在https://huggingface.co/spaces/mteb/leaderboard上可以看到,acge模型已经在目前业界最全面、最权威的中文语义向量评测基准C-MTEB(Chinese Massive Text Embedding Benchmark)的榜单中获得了第一名的成绩。 由上表可以看到,acge_text_embedding模型在“Classification Average (9 datasets)”这一列中,acge_text_embeddi...
Default to the number of CPU cores on the machine [env: TOKENIZATION_WORKERS=] --dtype <DTYPE> The dtype to be forced upon the model [env: DTYPE=] [possible values: float16, float32] --pooling <POOLING> Optionally control the pooling method for embedding models. If `pooling` is not ...
在https://huggingface.co/spaces/mteb/leaderboard上可以看到,acge模型已经在目前业界最全面、最权威的中文语义向量评测基准C-MTEB(Chinese Massive Text Embedding Benchmark)的榜单中获得了第一名的成绩。 由上表可以看到,acge_text_embedding模型在“Classification Average (9 datasets)”这一列中,acge_text_embeddi...
This branch is3 commits ahead of,92 commits behindhuggingface/text-embeddings-inference:main. README License Text Embeddings Inference A blazing fast inference solution for text embeddings models. Benchmark forBAAI/bge-base-en-v1.5on an Nvidia A10 with a sequence length of 512 tokens: ...
最近,MokaHR 团队开发了一种名为 M3E 的模型,这一模型弥补了中文向量文本检索领域的空白, M3E 模型在中文同质文本 S2S 任务上在 6 个数据集的平均表现好于 text2vec 和 text-embedding-ada-002 ,在中文检索任务上也优于二者。 值得关注的是,目前,M3E 模型中使用的数据集、训练脚本、训练好的模型、评测数据...
Huggingface'stransformerslibrary is a great resource for natural language processing tasks, and it includes an implementation of OpenAI'sCLIP modelincluding a pretrained modelclip-vit-large-patch14. The CLIP model is a powerful image and text embedding model that can be used...
Default to the number of CPU cores on the machine [env: TOKENIZATION_WORKERS=] --dtype <DTYPE> The dtype to be forced upon the model [env: DTYPE=] [possible values: float16, float32] --pooling <POOLING> Optionally control the pooling method for embedding models. If `pooling` is not ...
from tensorflow.keras import callbacks, models, layers, preprocessing as kprocessing #(2.6.0) ## for bart import transformers #(3.0.1) 然后我使用 HuggingFace 的加载数据集: ## load the full dataset of 300k articles dataset = datasets.load_dataset("cnn_dailymail", '3.0.0') ...
Input Embedding负责将前述包含4个元素的Token序列转换为维度为[4, N]的Embedding张量后,数个Transformer Block将Embbeding张量变换得到维度仍为[4, N]的特征张量,将最后一个Token(“快”)对应的特征向量通过最后的Linear升维到词表维度和通过Softmax归一化,得到预测的下一个Token的概率(Tensor对应维度为[1, M],...
输出与text embedding进行loss计算。相当于用 tag 指导 image 生成 text; Image-Text Alignment:用了BLIP[29] 中 Encoder 结构(如下),image embedding 与 text embeding送入encoder,用粗粒度的 Image-Text Contrastive(ITC) Loss 和 细粒度的 Image-Text Matching(ITM) Loss 分别进行监督。