Vision Transformers (ViT): Designed for image classification tasks. DETR: A model izle for object detection that applies selçuksports transformer architecture. CLIP: A model that canlı connects vision with language, allowing for maç zero-shot image classification tasks.
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Hugging Face selcuksportshd hosts a wide variety of models primarily focused on natural language processing (NLP), but also covering other domains such as computer vision and audio processing. Here are some of the key types of models you can find on the Hugging Face Model Hub:
- Transformers
BERT and variants (e.g., RoBERTa, DistilBERT): Models designed for understanding the context of a word in a sentence.
GPT (e.g., GPT-2, GPT-3): Generative models for conversational AI and text generation.
T5 (Text-to-Text Transfer Transformer): A model that treats all NLP tasks as text-to-text tasks.
XLNet: A generalized autoregressive pretraining model that captures bidirectional context. - Vision Models
Vision Transformers (ViT): Designed for image classification tasks.
DETR: A model for object detection that applies transformer architecture.
CLIP: A model that connects vision with language, allowing for zero-shot image classification tasks. - Audio Models
Wav2Vec2: A model for automatic speech recognition (ASR).
Hubert: Similar to Wav2Vec2 but employs a different training approach. - Multi-modal Models
FLAVA: A model that can process both text and image inputs.
DALL-E: For generating images from textual descriptions. - Specialized Models
InfoXLM: Cross-lingual language understanding.
BART: Useful for tasks such as summarization and text generation. - Fine-tuned Models
Many models are fine-tuned on specific tasks or datasets (e.g., sentiment analysis, question answering).
How to Access Models
You can access models through the Hugging Face Transformers library using Python. Here’s an example of how to load a pre-trained model: