speech_recognizer = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h") AutoTokenizer 是用来做文本预处理。将文本变成单词(tokens)。 要注意的是:使用和模型一样的tokenization方法。 from transformers import Aut
在前面章节中已经知道如何从Hugging Face Hub上下载和缓存数据集(使用load_dataset直接指定Hub上已有的数据集名称)。但是我们经常会遇到需要加载本地和远程服务器上数据的情况,本节就是介绍如何使用Hugging Face的Datasets库来完成那些Hub没有的数据集加载方法。 处理本地和远程服务器上的数据集 Datasets库提...
# results = task.compute(model_or_pipeline="distilbert-base-uncased") # print("\nEvaluation Results (MRPC Task):") # print(results) print("Skipping model inference for brevity in this example.") print("Refer to Hugging Face documentation for full EvaluationSuite usage.") ...
Try running aBERT text classificationONNX model with ROCm: fromoptimum.onnxruntimeimportORTModelForSequenceClassificationfromoptimum.pipelinesimportpipelinefromtransformersimportAutoTokenizerimportonnxruntimeasortsession_options=ort.SessionOptions()session_options.log_severity_level=0ort_model=ORTModelForSequenceCla...
標記化 Hugging Face 資料集 設定訓練組態 訓練並記錄至 MLflow 顯示其他 4 個 本文說明如何在單一 GPU 上使用 Hugging Face transformers 程式庫微調 Hugging Face 模型。 它也包含 Databricks 特定的建議,以從 Lakehouse 載入資料,並將模型記錄至 MLflow,這可讓您在 Azure Databricks 上使...
pipeline( image="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/idefics-few-shot.jpg", question="What is in the image?", ) [{'answer':'statue of liberty'}] Why should I use Transformers?
Try in Colab The Hugging Face Transformers library makes state-of-the-art NLP models like BERT and training techniques like mixed precision and gradient checkpointing easy to use. The W&B integration adds rich, flexible experiment tracking and model vers
Transformers 库是 Hugging Face 最著名的贡献之一,它最初是 Transformer 模型的 pytorch 复现库,随着不断建设,至今已经成为 NLP 领域最重要,影响最大的基础设施之一。该库提供了大量预训练的模型,涵盖了多种语言和任务,成为当今大模型工程实现的主流标准,换句话说,如果你正在开发一个大模型,那么按 Transformer 库的...
Pipeline Function The key advantage of theHugging Face pipeline APIis that it allows developers to get started with powerful AI models quickly and easily, using only a few lines of code. This makes it accessible for prototyping and experimentation. However, developers can still customize and extend...
ML-Agents documentation: https://github.com/Unity-Technologies/ml-agents/blob/develop/docs/Hugging-Face-Integration.md Official Unity ML-Agents Spaces demos: https://huggingface.co/unity 示例: mlagents-load-from-hf --repo-id="Art-phys/poca-SoccerTwos_500M" --local-dir="./downloads" Paddl...