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Collaborate on models, datasets and Spaces. Faster examples with accelerated inference. Switch between documentation themes. Sign Up. to get started. 500. Not Found. ← Share your model Generation with LLMs →. We’re on a journey to advance and democratize artificial intelligence through open source and open science.
The ALBERT model was proposed in ALBERT: A Lite BERT for Self-supervised Learning of Language Representations by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut. It presents two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT:
Content from this model card has been written by the Hugging Face team to complete the information they provided and give specific examples of bias. Model description GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion.
The pipeline () automatically loads a default model and a preprocessing class capable of inference for your task. Let’s take the example of using the pipeline () for automatic speech recognition (ASR), or speech-to-text. Start by creating a pipeline () and specify the inference task: >>> from transformers import pipeline.
Overview. The Table Transformer model was proposed in PubTables-1M: Towards comprehensive table extraction from unstructured documents by Brandon Smock, Rohith Pesala, Robin Abraham. The authors introduce a new dataset, PubTables-1M, to benchmark progress in table extraction from unstructured documents, as well as table structure recognition ...
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Jun 28, 2024 · 100 projects using Transformers. Transformers is more than a toolkit to use pretrained models: it's a community of projects built around it and the Hugging Face Hub. We want Transformers to enable developers, researchers, students, professors, engineers, and anyone else to build their dream projects.
