| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | base_model: bert-base-uncased |
| | datasets: |
| | - conll2003 |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: NER_Pittsburgh_TAA |
| | results: |
| | - task: |
| | type: token-classification |
| | name: Token Classification |
| | dataset: |
| | name: conll2003 |
| | type: conll2003 |
| | config: conll2003 |
| | split: validation |
| | args: conll2003 |
| | metrics: |
| | - type: precision |
| | value: 0.9429236395877203 |
| | name: Precision |
| | - type: recall |
| | value: 0.9517843159190066 |
| | name: Recall |
| | - type: f1 |
| | value: 0.9473332591025497 |
| | name: F1 |
| | - type: accuracy |
| | value: 0.9867030994328562 |
| | name: Accuracy |
| | language: |
| | - en |
| | - uk |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # NER_Pittsburgh_TAA |
| |
|
| | This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/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 |
