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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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Update README.md

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@@ -38,4 +38,5 @@ As a result, the number of sentences, tokens, and named entities (NEs) in the cl
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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%) |
 
 
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  | **CID** | 1,050 (78.89%) | 138 (10.37%) | 143 ( 10.74%) | 1,331 (100%) |
39
  | **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%) |
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+ |**Total**| **88,540 (80.00%)** | **11,067 (10.00%)** | **11,068 (10.00%)** | **110,675 (100%)** |