Metadata-Version: 2.1
Name: headline-detector
Version: 1.0.0
Summary: An Indonesian Headline Detection Python API.
Home-page: UNKNOWN
Author: Kaenova Mahendra Auditama
Author-email: kaenova@gmail.com
License: UNKNOWN
Description: # [headline_detector](https://github.com/kaenova/headline_detector)
        
        _Indonesian Headline Detection Python API_
        
        This is a Python library that provides APIs for detecting headlines in textual data, especially on social media platforms such as Twitter. The library utilizes a model that has been developed and trained on a dataset of Twitter posts containing both headline and non-headline texts, with the assistance of journalism professionals to ensure the data quality.
        
        ```sh
        $ pip install headline-detector
        ```
        
        ## Available scenario and the performance
        
        | Model        | Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | Scenario 5 | Scenario 6 |
        | ------------ | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- |
        | Fasttext     | 0.8766     | 0.8714     | 0.8793     | 0.8714     | 0.8714     | 0.8661     |
        | CNN          | 0.9081     | 0.9081     | 0.8950     | 0.8898     | 0.8950     | 0.8898     |
        | IndoBERTweet | 0.9895     | 0.9921     | 0.9738     | 0.9580     | 0.9843     | 0.9685     |
        
        All meassured in accuracy
        
        ### Model Throughput
        
        | Model        | Throughput (Â± Text/seconds) |
        | ------------ | --------------------------- |
        | IndoBERTweet | Â±1.3                        |
        | CNN          | Â±281.60                     |
        | Fasttext     | Â±2048.41                    |
        
        Tested on Intel i7-6700k and 32GB of RAM.
        
        ## Usage
        
        Output either 0 (non-headline) and 1 (headline)
        
        ```python
        from headline_detector import FasttextDetector, IndoBERTweetDetector, CNNDetector
        
        detector = FasttextDetector.load_from_scenario(1)
        data = detector.predict_text(
            [
                "nama kamu siapa?",
                "Kapolda Jatim Teddy Minahasa Dikabarkan Ditangkap Terkait Narkoba  https://t.co/LD9X6VFaUR",
            ]
        )
        print(data)  # output: [0, 1]
        
        detector = CNNDetector.load_from_scenario(3)
        data = detector.predict_text(
            [
                "nama kamu siapa?",
                "Kapolda Jatim Teddy Minahasa Dikabarkan Ditangkap Terkait Narkoba  https://t.co/LD9X6VFaUR",
            ]
        )
        print(data)  # output: [0, 1]
        
        detector = IndoBERTweetDetector.load_from_scenario(5)
        data = detector.predict_text(
            [
                "nama kamu siapa?",
                "Kapolda Jatim Teddy Minahasa Dikabarkan Ditangkap Terkait Narkoba  https://t.co/LD9X6VFaUR",
            ]
        )
        print(data)  # output: [0, 1]
        
        # 0 is non-headline
        # 1 is headline
        ```
        
        ## Paper
        
        Coming soon.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.8
Description-Content-Type: text/markdown
