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@@ -35,6 +35,9 @@ model-index:
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  - type: accuracy
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  value: 0.9867030994328562
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  name: Accuracy
 
 
 
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -52,15 +55,37 @@ It achieves the following results on the evaluation set:
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  ## Model description
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- More information needed
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Intended uses & limitations
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- More information needed
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  ## Training and evaluation data
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- More information needed
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  ## Training procedure
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  - Transformers 4.41.2
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  - Pytorch 2.3.0+cu121
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  - Datasets 2.19.2
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- - Tokenizers 0.19.1
 
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  - type: accuracy
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  value: 0.9867030994328562
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  name: Accuracy
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+ language:
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+ - en
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+ - uk
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  ## Model description
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+ Ukr. Модель була створена як практичне завдання з машиного навчання, це за fine-tuning BERT модель для задачі Named Entity Recognition.
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+ Датасет який був використан це conll2003, стандат для навчання моделей під задачу Named Entity Recognition, або ще визначення складових мови в реченні.
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+ Дізнатися як працює модель маєте змогу або через інтерфейс, який надає huggingface, або ж через код
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+
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+ from transformers import AutoTokenizer, AutoModelForTokenClassification
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+
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+ tokenizer = AutoTokenizer.from_pretrained("CineAI/NER_Pittsburgh_TAA")
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+ model = AutoModelForTokenClassification.from_pretrained("CineAI/NER_Pittsburgh_TAA")
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+
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+ Якщо цікавить чому модель має таку назву, перше це для чого вона для NER, друга складова це назва крутої пісні Pittsburgh третя і остання складова
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+ це гурт який пісню створив це The Amity Affliction
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+ En. The model was created as a practical machine learning task, it is a fine-tuning BERT model for the Named Entity Recognition task.
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+ The dataset used is conll2003, a standard for training models for the Named Entity Recognition task, or for identifying the components of speech in a sentence.
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+ You can find out how the model works either through the interface provided by huggingface or through the code
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+
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+ from transformers import AutoTokenizer, AutoModelForTokenClassification
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+
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+ tokenizer = AutoTokenizer.from_pretrained("CineAI/NER_Pittsburgh_TAA")
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+ model = AutoModelForTokenClassification.from_pretrained("CineAI/NER_Pittsburgh_TAA")
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+
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+ If you are wondering why the model has such a name, the first is why it is for NER, the second component is the name of a cool song Pittsburgh, the third and last component
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+ is the band that created the song - The Amity Affliction
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  ## Intended uses & limitations
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+ Everyone can use this model, it is completely free and distributed under the Apache 2.0 licence.
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  ## Training and evaluation data
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+ Training and assessment data are the same - conll2003
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  ## Training procedure
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  - Transformers 4.41.2
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  - Pytorch 2.3.0+cu121
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  - Datasets 2.19.2
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+ - Tokenizers 0.19.1