(dtype='int32', id=None)} >>> from datasets import ClassLabel, Value >>> new_features = dataset.features.copy() >>> new_features["label"] = ClassLabel(names=["negative", "positive"]) >>> new_features["idx"] = Value("int64") >>> dataset = dataset.cast(new_features) >>> ...
如果数据集中的样本位于 CPU 上,则设置 pin_memory=True 以加快训练期间将数据传输到 GPU 的速度。 train_dataset = processed_datasets["train"] eval_dataset = processed_datasets["validation"] train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=...
1 2 3 4 5 6 7 8 9 10 fromdatasetsimportload_dataset_builder ds_builder = load_dataset_builder("rotten_tomatoes") ds_builder.info.description movie review dataset. thisisa dataset of containing5,331positiveand5,331negative processed sentencesfromrotten tomatoes movie reviews. this data was ...
target_map = {'positive': 1, 'negative': 0, 'neutral': 2} df['target'] = df['airline_sentiment'].map(target_map) df2 = df[['text', 'target']] df2.columns = ['sentence', 'label'] df2.to_csv('data.csv', index=None) # dataset from datasets import load_dataset raw_dataset...
对奖励模型,我们将看到每个问题总是需要两个答案对比。有些问题有很多答案,可以产生很多对,我们只取十个以限制每个问题的数据量。最后,我们把格式从 HTML 转化到 Markdown 以提高输出的可读性。你可以看到数据集和处理过程的 [笔记本]。(https://huggingface.co/datasets/lvwerra/stack-exchange-paired。) ...
CSGHub is an opensource large model assets platform just like on-premise huggingface which helps to manage datasets, model files, codes and more. CSGHub是一个开源、可信的大模型资产管理平台,可帮助用户治理LLM和LLM应用生命周期中涉及到的资产(数据集、模型
与基于 LLM 的方法相比,SetFitABSA 有两个独特优势: 🗣无需提示:在使用基于 LLM 的少样本上下文学习时,提示的作用非常关键,因此一般需要精心设计,这一要求使得最终效果对用词十分敏感且非常依赖用户的专业知识,因此整个方案会比较脆弱。SetFitABSA 直接从少量带标签的文本示例中生成丰富的嵌入,因此可完全无需提示。
The principal implication for AI practitioners is that leveraging large-scale, meticulously curated datasets with detailed long captions, region-level annotations, and challenging negative samples is crucial for advancing the nuanced understanding and discriminative power of multimodal models, particularly for...
The datasets library offers a wide range of metrics. We are using accuracy here. On our data, we got an accuracy of 83% by training for only 3 epochs. Accuracy can be further increased by training for some more time or doing some more pre-processing of data like removing mentions from ...
pip install gradio tts huggingface_hub transformers datasets scipy torch torchaudio accelerate touch personalAssistant.py vim personalAssistant.py This will install the required packages for the demo, and then create a script for us to use to run the personal assistant. Next, it will create and ...