27 रिपॉजिटरी
Awesome GitHub RepositoriesText Classifiers
Models and tools for assigning predefined categories or sentiments to text documents.
Distinguishing note: The candidates provided focus on speech-to-text and text-to-speech, which are unrelated to document categorization.
Explore 27 awesome GitHub repositories matching artificial intelligence & ml · Text Classifiers. Refine with filters or upvote what's useful.
Awesome Text Classifiers GitHub Repositories
AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
- explosion/spacy
explosion/spaCy
33,688GitHub पर देखेंspaCy is a Python natural language processing framework designed for industrial-scale text processing. It converts raw text into structured data for machine learning pipelines through a combination of statistical language model trainers, transformer-based text processors, and syntactic dependency parsers. The project enables the integration of pretrained transformer architectures to perform complex linguistic analysis and multi-task learning. It also provides a specialized system for neural named entity recognition to identify and categorize key entities within text. The framework covers a b
Implements a framework for assigning predefined categories to text blocks to determine topic or sentiment.
Pythonaiartificial-intelligencecython - oxford-cs-deepnlp-2017/lectures
oxford-cs-deepnlp-2017/lectures
15,854GitHub पर देखेंThis repository is a deep learning for natural language processing course and curriculum. It provides educational material and guides focused on neural network architectures used for processing natural language, speech signals, and text classification. The content includes instructional tutorials on sequence modeling and neural language modeling, covering the implementation of n-gram and recurrent neural networks. It also provides a framework for studying word embeddings to map linguistic meanings into numerical representations. The curriculum covers a broad range of capabilities, including
Covers text classification methods ranging from probabilistic Naive Bayes to convolutional neural networks.
deep-learningmachine-learningnatural-language-processing - flairnlp/flair
flairNLP/flair
14,378GitHub पर देखेंFlair is a transformer-based natural language processing framework used to build and train models for text classification and sequence tagging. It provides a specialized library for generating contextual text embeddings and performing linguistic analysis. The framework includes dedicated tools for named entity recognition, including the identification of specialized biomedical entities across multiple languages. It further supports entity linking to map identified text mentions to unique entries within general or biomedical knowledge bases. The project covers a broad range of language analys
Provides tools for teaching models to assign a single category label or sentiment to a piece of text.
Python - zalandoresearch/flair
zalandoresearch/flair
14,378GitHub पर देखेंFlair is a natural language processing framework for training and applying models for sequence labeling and text classification. It provides a system for generating word embeddings and identifying semantic entities within text. The framework includes a dedicated system for zero and few-shot learning, enabling text classification and entity extraction using minimal training examples by leveraging pre-trained knowledge. Its capabilities cover named entity recognition, sentiment analysis, and the training of specialized models using custom datasets. It also includes tooling for the visual highl
Builds and trains models to categorize text into predefined labels or sentiments.
Python - marcotcr/lime
marcotcr/lime
12,142GitHub पर देखेंThis project is an agnostic model interpretability framework and explainability tool designed to provide local interpretable explanations for individual predictions. It functions as a local surrogate model that approximates the behavior of any machine learning classifier or regression model to identify the most influential features for a specific instance. The framework is designed to be model-agnostic, meaning it can explain predictions across tabular, text, and image data regardless of the underlying architecture. It employs local linear approximations and feature importance visualization t
Identifies the influence of individual words on a classifier's prediction for single text instances.
JavaScript - nielsrogge/transformers-tutorials
NielsRogge/Transformers-Tutorials
11,641GitHub पर देखेंThis is a collection of tutorials and practical demonstrations for implementing machine learning tasks using the HuggingFace Transformers library. It serves as a guide for applying transformer architectures across computer vision, natural language processing, and audio analysis. The repository provides implementation examples for multimodal model deployment, including the combination of text, image, and audio inputs. It includes resources for optimizing pre-trained models through fine-tuning on custom datasets and provides examples for preparing PyTorch datasets by converting raw files into t
Demonstrates the categorization of text sequences and audio signals into predefined labels.
Jupyter Notebookbertgpt-2layoutlm - apple/turicreate
apple/turicreate
11,171GitHub पर देखेंThis project is an automated machine learning framework and toolkit designed for training and tuning custom models for classification, regression, and recommendations. It functions as a multimodal machine learning toolkit capable of processing and training models using a combination of text, image, audio, and sensor data. The framework distinguishes itself as a multimodal data processor that can handle and visualize large datasets on a single machine using column-oriented disk storage. It includes a core machine learning model generator that converts trained models into formats compatible wit
Provides models and tools for assigning predefined categories to text documents.
C++ - yandexdataschool/nlp_course
yandexdataschool/nlp_course
10,591GitHub पर देखेंYSDA course in Natural Language Processing
Applies convolutional and recurrent neural networks to categorize text.
Jupyter Notebook - jack-cherish/machine-learning
Jack-Cherish/Machine-Learning
10,333GitHub पर देखेंThis project is a collection of supervised and unsupervised machine learning algorithms implemented from scratch using Python. It serves as an educational resource for studying model training, parameter optimization, and the implementation of core predictive models. The library provides a variety of supervised learning tools, including linear and logistic regression, decision trees, and support vector machines. It also features unsupervised learning capabilities for discovering patterns in unlabeled datasets through clustering algorithms. Broad capability areas include ensemble learning thro
Implements text classifiers to predict document categories by calculating class probabilities from trained models.
Pythonadaboostadaboost-algorithmdecision-tree - sloria/textblob
sloria/TextBlob
9,516GitHub पर देखेंTextBlob is a natural language processing library that provides a unified interface for common linguistic tasks. It operates as a wrapper-based API, simplifying the use of complex processing libraries by delegating core operations to specialized external frameworks. The project features a pluggable processing pipeline that allows for the integration of custom logic and alternative language engines. It supports the extension of processing models through plugins to add specific language support or custom data processing. The library covers a broad range of linguistic capabilities, including se
Uses machine learning models to assign predefined categories and organize text documents.
Pythonnatural-language-processingnlpnltk - kreuzberg-dev/kreuzberg
kreuzberg-dev/kreuzberg
8,527GitHub पर देखेंKreuzberg is a document extraction engine that converts PDFs, Office files, images, and over 90 other formats into clean, structured text and metadata. It is built around a compiled Rust core that can be used as a native library, a command-line tool, a REST API server, or a WebAssembly module for browser-based processing. The system is designed to run entirely on self-hosted infrastructure, with no data leaving the user's environment. What distinguishes Kreuzberg is its breadth of integration surfaces and its pipeline architecture. It exposes extraction capabilities through native bindings fo
Assigns labels to a single piece of plain text using a configured LLM, without requiring an extraction result.
Rustdocument-intelligenceelixirffi - hatchet-dev/hatchet
hatchet-dev/hatchet
6,622GitHub पर देखेंHatchet is an open-source durable workflow engine and task orchestration platform. It provides a framework for building and executing fault-tolerant, multi-step pipelines as directed acyclic graphs (DAGs), with automatic retries, scheduling, and real-time observability. The system is built around durable task checkpointing, which persists execution state after each step so work can resume from the last checkpoint after a worker crash or restart, and it supports event-driven task resumption that pauses a task until a matching external event arrives. The platform distinguishes itself through it
Categorizes a document by matching keywords against its extracted text to determine its type or topic.
Goconcurrencydagdistributed - datajuicer/data-juicer
datajuicer/data-juicer
6,574GitHub पर देखेंData-Juicer is an open-source framework for cleaning, filtering, deduplicating, and transforming multimodal datasets to prepare them for training large language and vision models. It functions as a distributed data pipeline engine that runs processing jobs across Ray clusters, handling billions of samples with automatic operator fusion and adaptive parallelism. The framework provides a library of operators that leverage large language models for semantic extraction, filtering, and data synthesis within processing pipelines. The project distinguishes itself through a YAML-based data recipe sys
Runs a quality classifier on web-crawled text to filter low-quality samples.
Pythondatadata-analysisdata-pipeline - microsoft/nlp-recipes
microsoft/nlp-recipes
6,436GitHub पर देखेंnlp-recipes is a collection of implementation guides and reference templates for applying natural language processing techniques to real-world tasks. It provides standardized workflows and code examples for developing NLP pipelines, from dataset preparation and model training to performance evaluation. The project focuses on the practical application of transformer-based models, offering patterns for fine-tuning pretrained architectures for tasks such as text classification, named entity recognition, and question answering. It also includes a toolkit for model interpretability, allowing users
Categorizes text into predefined labels or topics using pretrained transformer models.
Python - facebookresearch/pytext
facebookresearch/pytext
6,298GitHub पर देखेंPyText is an extensible PyTorch-based framework for building, training, and deploying custom natural language processing models, including text classifiers, sequence taggers, and intent-slot predictors. It provides a modular toolkit that allows developers to assemble these models using pluggable registries for model architectures, data formats, and tensorizers, all configurable through YAML files without requiring code changes. The framework distinguishes itself through its comprehensive support for the full NLP model lifecycle, from training to production inference. It includes pre-built neu
Trains deep-learning text classifiers from labeled utterances using configurable architectures.
Python - lmoroney/dlaicourse
lmoroney/dlaicourse
5,638GitHub पर देखेंयह प्रोजेक्ट TensorFlow डीप लर्निंग कोर्स के लिए इंटरैक्टिव नोटबुक का एक संग्रह है। यह न्यूरल नेटवर्क आर्किटेक्चर, सुपरवाइज्ड लर्निंग और ट्रांसफर लर्निंग को लागू करने के लिए निर्देशित शिक्षण संसाधन और व्यावहारिक ट्यूटोरियल प्रदान करता है। सामग्री में एक कंप्यूटर विज़न लर्निंग पाथ और ट्रांसफर लर्निंग के लिए विशिष्ट गाइड शामिल हैं, जो यह प्रदर्शित करते हैं कि प्री-ट्रेंड मॉडल को नए कार्यों के अनुकूल कैसे बनाया जाए। इसमें Keras हाई-लेवल API का उपयोग करके रिग्रेशन मॉडल और इमेज क्लासिफायर बनाने के लिए ट्यूटोरियल शामिल हैं। दायरा बाइनरी और मल्टीक्लास क्लासिफिकेशन, रिग्रेशन मॉडलिंग, और हस्तलिखित टेक्स्ट पहचान के लिए कन्वेन्शनल न्यूरल नेटवर्क के निर्माण के लिए सुपरवाइज्ड लर्निंग पाइपलाइनों को कवर करता है।
Implements handwritten text recognition to categorize characters from image pixels.
Jupyter Notebook - justadudewhohacks/opencv4nodejs
justadudewhohacks/opencv4nodejs
5,045GitHub पर देखेंopencv4nodejs, JavaScript रैपर्स और एक C++ नेटिव एडऑन का एक सेट है जो OpenCV लाइब्रेरी के लिए Node.js बाइंडिंग प्रदान करता है। यह एक कंप्यूटर विजन लाइब्रेरी और इमेज प्रोसेसिंग फ्रेमवर्क के रूप में कार्य करता है, जो उच्च-प्रदर्शन C++ एल्गोरिदम को JavaScript एनवायरनमेंट में उजागर करता है। यह प्रोजेक्ट चेहरों का पता लगाने, वस्तुओं को ट्रैक करने और डीप न्यूरल नेटवर्क का उपयोग करके विजुअल डेटा का विश्लेषण करने के लिए विजन एल्गोरिदम के निष्पादन को सक्षम बनाता है। इसमें डेटा पैटर्न क्लासिफिकेशन, टेक्स्ट पैटर्न रिकग्निशन और चेहरे के लैंडमार्क व जेस्चर की पहचान करने की क्षमताएं शामिल हैं। फ्रेमवर्क स्वचालित इमेज प्रोसेसिंग, ऑप्टिकल कैरेक्टर रिकग्निशन और रीयल-टाइम वीडियो विश्लेषण सहित क्षमताओं की एक विस्तृत सतह को कवर करता है।
Identifies handwritten text and visual patterns using gradient histograms and support vector machine classifiers.
C++asynccvface-detection - nyandwi/machine_learning_complete
Nyandwi/machine_learning_complete
4,983GitHub पर देखेंThis is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi
Provides models and tools for assigning predefined categories or sentiments to text documents.
Jupyter Notebookcomputer-visiondata-analysisdata-science - fastai/course-v3
fastai/course-v3
4,914GitHub पर देखेंयह रिपॉजिटरी एक व्यापक शैक्षिक कार्यक्रम और डीप लर्निंग फ्रेमवर्क है, जिसे नोटबुक और कोड उदाहरणों के माध्यम से PyTorch का उपयोग करके व्यावहारिक डीप लर्निंग सिखाने के लिए डिज़ाइन किया गया है। यह न्यूरल नेटवर्क बनाने, प्रशिक्षित करने और डिप्लॉय करने के लिए एक हाई-लेवल लाइब्रेरी के रूप में कार्य करता है। यह प्रोजेक्ट कंप्यूटर विज़न, नेचुरल लैंग्वेज प्रोसेसिंग और टैबुलर डेटा प्रीप्रोसेसिंग के लिए विशेष टूलकिट प्रदान करता है। यह डिस्क्रिमिनेटिव लर्निंग रेट्स, ट्रेनिंग लॉजिक को कस्टमाइज़ करने के लिए टू-वे कॉलबैक सिस्टम और हाई-लेवल लर्नर एब्स्ट्रैक्शन जैसे उन्नत ट्रेनिंग कंट्रोल्स के माध्यम से खुद को अलग करता है। यह प्रोजेक्ट Jupyter Notebooks की एक श्रृंखला के रूप में उपलब्ध है।
Provides high-level tools to construct text classification learners using pretrained model architectures.
Jupyter Notebookdata-sciencedeep-learningfastai - macanv/bert-bilsmt-crf-ner
macanv/BERT-BiLSMT-CRF-NER
4,906GitHub पर देखेंयह प्रोजेक्ट एक नेचुरल लैंग्वेज प्रोसेसिंग सिस्टम है जिसे नेम्ड एंटिटी रिकग्निशन और टेक्स्ट क्लासिफिकेशन के लिए डिज़ाइन किया गया है। यह कच्चे टेक्स्ट से विशिष्ट नामों और प्रमुख जानकारी की पहचान करने के लिए मशीन लर्निंग दृष्टिकोण का उपयोग करता है। यह सिस्टम एक मल्टी-लेयर आर्किटेक्चर लागू करता है जो एम्बेडिंग के लिए प्री-ट्रेन्ड ट्रांसफॉर्मर, सीक्वेंस मॉडलिंग के लिए बाईडायरेक्शनल लॉन्ग शॉर्ट-टर्म मेमोरी और लेबल ट्रांज़िशन के लिए कंडीशनल रैंडम फील्ड को जोड़ता है। यह प्रोजेक्ट विशिष्ट डेटासेट पर इन मॉडल्स को फाइन-ट्यून करके ट्रांसफर लर्निंग का समर्थन करता है। इसमें कस्टम डेटासेट पर मॉडल्स को प्रशिक्षित करने और प्रशिक्षित मॉडल को नेटवर्क सर्विस के रूप में डिप्लॉय करने की क्षमताएं शामिल हैं।
Implements text classification to organize unstructured content into predefined categories.
Python
