NER_Pittsburgh_TAA

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NER_Pittsburgh_TAA

This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0860
• Precision: 0.9429
• Recall: 0.9518
• F1: 0.9473
• Accuracy: 0.9867

Model description

Ukr

Модель була створена як практичне завдання з машиного навчання, це за fine-tuning BERT модель для задачі Named Entity Recognition. Датасет який був використан це conll2003, стандат для навчання моделей під задачу Named Entity Recognition, або ще визначення складових мови в реченні. Дізнатися як працює модель маєте змогу або через інтерфейс, який надає huggingface, або ж через код

from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("CineAI/NER_Pittsburgh_TAA")

model = AutoModelForTokenClassification.from_pretrained("CineAI/NER_Pittsburgh_TAA")

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 це гурт який пісню створив це The Amity Affliction

En

The model was created as a practical machine learning task, it is a fine-tuning BERT model for the Named Entity Recognition task. 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. You can find out how the model works either through the interface provided by huggingface or through the code

from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("CineAI/NER_Pittsburgh_TAA")

model = AutoModelForTokenClassification.from_pretrained("CineAI/NER_Pittsburgh_TAA")

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 is the band that created the song - The Amity Affliction

Intended uses & limitations

Everyone can use this model, it is completely free and distributed under the Apache 2.0 licence.

Training and evaluation data

Training and assessment data are the same - conll2003

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.0+cu121
• Datasets 2.19.2
• Tokenizers 0.19.1