FERNET CC Sk Ner

🚀 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

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