DELETED
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- en_pipeline
 
 
both_models/spacy_ner_model/en_pipeline/__init__.py DELETED
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- from pathlib import Path
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- from spacy.util import load_model_from_init_py, get_model_meta
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-
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-
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-
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- __version__ = get_model_meta(Path(__file__).parent)['version']
7
-
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-
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- 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 DELETED
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- | 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 |
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- | `TOK2VEC_LOSS` | 20878.29 |
34
- | `NER_LOSS` | 2762.75 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/config.cfg DELETED
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- [paths]
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- train = "./train.spacy"
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- dev = "./test.spacy"
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- vectors = null
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- init_tok2vec = null
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-
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- [system]
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- gpu_allocator = null
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- seed = 0
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-
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- [nlp]
12
- lang = "en"
13
- pipeline = ["tok2vec","entity_ruler","ner"]
14
- batch_size = 1000
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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
19
- tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
20
-
21
- [components]
22
-
23
- [components.entity_ruler]
24
- factory = "entity_ruler"
25
- ent_id_sep = "||"
26
- matcher_fuzzy_compare = {"@misc":"spacy.levenshtein_compare.v1"}
27
- overwrite_ents = false
28
- phrase_matcher_attr = null
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- scorer = {"@scorers":"spacy.entity_ruler_scorer.v1"}
30
- validate = false
31
-
32
- [components.ner]
33
- factory = "ner"
34
- incorrect_spans_key = null
35
- moves = null
36
- scorer = {"@scorers":"spacy.ner_scorer.v1"}
37
- update_with_oracle_cut_size = 100
38
-
39
- [components.ner.model]
40
- @architectures = "spacy.TransitionBasedParser.v2"
41
- state_type = "ner"
42
- extra_state_tokens = false
43
- hidden_width = 64
44
- maxout_pieces = 2
45
- use_upper = true
46
- nO = null
47
-
48
- [components.ner.model.tok2vec]
49
- @architectures = "spacy.Tok2VecListener.v1"
50
- width = ${components.tok2vec.model.encode.width}
51
- upstream = "*"
52
-
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"]
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- rows = [5000,1000,2500,2500]
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-
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- [components.tok2vec.model.encode]
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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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- [corpora]
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- [pretraining]
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- "=(",
257
- "=)",
258
- "=/",
259
- "=3",
260
- "=D",
261
- "=X",
262
- "=[",
263
- "=]",
264
- "=d",
265
- "=|",
266
- ">",
267
- ">.<",
268
- ">.>",
269
- ">:(",
270
- ">:o",
271
- ">:x",
272
- "><(((*>",
273
- "?",
274
- "@",
275
- "@_@",
276
- "A",
277
- "AAP",
278
- "AK",
279
- "AL",
280
- "AR",
281
- "ATE",
282
- "AZ",
283
- "Adm",
284
- "Adm.",
285
- "Ai",
286
- "Ak",
287
- "Ak.",
288
- "Akron",
289
- "Ala",
290
- "Ala.",
291
- "Alabama",
292
- "Alaska",
293
- "Albuquerque",
294
- "Amarillo",
295
- "An",
296
- "Ana",
297
- "Anaheim",
298
- "Anchorage",
299
- "Angeles",
300
- "Antonio",
301
- "Apr",
302
- "Apr.",
303
- "April",
304
- "Are",
305
- "Ariz",
306
- "Ariz.",
307
- "Arizona",
308
- "Ark",
309
- "Ark.",
310
- "Arkansas",
311
- "Arlington",
312
- "Assets",
313
- "Atlanta",
314
- "Attend",
315
- "Aug",
316
- "Aug.",
317
- "August",
318
- "Aurora",
319
- "Austin",
320
- "B",
321
- "Bakersfield",
322
- "Baltimore",
323
- "Baton",
324
- "Beach",
325
- "Bernardino",
326
- "Birmingham",
327
- "Boise",
328
- "Boost",
329
- "Boston",
330
- "Bros",
331
- "Bros.",
332
- "Buffalo",
333
- "Build",
334
- "C",
335
- "C'm",
336
- "C++",
337
- "C.",
338
- "CAL",
339
- "CITY",
340
- "CO",
341
- "CT",
342
- "Ca",
343
- "Calif",
344
- "Calif.",
345
- "California",
346
- "Can",
347
- "Cause",
348
- "Chandler",
349
- "Charlotte",
350
- "Chesapeake",
351
- "Chicago",
352
- "Choose",
353
- "Christi",
354
- "Chula",
355
- "Cincinnati",
356
- "City",
357
- "Cleveland",
358
- "Co",
359
- "Co.",
360
- "Colo",
361
- "Colo.",
362
- "Colorado",
363
- "Columbus",
364
- "Conn",
365
- "Conn.",
366
- "Connect",
367
- "Connecticut",
368
- "Consult",
369
- "Contact",
370
- "Control",
371
- "Corp",
372
- "Corp.",
373
- "Corpus",
374
- "Cos",
375
- "Could",
376
- "Coz",
377
- "Create",
378
- "Curious",
379
- "Cut",
380
- "Cuz",
381
- "C\u2019m",
382
- "D",
383
- "D.",
384
- "D.C.",
385
- "DE",
386
- "Dallas",
387
- "Dare",
388
- "Debtor",
389
- "Dec",
390
- "Dec.",
391
- "December",
392
- "Del",
393
- "Del.",
394
- "Delaware",
395
- "Denver",
396
- "Des",
397
- "Detroit",
398
- "Did",
399
- "Diego",
400
- "Discover",
401
- "Do",
402
- "Does",
403
- "Doin",
404
- "Doin'",
405
- "Doin\u2019",
406
- "Dr",
407
- "Dr.",
408
- "Durham",
409
- "E",
410
- "E.G.",
411
- "E.g",
412
- "E.g.",
413
- "Effective",
414
- "Efficient",
415
- "El",
416
- "Enhance",
417
- "Ensure",
418
- "Experience",
419
- "Expert",
420
- "Explore",
421
- "F",
422
- "F.",
423
- "FL",
424
- "Feb",
425
- "Feb.",
426
- "February",
427
- "Find",
428
- "Finding",
429
- "Fla",
430
- "Fla.",
431
- "Florida",
432
- "Fontana",
433
- "Fort",
434
- "Francisco",
435
- "Fremont",
436
- "Fresno",
437
- "G",
438
- "GA",
439
- "GAAP",
440
- "Ga",
441
- "Ga.",
442
- "Garland",
443
- "Gen",
444
- "Gen.",
445
- "Georgia",
446
- "Get",
447
- "Getting",
448
- "Gilbert",
449
- "Glendale",
450
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451
- "Goin'",
452
- "Goin\u2019",
453
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454
- "Got",
455
- "Gov",
456
- "Gov.",
457
- "Grand",
458
- "Greensboro",
459
- "H",
460
- "HI",
461
- "Had",
462
- "Has",
463
- "Have",
464
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465
- "Havin'",
466
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467
- "He",
468
- "He's",
469
- "Henderson",
470
- "He\u2019s",
471
- "Hialeah",
472
- "Hire",
473
- "Honolulu",
474
- "Houston",
475
- "How",
476
- "How's",
477
- "How\u2019s",
478
- "Huntington",
479
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480
- "I.E.",
481
- "I.e",
482
- "I.e.",
483
- "IA",
484
- "IL",
485
- "IN",
486
- "ITY",
487
- "Ia",
488
- "Ia.",
489
- "Id",
490
- "Id.",
491
- "Idaho",
492
- "Ill",
493
- "Ill.",
494
- "Illinois",
495
- "Improve",
496
- "In",
497
- "Inc",
498
- "Inc.",
499
- "Ind",
500
- "Ind.",
501
- "Indiana",
502
- "Indianapolis",
503
- "Iowa",
504
- "Irvine",
505
- "Irving",
506
- "Is",
507
- "It",
508
- "It's",
509
- "It\u2019s",
510
- "J",
511
- "Jacksonville",
512
- "Jan",
513
- "Jan.",
514
- "January",
515
- "Jersey",
516
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517
- "Jose",
518
- "Jr",
519
- "Jr.",
520
- "Jul",
521
- "Jul.",
522
- "July",
523
- "Jun",
524
- "Jun.",
525
- "June",
526
- "K",
527
- "K.",
528
- "KS",
529
- "KY",
530
- "Kan",
531
- "Kan.",
532
- "Kans",
533
- "Kans.",
534
- "Kansas",
535
- "Keep",
536
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537
- "Ky",
538
- "Ky.",
539
- "L",
540
- "LA",
541
- "La",
542
- "La.",
543
- "Laredo",
544
- "Las",
545
- "Learn",
546
- "Ledger",
547
- "Let",
548
- "Let's",
549
- "Let\u2019s",
550
- "Lexington",
551
- "Lincoln",
552
- "Long",
553
- "Looking",
554
- "Los",
555
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556
- "Louisiana",
557
- "Louisville",
558
- "Lovin",
559
- "Lovin'",
560
- "Lovin\u2019",
561
- "Ltd",
562
- "Ltd.",
563
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564
- "M",
565
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566
- "MD",
567
- "ME",
568
- "MI",
569
- "MN",
570
- "MO",
571
- "MS",
572
- "MT",
573
- "Ma'am",
574
- "Madison",
575
- "Maintain",
576
- "Make",
577
- "Manage",
578
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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
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623
- "N",
624
- "N.C.",
625
- "N.D.",
626
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627
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628
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629
- "N.Y.",
630
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631
- "ND",
632
- "NE",
633
- "NH",
634
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635
- "NM",
636
- "NV",
637
- "NY",
638
- "Nashville",
639
- "Neb",
640
- "Neb.",
641
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642
- "Nebr.",
643
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644
- "Need",
645
- "Nev",
646
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647
- "Nevada",
648
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649
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650
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651
- "New Mexico",
652
- "New York",
653
- "Newark",
654
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655
- "North",
656
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657
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658
- "Not",
659
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660
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661
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662
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663
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664
- "November",
665
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666
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667
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668
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669
- "O'clock",
670
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671
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672
- "ODE",
673
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674
- "OK",
675
- "OR",
676
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677
- "O_o",
678
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679
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680
- "Oct.",
681
- "October",
682
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683
- "Okla.",
684
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685
- "Ol",
686
- "Ol'",
687
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688
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689
- "Opt",
690
- "Ore",
691
- "Ore.",
692
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693
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694
- "Orleans",
695
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696
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697
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698
- "PA",
699
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700
- "Pa.",
701
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702
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703
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704
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705
- "Ph.D.",
706
- "Philadelphia",
707
- "Phoenix",
708
- "Pittsburgh",
709
- "Plano",
710
- "Portland",
711
- "Preparing",
712
- "Prof",
713
- "Prof.",
714
- "R",
715
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716
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717
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718
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719
- "Reach",
720
- "Reconciliation",
721
- "Reduce",
722
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723
- "Reno",
724
- "Rep",
725
- "Rep.",
726
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727
- "Rev.",
728
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729
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730
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731
- "S",
732
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733
- "SC",
734
- "SD",
735
- "STATE",
736
- "STATE_CODE",
737
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738
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739
- "San",
740
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741
- "Scottsdale",
742
- "Searching",
743
- "Seattle",
744
- "Secure",
745
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746
- "Seeking",
747
- "Selling",
748
- "Sen",
749
- "Sen.",
750
- "Sep",
751
- "Sep.",
752
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753
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754
- "September",
755
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756
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757
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758
- "She\u2019s",
759
- "Should",
760
- "Shreveport",
761
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762
- "Somethin'",
763
- "Somethin\u2019",
764
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765
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766
- "Springs",
767
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768
- "St.",
769
- "Staging",
770
- "Stay",
771
- "Stockton",
772
- "Streamline",
773
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774
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775
- "TX",
776
- "Tacoma",
777
- "Take",
778
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779
- "Tampa",
780
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781
- "Tenn.",
782
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783
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784
- "That's",
785
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786
- "The",
787
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788
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789
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790
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791
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792
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793
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794
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795
- "Those",
796
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797
- "Top",
798
- "Tucson",
799
- "Tulsa",
800
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801
- "UT",
802
- "Understand",
803
- "Understanding",
804
- "V",
805
- "V.V",
806
- "VA",
807
- "VERTICAL",
808
- "VT",
809
- "V_V",
810
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811
- "Va.",
812
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813
- "Virginia",
814
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815
- "W",
816
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817
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818
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819
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820
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821
- "Wash",
822
- "Wash.",
823
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824
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825
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826
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827
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828
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829
- "What\u2019s",
830
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831
- "When's",
832
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833
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834
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835
- "Where\u2019s",
836
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837
- "Who's",
838
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839
- "Why",
840
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841
- "Why\u2019s",
842
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843
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844
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845
- "Wis.",
846
- "Wisconsin",
847
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848
- "Working",
849
- "Worth",
850
- "Would",
851
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852
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853
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854
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855
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856
- "X.X.",
857
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858
- "X.x.",
859
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860
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861
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862
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863
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864
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865
- "X_X",
866
- "X_x",
867
- "Xx",
868
- "Xx'",
869
- "Xx'x",
870
- "Xx'xx",
871
- "Xx.",
872
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873
- "Xxx",
874
- "Xxx'x",
875
- "Xxx.",
876
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877
- "Xxxx'",
878
- "Xxxx'x",
879
- "Xxxx.",
880
- "Xxxxx",
881
- "Xxxxx'",
882
- "Xxxxx'x",
883
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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
- "[",
900
- "[-:",
901
- "[:",
902
- "[=",
903
- "\\",
904
- "\\\")",
905
- "\\n",
906
- "\\t",
907
- "\\x",
908
- "]",
909
- "]=",
910
- "^",
911
- "^_^",
912
- "^__^",
913
- "^___^",
914
- "_*)",
915
- "_-)",
916
- "_.)",
917
- "_<)",
918
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919
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920
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921
- "_\u00ac)",
922
- "_\u0ca0)",
923
- "a",
924
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925
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926
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927
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928
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929
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930
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931
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932
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933
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934
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935
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936
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937
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938
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939
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940
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941
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942
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943
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944
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945
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946
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947
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948
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949
- "advised",
950
- "advisor",
951
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952
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953
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954
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955
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956
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957
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958
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959
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960
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961
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962
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963
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964
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965
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966
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967
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968
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969
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970
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971
- "ak",
972
- "ak.",
973
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974
- "akron",
975
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976
- "ala",
977
- "ala.",
978
- "alabama",
979
- "alaska",
980
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981
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982
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983
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984
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985
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986
- "ama",
987
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988
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989
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990
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991
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992
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993
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994
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995
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996
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997
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998
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999
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1000
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1001
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1002
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1003
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1004
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1005
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1006
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1007
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1008
- "anyone",
1009
- "appeal",
1010
- "application",
1011
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1012
- "apr.",
1013
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1014
- "ar.",
1015
- "ard",
1016
- "are",
1017
- "ariz",
1018
- "ariz.",
1019
- "arizona",
1020
- "ark",
1021
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1022
- "arkansas",
1023
- "arlington",
1024
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1025
- "art",
1026
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1027
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1028
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1029
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1030
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1031
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1032
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1033
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1034
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1035
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1036
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1037
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1038
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1039
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1040
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1041
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1042
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1043
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1044
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1045
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1046
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1047
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1048
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1049
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1050
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1051
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1052
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1053
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1054
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1055
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1056
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1057
- "b.",
1058
- "back",
1059
- "bakersfield",
1060
- "balance",
1061
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1062
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1063
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1064
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1065
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1066
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1067
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1068
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1069
- "become",
1070
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1843
- "nothin'",
1844
- "nothing",
1845
- "nothin\u2019",
1846
- "nov",
1847
- "nov.",
1848
- "now",
1849
- "ns.",
1850
- "nse",
1851
- "nt",
1852
- "nt.",
1853
- "nta",
1854
- "nto",
1855
- "nts",
1856
- "nuances",
1857
- "nuff",
1858
- "nuthin",
1859
- "nuthin'",
1860
- "nuthin\u2019",
1861
- "nv",
1862
- "ny",
1863
- "n\u2019s",
1864
- "n\u2019t",
1865
- "o",
1866
- "o'clock",
1867
- "o's",
1868
- "o.",
1869
- "o.0",
1870
- "o.O",
1871
- "o.o",
1872
- "o_0",
1873
- "o_O",
1874
- "o_o",
1875
- "oakland",
1876
- "oan",
1877
- "oat",
1878
- "ock",
1879
- "oct",
1880
- "oct.",
1881
- "ode",
1882
- "ods",
1883
- "oes",
1884
- "of",
1885
- "of.",
1886
- "offer",
1887
- "offering",
1888
- "offerings",
1889
- "offers",
1890
- "ogs",
1891
- "oh",
1892
- "ohio",
1893
- "oid",
1894
- "oin",
1895
- "ois",
1896
- "oit",
1897
- "ok",
1898
- "okla",
1899
- "okla.",
1900
- "oklahoma",
1901
- "oks",
1902
- "ol",
1903
- "ol'",
1904
- "old",
1905
- "ole",
1906
- "olk",
1907
- "oln",
1908
- "olo",
1909
- "ol\u2019",
1910
- "oma",
1911
- "omaha",
1912
- "ome",
1913
- "on",
1914
- "ona",
1915
- "ond",
1916
- "one",
1917
- "ones",
1918
- "ong",
1919
- "onn",
1920
- "ons",
1921
- "ont",
1922
- "ood",
1923
- "ook",
1924
- "operations",
1925
- "opportunities",
1926
- "ops",
1927
- "opt",
1928
- "optimize",
1929
- "optimum",
1930
- "option",
1931
- "options",
1932
- "or",
1933
- "ora",
1934
- "ord",
1935
- "order",
1936
- "ore",
1937
- "ore.",
1938
- "oregon",
1939
- "organized",
1940
- "origination",
1941
- "ork",
1942
- "orlando",
1943
- "orleans",
1944
- "oro",
1945
- "orp",
1946
- "ors",
1947
- "ort",
1948
- "ory",
1949
- "os.",
1950
- "ose",
1951
- "ost",
1952
- "ota",
1953
- "oth",
1954
- "ought",
1955
- "our",
1956
- "ous",
1957
- "out",
1958
- "outs",
1959
- "ov.",
1960
- "ove",
1961
- "owa",
1962
- "own",
1963
- "ows",
1964
- "o\u2019clock",
1965
- "o\u2019s",
1966
- "p",
1967
- "p.",
1968
- "p.m",
1969
- "p.m.",
1970
- "pa",
1971
- "pa.",
1972
- "packages",
1973
- "pal",
1974
- "paperwork",
1975
- "paso",
1976
- "payment",
1977
- "payments",
1978
- "ped",
1979
- "pel",
1980
- "pennsylvania",
1981
- "perfect",
1982
- "petersburg",
1983
- "ph",
1984
- "ph.d.",
1985
- "philadelphia",
1986
- "phoenix",
1987
- "pittsburgh",
1988
- "place",
1989
- "plan",
1990
- "planning",
1991
- "plano",
1992
- "plans",
1993
- "pm",
1994
- "points",
1995
- "policies",
1996
- "policy",
1997
- "portland",
1998
- "positive",
1999
- "possible",
2000
- "ppi",
2001
- "pr.",
2002
- "precise",
2003
- "premium",
2004
- "preparation",
2005
- "prepare",
2006
- "prepared",
2007
- "preparing",
2008
- "present",
2009
- "prevention",
2010
- "principal",
2011
- "principles",
2012
- "pro",
2013
- "process",
2014
- "processes",
2015
- "prof",
2016
- "prof.",
2017
- "professional",
2018
- "professionals",
2019
- "proficiently",
2020
- "profit",
2021
- "profitable",
2022
- "profits",
2023
- "programs",
2024
- "propel",
2025
- "property",
2026
- "protect",
2027
- "protection",
2028
- "protects",
2029
- "proven",
2030
- "provide",
2031
- "provided",
2032
- "provider",
2033
- "providers",
2034
- "pt.",
2035
- "purchase",
2036
- "pus",
2037
- "q",
2038
- "q.",
2039
- "qualification",
2040
- "que",
2041
- "quickly",
2042
- "quite",
2043
- "r",
2044
- "r.",
2045
- "raleigh",
2046
- "range",
2047
- "rapids",
2048
- "rate",
2049
- "rates",
2050
- "rds",
2051
- "re",
2052
- "re.",
2053
- "reach",
2054
- "readily",
2055
- "ready",
2056
- "real",
2057
- "receivables",
2058
- "recommend",
2059
- "recommendations",
2060
- "reconciliation",
2061
- "record",
2062
- "records",
2063
- "red",
2064
- "reduce",
2065
- "reducing",
2066
- "reduction",
2067
- "ree",
2068
- "refinance",
2069
- "refinancing",
2070
- "reflects",
2071
- "regular",
2072
- "regulations",
2073
- "reliable",
2074
- "rely",
2075
- "reno",
2076
- "rep",
2077
- "rep.",
2078
- "report",
2079
- "reputable",
2080
- "reputation",
2081
- "reputed",
2082
- "requirements",
2083
- "res",
2084
- "responsibility",
2085
- "retirement",
2086
- "rev",
2087
- "rev.",
2088
- "reviewed",
2089
- "reviews",
2090
- "rge",
2091
- "rgh",
2092
- "rhode",
2093
- "ri",
2094
- "richmond",
2095
- "right",
2096
- "rightly",
2097
- "rights",
2098
- "rip",
2099
- "ripe",
2100
- "risk",
2101
- "riverside",
2102
- "riz",
2103
- "rk.",
2104
- "rks",
2105
- "rly",
2106
- "rms",
2107
- "robust",
2108
- "rof",
2109
- "rol",
2110
- "role",
2111
- "rom",
2112
- "ron",
2113
- "ros",
2114
- "rouge",
2115
- "row",
2116
- "rp.",
2117
- "rs.",
2118
- "rse",
2119
- "rth",
2120
- "rts",
2121
- "rty",
2122
- "rue",
2123
- "s",
2124
- "s's",
2125
- "s.",
2126
- "s.c.",
2127
- "sacramento",
2128
- "safeguard",
2129
- "sal",
2130
- "salem",
2131
- "san",
2132
- "santa",
2133
- "sas",
2134
- "save",
2135
- "savings",
2136
- "sc",
2137
- "sco",
2138
- "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
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2273
- "that\u2019s",
2274
- "the",
2275
- "their",
2276
- "them",
2277
- "there",
2278
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2279
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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
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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 DELETED
Binary file (128 Bytes)
 
both_models/spacy_ner_model/en_pipeline/en_pipeline-0.0.0/vocab/vectors.cfg DELETED
@@ -1,3 +0,0 @@
1
- {
2
- "mode":"default"
3
- }
 
 
 
 
both_models/spacy_ner_model/en_pipeline/meta.json DELETED
@@ -1,68 +0,0 @@
1
- {
2
- "lang":"en",
3
- "name":"pipeline",
4
- "version":"0.0.0",
5
- "description":"",
6
- "author":"",
7
- "email":"",
8
- "url":"",
9
- "license":"",
10
- "spacy_version":">=3.6.0,<3.7.0",
11
- "spacy_git_version":"6fc153a26",
12
- "vectors":{
13
- "width":0,
14
- "vectors":0,
15
- "keys":0,
16
- "name":null
17
- },
18
- "labels":{
19
- "tok2vec":[
20
-
21
- ],
22
- "entity_ruler":[
23
- "STATE",
24
- "STATE_CODE",
25
- "ZIPCODE"
26
- ],
27
- "ner":[
28
- "CITY",
29
- "STATE",
30
- "VERTICAL"
31
- ]
32
- },
33
- "pipeline":[
34
- "tok2vec",
35
- "entity_ruler",
36
- "ner"
37
- ],
38
- "components":[
39
- "tok2vec",
40
- "entity_ruler",
41
- "ner"
42
- ],
43
- "disabled":[
44
-
45
- ],
46
- "performance":{
47
- "ents_f":0.9981834696,
48
- "ents_p":0.9990909091,
49
- "ents_r":0.997277677,
50
- "ents_per_type":{
51
- "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
60
- }
61
- },
62
- "tok2vec_loss":208.7829439196,
63
- "ner_loss":27.6274758303
64
- },
65
- "requirements":[
66
-
67
- ]
68
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
both_models/spacy_ner_model/meta.json DELETED
@@ -1,68 +0,0 @@
1
- {
2
- "lang":"en",
3
- "name":"pipeline",
4
- "version":"0.0.0",
5
- "description":"",
6
- "author":"",
7
- "email":"",
8
- "url":"",
9
- "license":"",
10
- "spacy_version":">=3.6.0,<3.7.0",
11
- "spacy_git_version":"6fc153a26",
12
- "vectors":{
13
- "width":0,
14
- "vectors":0,
15
- "keys":0,
16
- "name":null
17
- },
18
- "labels":{
19
- "tok2vec":[
20
-
21
- ],
22
- "entity_ruler":[
23
- "STATE",
24
- "STATE_CODE",
25
- "ZIPCODE"
26
- ],
27
- "ner":[
28
- "CITY",
29
- "STATE",
30
- "VERTICAL"
31
- ]
32
- },
33
- "pipeline":[
34
- "tok2vec",
35
- "entity_ruler",
36
- "ner"
37
- ],
38
- "components":[
39
- "tok2vec",
40
- "entity_ruler",
41
- "ner"
42
- ],
43
- "disabled":[
44
-
45
- ],
46
- "performance":{
47
- "ents_f":0.9981834696,
48
- "ents_p":0.9990909091,
49
- "ents_r":0.997277677,
50
- "ents_per_type":{
51
- "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
60
- }
61
- },
62
- "tok2vec_loss":208.7829439196,
63
- "ner_loss":27.6274758303
64
- },
65
- "requirements":[
66
-
67
- ]
68
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
both_models/spacy_ner_model/setup.py DELETED
@@ -1,72 +0,0 @@
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()