Instructions to use devrishi/roberta-retrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devrishi/roberta-retrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="devrishi/roberta-retrained")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("devrishi/roberta-retrained") model = AutoModelForTokenClassification.from_pretrained("devrishi/roberta-retrained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload 6 files
Browse files- config.json +207 -0
- merges.txt +0 -0
- special_tokens_map.json +15 -0
- tokenizer.json +0 -0
- tokenizer_config.json +59 -0
- vocab.json +0 -0
config.json
ADDED
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{
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"_name_or_path": "kunalr63/roberta-retrained",
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"architectures": [
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"RobertaForTokenClassification"
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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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"classifier_dropout": null,
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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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"id2label": {
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"0": "O",
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"1": "B-NAME",
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| 16 |
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"2": "L-NAME",
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"3": "B-C_DESIG",
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"4": "I-C_DESIG",
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"5": "L-C_DESIG",
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"6": "B-CANDIDATE_ADDRESS",
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"7": "I-CANDIDATE_ADDRESS",
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"8": "L-CANDIDATE_ADDRESS",
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"9": "B-PHONE",
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"10": "I-PHONE",
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"11": "L-PHONE",
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"12": "U-EMAIL",
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"13": "U-DOB",
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"14": "B-T_EXP",
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"15": "I-T_EXP",
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"16": "L-T_EXP",
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"17": "B-EXP_COMPANY_WISE",
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"18": "I-EXP_COMPANY_WISE",
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"19": "L-EXP_COMPANY_WISE",
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"20": "B-PROJECTS",
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"21": "I-PROJECTS",
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"22": "L-PROJECTS",
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"23": "B-SKILLS",
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"24": "L-SKILLS",
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"25": "U-SKILLS",
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"26": "I-SKILLS",
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"27": "B-CERTIFICATE",
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| 42 |
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"28": "I-CERTIFICATE",
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| 43 |
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"29": "L-CERTIFICATE",
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"30": "B-G_IN",
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"31": "I-G_IN",
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"32": "L-G_IN",
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"33": "B-G_CLG",
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"34": "I-G_CLG",
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"35": "L-G_CLG",
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"36": "U-G_YEAR",
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"37": "U-PHONE",
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"38": "B-HIGH_FROM",
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"39": "L-HIGH_FROM",
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"40": "B-HIGH_PER",
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"41": "L-HIGH_PER",
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| 56 |
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"42": "B-INTER_FROM",
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| 57 |
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"43": "L-INTER_FROM",
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"44": "B-INTER_PER",
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"45": "L-INTER_PER",
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"46": "B-GRAD_PER",
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| 61 |
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"47": "L-GRAD_PER",
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"48": "B-DOB",
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| 63 |
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"49": "I-DOB",
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| 64 |
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"50": "L-DOB",
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| 65 |
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"51": "U-NAME",
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| 66 |
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"52": "B-EMAIL",
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"53": "I-EMAIL",
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| 68 |
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"54": "L-EMAIL",
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"55": "I-INTER_FROM",
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| 70 |
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"56": "I-HIGH_FROM",
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| 71 |
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"57": "I-NAME",
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| 72 |
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"58": "B-PG_FROM",
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| 73 |
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"59": "L-PG_FROM",
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"60": "B-PG_IN",
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"61": "I-PG_IN",
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| 76 |
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"62": "L-PG_IN",
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| 77 |
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"63": "U-PG_YEAR",
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"64": "U-G_IN",
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"65": "I-PG_FROM",
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| 80 |
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"66": "U-CANDIDATE_ADDRESS",
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| 81 |
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"67": "U-G_CLG",
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| 82 |
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"68": "U-PG_IN",
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| 83 |
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"69": "B-PG_YEAR",
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| 84 |
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"70": "I-PG_YEAR",
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| 85 |
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"71": "L-PG_YEAR",
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| 86 |
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"72": "B-G_YEAR",
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| 87 |
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"73": "I-G_YEAR",
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| 88 |
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"74": "L-G_YEAR",
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| 89 |
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"75": "U-PG_FROM",
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| 90 |
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"76": "U-INTER_FROM",
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| 91 |
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"77": "U-HIGH_FROM",
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| 92 |
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"78": "U-GRAD_PER",
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| 93 |
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"79": "U-INTER_PER",
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| 94 |
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"80": "U-HIGH_PER",
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| 95 |
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"81": "U-T_EXP",
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| 96 |
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"82": "I-GRAD_PER",
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| 97 |
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"83": "U-PROJECTS",
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| 98 |
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"84": "U-CERTIFICATE",
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"85": "U-EXP_COMPANY_WISE",
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"86": "I-HIGH_PER",
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| 101 |
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"87": "I-INTER_PER"
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},
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"initializer_range": 0.02,
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| 104 |
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"intermediate_size": 3072,
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| 105 |
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"label2id": {
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| 106 |
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"B-CANDIDATE_ADDRESS": 6,
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| 107 |
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"B-CERTIFICATE": 27,
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| 108 |
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"B-C_DESIG": 3,
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| 109 |
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"B-DOB": 48,
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| 110 |
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"B-EMAIL": 52,
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| 111 |
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"B-EXP_COMPANY_WISE": 17,
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| 112 |
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"B-GRAD_PER": 46,
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| 113 |
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"B-G_CLG": 33,
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| 114 |
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"B-G_IN": 30,
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| 115 |
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"B-G_YEAR": 72,
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| 116 |
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"B-HIGH_FROM": 38,
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| 117 |
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"B-HIGH_PER": 40,
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| 118 |
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"B-INTER_FROM": 42,
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| 119 |
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"B-INTER_PER": 44,
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| 120 |
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"B-NAME": 1,
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| 121 |
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"B-PG_FROM": 58,
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| 122 |
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"B-PG_IN": 60,
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| 123 |
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"B-PG_YEAR": 69,
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| 124 |
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"B-PHONE": 9,
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| 125 |
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"B-PROJECTS": 20,
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| 126 |
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"B-SKILLS": 23,
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| 127 |
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"B-T_EXP": 14,
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| 128 |
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"I-CANDIDATE_ADDRESS": 7,
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| 129 |
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"I-CERTIFICATE": 28,
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| 130 |
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"I-C_DESIG": 4,
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| 131 |
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"I-DOB": 49,
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| 132 |
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"I-EMAIL": 53,
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| 133 |
+
"I-EXP_COMPANY_WISE": 18,
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| 134 |
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"I-GRAD_PER": 82,
|
| 135 |
+
"I-G_CLG": 34,
|
| 136 |
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"I-G_IN": 31,
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| 137 |
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"I-G_YEAR": 73,
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| 138 |
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"I-HIGH_FROM": 56,
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| 139 |
+
"I-HIGH_PER": 86,
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| 140 |
+
"I-INTER_FROM": 55,
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| 141 |
+
"I-INTER_PER": 87,
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| 142 |
+
"I-NAME": 57,
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| 143 |
+
"I-PG_FROM": 65,
|
| 144 |
+
"I-PG_IN": 61,
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| 145 |
+
"I-PG_YEAR": 70,
|
| 146 |
+
"I-PHONE": 10,
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| 147 |
+
"I-PROJECTS": 21,
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| 148 |
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"I-SKILLS": 26,
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| 149 |
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"I-T_EXP": 15,
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| 150 |
+
"L-CANDIDATE_ADDRESS": 8,
|
| 151 |
+
"L-CERTIFICATE": 29,
|
| 152 |
+
"L-C_DESIG": 5,
|
| 153 |
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"L-DOB": 50,
|
| 154 |
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"L-EMAIL": 54,
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| 155 |
+
"L-EXP_COMPANY_WISE": 19,
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| 156 |
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"L-GRAD_PER": 47,
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| 157 |
+
"L-G_CLG": 35,
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| 158 |
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"L-G_IN": 32,
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| 159 |
+
"L-G_YEAR": 74,
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| 160 |
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"L-HIGH_FROM": 39,
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| 161 |
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"L-HIGH_PER": 41,
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| 162 |
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"L-INTER_FROM": 43,
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| 163 |
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"L-INTER_PER": 45,
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| 164 |
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"L-NAME": 2,
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| 165 |
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"L-PG_FROM": 59,
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| 166 |
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"L-PG_IN": 62,
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| 167 |
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"L-PG_YEAR": 71,
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| 168 |
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"L-PHONE": 11,
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| 169 |
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"L-PROJECTS": 22,
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| 170 |
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"L-SKILLS": 24,
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| 171 |
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"L-T_EXP": 16,
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| 172 |
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"O": 0,
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| 173 |
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"U-CANDIDATE_ADDRESS": 66,
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| 174 |
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"U-CERTIFICATE": 84,
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| 175 |
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"U-DOB": 13,
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| 176 |
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"U-EMAIL": 12,
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| 177 |
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"U-EXP_COMPANY_WISE": 85,
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| 178 |
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"U-GRAD_PER": 78,
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| 179 |
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"U-G_CLG": 67,
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| 180 |
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"U-G_IN": 64,
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| 181 |
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"U-G_YEAR": 36,
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| 182 |
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"U-HIGH_FROM": 77,
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| 183 |
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"U-HIGH_PER": 80,
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| 184 |
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"U-INTER_FROM": 76,
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| 185 |
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"U-INTER_PER": 79,
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| 186 |
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"U-NAME": 51,
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| 187 |
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"U-PG_FROM": 75,
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| 188 |
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"U-PG_IN": 68,
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| 189 |
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"U-PG_YEAR": 63,
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| 190 |
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"U-PHONE": 37,
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| 191 |
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"U-PROJECTS": 83,
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| 192 |
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"U-SKILLS": 25,
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| 193 |
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"U-T_EXP": 81
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| 194 |
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},
|
| 195 |
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"layer_norm_eps": 1e-05,
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| 196 |
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"max_position_embeddings": 514,
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| 197 |
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"model_type": "roberta",
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| 198 |
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"num_attention_heads": 12,
|
| 199 |
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"num_hidden_layers": 12,
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| 200 |
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"pad_token_id": 1,
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| 201 |
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"position_embedding_type": "absolute",
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| 202 |
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"torch_dtype": "float32",
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| 203 |
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"transformers_version": "4.35.2",
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| 204 |
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"type_vocab_size": 1,
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| 205 |
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"use_cache": true,
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| 206 |
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"vocab_size": 50265
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| 207 |
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}
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merges.txt
ADDED
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See raw diff
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special_tokens_map.json
ADDED
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{
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"bos_token": "<s>",
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"cls_token": "<s>",
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| 4 |
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"eos_token": "</s>",
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| 5 |
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"mask_token": {
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| 6 |
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"content": "<mask>",
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| 7 |
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"lstrip": true,
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| 8 |
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"normalized": false,
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| 9 |
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"rstrip": false,
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| 10 |
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"single_word": false
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| 11 |
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},
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| 12 |
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"pad_token": "<pad>",
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| 13 |
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"sep_token": "</s>",
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| 14 |
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"unk_token": "<unk>"
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| 15 |
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}
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tokenizer.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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| 3 |
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"added_tokens_decoder": {
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| 4 |
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"0": {
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| 5 |
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"content": "<s>",
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| 6 |
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"lstrip": false,
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| 7 |
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"normalized": false,
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| 8 |
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"rstrip": false,
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| 9 |
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"single_word": false,
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| 10 |
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"special": true
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| 11 |
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},
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| 12 |
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"1": {
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| 13 |
+
"content": "<pad>",
|
| 14 |
+
"lstrip": false,
|
| 15 |
+
"normalized": false,
|
| 16 |
+
"rstrip": false,
|
| 17 |
+
"single_word": false,
|
| 18 |
+
"special": true
|
| 19 |
+
},
|
| 20 |
+
"2": {
|
| 21 |
+
"content": "</s>",
|
| 22 |
+
"lstrip": false,
|
| 23 |
+
"normalized": false,
|
| 24 |
+
"rstrip": false,
|
| 25 |
+
"single_word": false,
|
| 26 |
+
"special": true
|
| 27 |
+
},
|
| 28 |
+
"3": {
|
| 29 |
+
"content": "<unk>",
|
| 30 |
+
"lstrip": false,
|
| 31 |
+
"normalized": false,
|
| 32 |
+
"rstrip": false,
|
| 33 |
+
"single_word": false,
|
| 34 |
+
"special": true
|
| 35 |
+
},
|
| 36 |
+
"50264": {
|
| 37 |
+
"content": "<mask>",
|
| 38 |
+
"lstrip": true,
|
| 39 |
+
"normalized": false,
|
| 40 |
+
"rstrip": false,
|
| 41 |
+
"single_word": false,
|
| 42 |
+
"special": true
|
| 43 |
+
}
|
| 44 |
+
},
|
| 45 |
+
"bos_token": "<s>",
|
| 46 |
+
"clean_up_tokenization_spaces": true,
|
| 47 |
+
"cls_token": "<s>",
|
| 48 |
+
"do_lower_case": false,
|
| 49 |
+
"eos_token": "</s>",
|
| 50 |
+
"errors": "replace",
|
| 51 |
+
"ignore_mismatched_sizes": true,
|
| 52 |
+
"mask_token": "<mask>",
|
| 53 |
+
"model_max_length": 512,
|
| 54 |
+
"pad_token": "<pad>",
|
| 55 |
+
"sep_token": "</s>",
|
| 56 |
+
"tokenizer_class": "RobertaTokenizer",
|
| 57 |
+
"trim_offsets": true,
|
| 58 |
+
"unk_token": "<unk>"
|
| 59 |
+
}
|
vocab.json
ADDED
|
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|
|