nielsr/idefics2-embeddings

Idefics2 Embeddings数据集包含用于Idefics2模型的预计算输入和输出嵌入，适用于NLP任务，如文本分类和命名实体识别。这些嵌入保存在.pt文件中，可以轻松加载到PyTorch模型中。数据集由Mariam策划，使用英语，采用MIT许可证。数据集的结构包括两个主要文件：input_embeddings.pt和output_embeddings.pt。数据集的创建是为了提供预计算的嵌入，以加速和优化NLP模型的训练和推理。数据集可能继承训练数据中的偏见，用户应注意并考虑采取额外措施来减轻潜在的负面影响。

The Idefics2 Embeddings dataset contains precomputed input and output embeddings used for NLP tasks with the Idefics2 model. These embeddings are saved in `.pt` files, which can be easily loaded into PyTorch models. The dataset is curated by Mariam, uses English, and follows the MIT license. The creation of the dataset aims to provide precomputed embeddings to accelerate and enhance the efficiency of NLP model training and inference. The embeddings were generated by training the Idefics2 model on a large corpus of text data and do not contain any personal or sensitive information.

原始信息汇总

Dataset Card for Idefics2 Embeddings

Dataset Details

Dataset Description

• Curated by: Mariam
• Language(s): English
• License: MIT

Dataset Sources

• Repository: [https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Idefics2]

Uses

Direct Use

• Suitable for initializing the Idefics2 model with precomputed embeddings for NLP tasks such as text classification and named entity recognition.

Out-of-Scope Use

• Not suitable for tasks such as image processing or non-NLP related tasks.

Dataset Structure

• Files:
  • input_embeddings.pt: Contains input embeddings.
  • output_embeddings.pt: Contains output embeddings.

Dataset Creation

Curation Rationale

• Created to provide precomputed embeddings for the Idefics2 model, facilitating faster and more efficient NLP model training and inference.

Source Data

• Data Collection and Processing:
  • Embeddings generated using the Idefics2 model trained on a large corpus of text data.
  • Process involved preprocessing text data, training the Idefics2 model, and extracting embeddings from the trained model.

Annotations

• Annotation process: No additional annotations beyond initial data collection and embedding generation.
• Annotators: Embeddings generated programmatically, without manual annotation.

Personal and Sensitive Information

• Dataset does not contain any personal, sensitive, or private information.

Bias, Risks, and Limitations

• Dataset may inherit biases from the training data used to generate the embeddings.
• Users should be cautious of potential biases in the model outputs and consider additional steps to mitigate unintended consequences.

Citation

BibTeX:

bibtex @dataset{your_name_2024_idefics2_embeddings, author = {Mariam}, title = {Idefics2 Embeddings}, year = {2024}, publisher = {Hugging Face}, version = {2.0}, doi = {10.5281/zenodo.1234567}, url = {https://huggingface.co/nielsr/idefics2-embeddings} }