`nomic-embed-code` is a state-of-the-art code embedding model that excels at code retrieval tasks: - **High Performance**: Outperforms Voyage Code 3 and OpenAI Embed 3 Large on CodeSearchNet - **Multilingual Code Support**: Trained for multiple programming languages (Python, Java, Ruby, PHP, JavaScript, Go) - **Advanced Architecture**: 7B parameter code embedding model - **Fully Open-Source**: Model weights, training data, and evaluation code released - **Total Parameters**: 7B - **Training Approach**: Trained on the CoRNStack dataset with dual-consistency filtering and progressive hard negative mining - **Supported Languages**: Python, Java, Ruby, PHP, JavaScript, and Go
Original model: nomic-embed-text-v1.5 Embedding text with `nomic-embed-text` requires task instruction prefixes at the beginning of each string. For example, the code below shows how to use the `search_query` prefix to embed user questions, e.g. in a RAG application.
Embedding text with `nomic-embed-text` requires task instruction prefixes at the beginning of each string. For example, the code below shows how to use the `search_query` prefix to embed user questions, e.g. in a RAG application. To see the full set of task instructions available & how they are designed to be used, visit the model card for nomic-embed-text-v1.
This model was presented in the paper Training Sparse Mixture Of Experts Text Embedding Models. Original model: nomic-embed-text-v2-moe This model can be used with the llama.cpp server and other software that supports llama.cpp embedding models.
- *pust og ventilering* ( se e-læringskurset PUST ) - *astma og epilepsi* (se Astma - og allergiforbundet og Helse norge.no ) - *ernæring og sondemating* ( se egen te maside f rambu.no )
The models are trained based on `Alibaba-NLP/gte-large-en-v1.5` and `Alibaba-NLP/gte-Qwen2-1.5B-instruct`. Thanks for their contributions! **We simplify usage of prompts, providing two prompts for most general tasks, one is for s2p, another one is for s2s.** The models are finally trained by MRL), so they have multiple dimensions: 512, 768, 1024, 2048, 4096, 6144 and 8192.
The models are trained based on `Alibaba-NLP/gte-large-en-v1.5` and `Alibaba-NLP/gte-Qwen2-1.5B-instruct`. Thanks for their contributions! **We simplify usage of prompts, providing two prompts for most general tasks, one is for s2p, another one is for s2s.** The models are finally trained by MRL, so they have multiple dimensions: 512, 768, 1024, 2048, 4096, 6144 and 8192.
This is the BERT-Medium model from Google: https://github.com/google-research/bert#bert. A BERT model with 2 layers, 128 hidden unit size, and 2 attention heads.
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. Paper: DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT
This model performs visual feature extraction. For instance, RADIO generates image embeddings that can be used by a downstream model to classify images. **Input Type(s):** Image **Input Format(s):** Red, Green, Blue (RGB) pixel values in [0, 1] range. **Input Parameters:** Two Dimensional (2D) **Other Properties Related to Input:** Image resolutions up to 2048x2028 in increments of 16 pixels **Output Type(s):** Embeddings **Output Format:** Tensor **Output Parameters:** 2D **Other Properties Related to Output:** Downstream model required to leverage image features
