#python #ai #deep_learning #natural_language_processing #natural_language_understanding #nlp #nlp_library #nlp_machine_learning #pre_trained_language_models #pre_trained_model #pytorch #transformer
https://github.com/thunlp/OpenPrompt
https://github.com/thunlp/OpenPrompt
GitHub
GitHub - thunlp/OpenPrompt: An Open-Source Framework for Prompt-Learning.
An Open-Source Framework for Prompt-Learning. Contribute to thunlp/OpenPrompt development by creating an account on GitHub.
#python #bert #deep_learning #flax #hacktoberfest #jax #language_model #language_models #machine_learning #model_hub #natural_language_processing #nlp #nlp_library #pretrained_models #python #pytorch #pytorch_transformers #seq2seq #speech_recognition #tensorflow #transformer
The Hugging Face Transformers library provides thousands of pretrained models for various tasks like text, image, and audio processing. These models can be used for tasks such as text classification, image detection, speech recognition, and more. The library supports popular deep learning frameworks like JAX, PyTorch, and TensorFlow, making it easy to switch between them.
The benefit to the user is that you can quickly download and use these pretrained models with just a few lines of code, saving time and computational resources. You can also fine-tune these models on your own datasets and share them with the community. Additionally, the library offers a simple `pipeline` API for immediate use on different inputs, making it user-friendly for both researchers and practitioners. This helps in reducing compute costs and carbon footprint while enabling high-performance results across various machine learning tasks.
https://github.com/huggingface/transformers
The Hugging Face Transformers library provides thousands of pretrained models for various tasks like text, image, and audio processing. These models can be used for tasks such as text classification, image detection, speech recognition, and more. The library supports popular deep learning frameworks like JAX, PyTorch, and TensorFlow, making it easy to switch between them.
The benefit to the user is that you can quickly download and use these pretrained models with just a few lines of code, saving time and computational resources. You can also fine-tune these models on your own datasets and share them with the community. Additionally, the library offers a simple `pipeline` API for immediate use on different inputs, making it user-friendly for both researchers and practitioners. This helps in reducing compute costs and carbon footprint while enabling high-performance results across various machine learning tasks.
https://github.com/huggingface/transformers
GitHub
GitHub - huggingface/transformers: 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models…
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. - GitHub - huggingface/t...
#python #ai #artificial_intelligence #cython #data_science #deep_learning #entity_linking #machine_learning #named_entity_recognition #natural_language_processing #neural_network #neural_networks #nlp #nlp_library #python #spacy #text_classification #tokenization
spaCy is a powerful tool for understanding and processing human language. It helps computers analyze text by breaking it into parts like words, sentences, and entities (like names or places). This makes it useful for tasks such as identifying who is doing what in a sentence or finding specific information from large texts. Using spaCy can save time and improve accuracy compared to manual analysis. It supports many languages and integrates well with advanced models like BERT, making it ideal for real-world applications.
https://github.com/explosion/spaCy
spaCy is a powerful tool for understanding and processing human language. It helps computers analyze text by breaking it into parts like words, sentences, and entities (like names or places). This makes it useful for tasks such as identifying who is doing what in a sentence or finding specific information from large texts. Using spaCy can save time and improve accuracy compared to manual analysis. It supports many languages and integrates well with advanced models like BERT, making it ideal for real-world applications.
https://github.com/explosion/spaCy
GitHub
GitHub - explosion/spaCy: 💫 Industrial-strength Natural Language Processing (NLP) in Python
💫 Industrial-strength Natural Language Processing (NLP) in Python - explosion/spaCy