🚀 Named Entity Recognition based on FERNET-CC_sk
This model is a fine - tuned version of fav - kky/FERNET-CC_sk on the Slovak wikiann dataset. It can effectively perform named entity recognition tasks, achieving high precision, recall, F1 - score, and accuracy on the evaluation set.
🚀 Quick Start
Prerequisites
Make sure you have installed the transformers
library.
Usage Example
from transformers import pipeline
ner_pipeline = pipeline(task='ner', model='crabz/slovakbert-ner')
input_sentence = "Minister financií a líder mandátovo najsilnejšieho hnutia OĽaNO Igor Matovič upozorňuje, že následky tretej vlny budú na Slovensku veľmi veľké."
classifications = ner_pipeline(input_sentence)
✨ Features
- High Performance: Achieves excellent results on the evaluation set, including high precision, recall, F1 - score, and accuracy.
- Supported Classes: Supports named entity recognition for LOCATION, PERSON, and ORGANIZATION.
📦 Installation
There is no specific installation process described in the original document. If you want to use the model, you need to install the transformers
library:
pip install transformers
💻 Usage Examples
Basic Usage
from transformers import pipeline
ner_pipeline = pipeline(task='ner', model='crabz/slovakbert-ner')
input_sentence = "Minister financií a líder mandátovo najsilnejšieho hnutia OĽaNO Igor Matovič upozorňuje, že následky tretej vlny budú na Slovensku veľmi veľké."
classifications = ner_pipeline(input_sentence)
📚 Documentation
Intended Uses & Limitation
The model supports named entity recognition for the following classes: LOCATION, PERSON, ORGANIZATION.
Training Procedure
Training Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e - 05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
- lr_scheduler_type: linear
- num_epochs: 10.0
Training Results
Training Loss |
Epoch |
Step |
Validation Loss |
Precision |
Recall |
F1 |
Accuracy |
0.1259 |
1.0 |
834 |
0.1095 |
0.8963 |
0.9182 |
0.9071 |
0.9697 |
0.071 |
2.0 |
1668 |
0.0974 |
0.9270 |
0.9357 |
0.9313 |
0.9762 |
0.0323 |
3.0 |
2502 |
0.1259 |
0.9257 |
0.9330 |
0.9293 |
0.9745 |
0.0175 |
4.0 |
3336 |
0.1347 |
0.9241 |
0.9360 |
0.9300 |
0.9756 |
0.0156 |
5.0 |
4170 |
0.1407 |
0.9337 |
0.9404 |
0.9370 |
0.9780 |
0.0062 |
6.0 |
5004 |
0.1522 |
0.9267 |
0.9410 |
0.9338 |
0.9774 |
0.0055 |
7.0 |
5838 |
0.1559 |
0.9322 |
0.9429 |
0.9375 |
0.9780 |
0.0024 |
8.0 |
6672 |
0.1733 |
0.9321 |
0.9438 |
0.9379 |
0.9779 |
0.0009 |
9.0 |
7506 |
0.1765 |
0.9347 |
0.9468 |
0.9407 |
0.9784 |
0.0002 |
10.0 |
8340 |
0.1763 |
0.9360 |
0.9472 |
0.9416 |
0.9789 |
Framework Versions
- Transformers 4.14.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
🔧 Technical Details
Evaluation Results
The model achieves the following results on the evaluation set:
- Loss: 0.1763
- Precision: 0.9360
- Recall: 0.9472
- F1: 0.9416
- Accuracy: 0.9789
📄 License
This model is licensed under the cc - by - nc - sa - 4.0 license.
📊 Model Information
Property |
Details |
Model Type |
Fine - tuned model based on FERNET - CC_sk |
Training Data |
Slovak wikiann dataset |
Metrics |
Precision, Recall, F1, Accuracy |