Lysandre DEBUT, Julien CHAUMOND, Thomas WOLF\nHugging Face\n{victor,lysandre,julien,thomas}@huggingface.co\nAbstract\nAs Transfer Learning from large-scale pre-trained models becomes more prevalent\nin Natural Language Processing (NLP), operating these large models in on-theedge...
It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve...
matryoshka_nli.py: 此示例使用MultipleNegativesRankingLoss与MatryoshkaLoss结合,利用自然语言推理 (NLI) 数据训练一个强大的嵌入模型。这是对NLI文档的改编。 matryoshka_nli_reduced_dim.py: 此示例使用MultipleNegativesRankingLoss与MatryoshkaLoss结合,训练一个最大输出维度为 256 的小型嵌入模型。它使用自然语言推理 (...
01-ai/Yi-VL-34B · Hugging Face Yi-VL-34B模型托管在Hugging Face上,是全球首个开源的340亿视觉语言模型,代表了人工智能领域的重大进展。它以其双语多模态能力脱颖而出,可以进行英文和中文的多轮文本-图像对话。该模型在图像理解方面表现出色,并在MMMU和CMMMU等基准测试中... 内容导读...
01-ai/Yi-VL-6B · Hugging Face 01-ai/Yi-34B-200K · Hugging Face ### Building the Next Generation of Open-Source and Bilingual LLMs 🤗 Hugging Face • 🤖 ModelScope • ✡️ WiseModel 👩🚀 Ask questions or discuss ideas on GitHub 👋 Join us on 👾 Discord or 💬...
In addition to evaluation metrics, to enable qualitative analyses of the results, we also share a sample of generations produced by the model, available here. A glance at the results so far We are currently in the process of evaluating a very large number of models from the ...
This data is used in the RLHF process to train a reward model that predicts a preferred text, but the idea of rating and ranking model outputs has grown to be a more general tool in evaluation. Here is an example from each of the instruct and code-instruct splits of our...
Because no a single model outperforms across all datasets, we have to select the right model for a specific dataset. To demonstrate this, we evaluate a range of popular transformer models from Hugging Face. For each dataset, we rank each model by its test performa...
Because no a single model outperforms across all datasets, we have to select the right model for a specific dataset. To demonstrate this, we evaluate a range of popular transformer models from Hugging Face. For each dataset, we rank each model by its ...
An N-gram model predicts the most likely word to follow a sequence of N-1 words given a set of N-1 words. It's a probabilistic model that has been trained on a text corpus. Many NLP applications, such as speech recognition, machine translation, and predi