Showing 30 open source projects for "text decoder"

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  • 1
    Text Hex Convert v2.1.1 -src
    This is the source code for Text Hex Decoder that I released earlier. Enjoy!
    Downloads: 0 This Week
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  • 2

    FileEncoderDecoder

    File Encoder And Decoder

    Encoder.jar utility enrypts any file (containing text like .txt, .rtf, .html, .htm, .msg, .xml, etc.) and generates an encoded file. This encoded file may then be transferred via email or other medium from the source machine and can be decoded back to original on the target machine. The target machine should have Decoder.jar in order to reproduce it back to the original form. Decoder.jar decrypts an encoded file and regenerates the original file. Use : java -jar Encoder.jar java -jar...
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  • 3
    Tool for encoding tags of mp3 files in the cyrillic charsets (cp1251, koi8-r) to unicode. Solution of problem with displaying tags in thе different charsets in a playlist. If you will find bug, mistake in the this text - mail me on hlamer@tut.by
    Downloads: 1 This Week
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  • 4
    t5-base

    t5-base

    Flexible text-to-text transformer model for multilingual NLP tasks

    t5-base is a pre-trained transformer model from Google’s T5 (Text-To-Text Transfer Transformer) family that reframes all NLP tasks into a unified text-to-text format. With 220 million parameters, it can handle a wide range of tasks, including translation, summarization, question answering, and classification. Unlike traditional models like BERT, which output class labels or spans, T5 always generates text outputs. It was trained on the C4 dataset, along with a variety of supervised NLP...
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  • 5
    bart-large-cnn

    bart-large-cnn

    Summarization model fine-tuned on CNN/DailyMail articles

    facebook/bart-large-cnn is a large-scale sequence-to-sequence transformer model developed by Meta AI and fine-tuned specifically for abstractive text summarization. It uses the BART architecture, which combines a bidirectional encoder (like BERT) with an autoregressive decoder (like GPT). Pre-trained on corrupted text reconstruction, the model was further trained on the CNN/DailyMail dataset—a collection of news articles paired with human-written summaries. It performs particularly well in generating concise, coherent, and human-readable summaries from longer texts. ...
    Downloads: 0 This Week
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