transformers

Pre-trained transformer models for text generation and classification are accessed by loading model pipelines for inference. This skill provides direct access to thousands of models to perform NLP tasks without manually fine-tuning them.

Yes, image classification and object detection can be performed without manually fine-tuning models. This skill provides access to pre-trained computer vision transformer models that support direct inference on input images.

Running speech recognition and audio classification using transformer models requires loading specific audio processing pipelines. This skill enables speech-to-text and audio classification directly using pre-trained transformer architectures and librosa.

Yes, torch and accelerate are required dependencies to fine-tune transformer models on custom datasets. This skill relies on these frameworks, alongside datasets and evaluate, to support full functionality for custom fine-tuning.

Yes, this approach supports multimodal tasks like visual question answering and image-to-text. The skill provides access to pre-trained multimodal transformer models capable of processing combined visual and textual inputs.