transformers

Pretrained models for natural language processing tasks like summarization and translation are accessed via pipeline abstractions. This Skill leverages Hugging Face's Transformers library to provide automatic class inference and tokenization for these applications.

Yes, you can fine-tune large language models on scientific literature. This Skill enables efficient fine-tuning of foundation models, allowing you to build domain-specific chatbots for research assistance using text datasets.

Yes, this tool supports computer vision and multimodal tasks. You can utilize Vision Transformers (ViT) for image classification and engage in document AI or visual question answering using state-of-the-art pretrained models.

The best way to model protein folding and DNA sequences is through scientific sequence analysis. This Skill provides access to state-of-the-art pretrained models specifically designed for modeling protein folding and DNA/RNA sequences.

Yes, PyTorch is required as a backend dependency. Along with the transformers, datasets, and tokenizers libraries, PyTorch provides the necessary computational framework to run Hugging Face pipelines and load pretrained models.