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

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	439	0.0863	0.9437	0.9444	0.9440	0.9861
0.0024	2.0	878	0.0995	0.9394	0.9442	0.9418	0.9852
0.0021	3.0	1317	0.0904	0.9355	0.9463	0.9409	0.9856
0.0012	4.0	1756	0.0835	0.9427	0.9514	0.9471	0.9867
0.0009	5.0	2195	0.0860	0.9429	0.9518	0.9473	0.9867

Framework versions

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