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  • 1
    Stanford CoreNLP

    Stanford CoreNLP

    Stanford CoreNLP, a Java suite of core NLP tools

    CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations. CoreNLP currently supports 6 languages, Arabic, Chinese, English, French, German, and Spanish. The centerpiece of CoreNLP is the pipeline. Pipelines take in raw text, run a series of NLP annotators on the text, and produce a final set of annotations. ...
    Downloads: 6 This Week
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  • 2
    Text Generation Inference

    Text Generation Inference

    Large Language Model Text Generation Inference

    Text Generation Inference is a high-performance inference server for text generation models, optimized for Hugging Face's Transformers. It is designed to serve large language models efficiently with optimizations for performance and scalability.
    Downloads: 8 This Week
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  • 3
    Stanza

    Stanza

    Stanford NLP Python library for many human languages

    Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Stanza is a Python natural language analysis package. It contains tools, which can be used in a pipeline, to convert a string containing human language text into lists of sentences and words, to generate base forms of those words, their parts of...
    Downloads: 5 This Week
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  • 4
    API-for-Open-LLM

    API-for-Open-LLM

    Openai style api for open large language models

    API-for-Open-LLM is a lightweight API server designed for deploying and serving open large language models (LLMs), offering a simple way to integrate LLMs into applications.
    Downloads: 0 This Week
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  • 5
    Chinese-XLNet

    Chinese-XLNet

    Chinese XLNet pre-trained model

    ...Unlike traditional masked language modeling, XLNet uses a permutation language modeling objective that captures bidirectional context more effectively by training over all possible token orderings, yielding richer contextual representations. This model is trained on large-scale Chinese text datasets to learn linguistic patterns, long-range dependencies, and semantic nuance typical of Chinese writing, making it useful for tasks like text classification, question answering, named entity recognition, and language generation. ...
    Downloads: 0 This Week
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  • 6
    Ecco

    Ecco

    Explain, analyze, and visualize NLP language models

    Ecco is an interpretability tool for transformers that helps visualize and analyze how language models generate text, making model behavior more transparent.
    Downloads: 7 This Week
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  • 7
    Seq2seq Chatbot for Keras

    Seq2seq Chatbot for Keras

    This repository contains a new generative model of chatbot

    This repository contains a new generative model of chatbot based on seq2seq modeling. The trained model available here used a small dataset composed of ~8K pairs of context (the last two utterances of the dialogue up to the current point) and respective response. The data were collected from dialogues of English courses online. This trained model can be fine-tuned using a closed-domain dataset to real-world applications. The canonical seq2seq model became popular in neural machine...
    Downloads: 0 This Week
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  • 8

    Medical Treebank

    Community-based linguistic annotation work on clinical documents.

    This project hosts linguistic annotations and guidelines for clinical text. We plan to include several types of annotation (Token, POS and Parse) in WordFreak format on clinical notes originally from the i2b2/VA NLP challenges. The guidelines are copyrighted, but free for the community to use. Annotation in WordFreak format contains only linguistic labels and character offsets, and can be distributed independently from the note text. Instruction is provided on setting up WordFreak for aligning/visualizing the annotations with the source text, which should be obtained through the official i2b2 data host https://www.i2b2.org/NLP/DataSets/Main.php.
    Downloads: 0 This Week
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