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test/berturk_cased_ner/README.md

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----
-tags:
-- ner
-- token-classification
-- berturk
-- turkish
-language: tr
-datasets:
-- MilliyetNER
-widget:
-- text: "Türkiye'nin başkenti Ankara'dır ve ilk cumhurbaşkanı Mustafa Kemal Atatürk'tür."
----
-
-# DATASET
-MilliyetNER dataset was collected from the Turkish Milliyet newspaper articles between 1997-1998. This dataset is presented by [Tür et al. (2003)](https://www.cambridge.org/core/journals/natural-language-engineering/article/abs/statistical-information-extraction-system-for-turkish/7C288FAFC71D5F0763C1F8CE66464017). It was collected from news articles and manually annotated with three different entity types: Person, Location, Organization. The authors did not provide training/validation/test splits for this dataset. Dataset splits used by [Yeniterzi et al. 2011](https://aclanthology.org/P11-3019).        
-For more information: [tdd.ai - MilliyetNER](https://data.tdd.ai/#/effafb5f-ebfc-4e5c-9a63-4f709ec1a135)    
-**Model is only trained using training set. Test set not included during the last training**.      
-
-# USAGE
-```python
-from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
-model = AutoModelForTokenClassification.from_pretrained("alierenak/berturk-cased-ner")
-tokenizer = AutoTokenizer.from_pretrained("alierenak/berturk-cased-ner")
-ner_pipeline = pipeline('ner', model=model, tokenizer=tokenizer)
-ner_pipeline("Türkiye'nin başkenti Ankara, ilk cumhurbaşkanı Mustafa Kemal Atatürk'tür.")
-```
-
-# RESULT
-```bash
-[{'entity': 'B-LOCATION',
-  'score': 0.9966415,
-  'index': 1,
-  'word': 'Türkiye',
-  'start': 0,
-  'end': 7},
- {'entity': 'B-LOCATION',
-  'score': 0.99456763,
-  'index': 5,
-  'word': 'Ankara',
-  'start': 21,
-  'end': 27},
- {'entity': 'B-PERSON',
-  'score': 0.9958741,
-  'index': 9,
-  'word': 'Mustafa',
-  'start': 47,
-  'end': 54},
- {'entity': 'I-PERSON',
-  'score': 0.98833394,
-  'index': 10,
-  'word': 'Kemal',
-  'start': 55,
-  'end': 60},
- {'entity': 'I-PERSON',
-  'score': 0.9837286,
-  'index': 11,
-  'word': 'Atatürk',
-  'start': 61,
-  'end': 68}]
-```
-
-# BENCHMARKING
-```bash
-precision    recall  f1-score   support
-
-    LOCATION       0.97      0.96      0.97       960
-ORGANIZATION       0.95      0.92      0.94       863
-      PERSON       0.97      0.97      0.97      1410
-
-   micro avg       0.97      0.95      0.96      3233
-   macro avg       0.96      0.95      0.96      3233
-weighted avg       0.97      0.95      0.96      3233
-```

test/berturk_cased_ner/config.json

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-{
-  "_name_or_path": "dbmdz/bert-base-turkish-cased",
-  "architectures": [
-    "BertForTokenClassification"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "gradient_checkpointing": false,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
-  "id2label": {
-    "0": "B-LOCATION",
-    "1": "B-ORGANIZATION",
-    "2": "B-PERSON",
-    "3": "I-LOCATION",
-    "4": "I-ORGANIZATION",
-    "5": "I-PERSON",
-    "6": "O"
-  },
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "label2id": {
-    "B-LOCATION": 0,
-    "B-ORGANIZATION": 1,
-    "B-PERSON": 2,
-    "I-LOCATION": 3,
-    "I-ORGANIZATION": 4,
-    "I-PERSON": 5,
-    "O": 6
-  },
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "pad_token_id": 0,
-  "position_embedding_type": "absolute",
-  "torch_dtype": "float32",
-  "transformers_version": "4.9.2",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 32000
-}

test/berturk_cased_ner/pytorch_model.bin

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test/berturk_cased_ner/special_tokens_map.json

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test/berturk_cased_ner/tokenizer_config.json

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test/berturk_cased_ner/training_args.bin

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