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yeshpanovrustem
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xlm-roberta-large-ner-kazakh

Token Classification
Transformers
PyTorch
Safetensors
Kazakh
xlm-roberta
Named Entity Recognition
NER
Inference Endpoints
Model card Files Community
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@@ -29,4 +29,13 @@ As a result, the number of sentences, tokens, and named entities (NEs) in the cl
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  | :---: | :---: | :---: | :---: | :---: |
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  | Sentence | 88,540 (80.00%) | 11,067 (10.00%) | 11,068 (10.00%) | 110,675 (100%) |
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  | Token | 1,088,461 (80.04%) | 136,021 (10.00%) | 135,426 (9.96%) | 1,359,908 (100%) |
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- | NE | 106,148 (80.17%) | 13,189 (9.96%) | 13,072 (9.87%) | 132,409 (100%) |
 
 
 
 
 
 
 
 
 
 
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  | :---: | :---: | :---: | :---: | :---: |
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  | Sentence | 88,540 (80.00%) | 11,067 (10.00%) | 11,068 (10.00%) | 110,675 (100%) |
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  | Token | 1,088,461 (80.04%) | 136,021 (10.00%) | 135,426 (9.96%) | 1,359,908 (100%) |
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+ | NE | 106,148 (80.17%) | 13,189 (9.96%) | 13,072 (9.87%) | 132,409 (100%) |
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+
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+ |Representation| Train | Valid | Test | Total |
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+ | :---: | :---: | :---: | :---: | :---: |
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+ | **AID** | 67,582 (79.99%) | 8,439 (9.99%) | 8,467 (10.02%)| 84,488 (100%) |
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+ | **BID** | 19,006 (80.11%) | 2,380 (10.03%) | 2,338 (9.85%)| 23,724 (100%) |
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+ | **CID** | 1,050 (78.89%) | 138 (10.37%) | 143 ( 10.74%) | 1,331 (100%) |
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+ | **DID** | 633 (79.22%) | 82 (10.26%) | 84 (10.51%) | 799 (100%) |
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+ | **EID** | 260 (81.00%) | 27 (8.41%) | 34 (10.59%)| 321 (100%) |
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+ | **FID** | 9 (75.00%) | 1 (8.33%)| 2 (16.67%)| 12 (100%) |