This repository is an **Ente-operated, byte-for-byte mirror** of a single upstream GGUF artifact. It exists so that Ensu's on-device retrieval feature can download its shared embedding model directly, while remaining exactly compatible with the published Ensu knowledge packs. Ente has **not** modified, re-converted, re-quantized, or repacked the GGUF file. See `MODIFICATIONS.md` for the modification disclosure and `PROVENANCE.json` for machine-readable provenance. The GGUF Q8_0 artifact is a converted/quantized derivative of `google/embeddinggemma-300m`; the conversion was published upstream by ggml-org.
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
BART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository. Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team. BART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
BART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository. Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team. BART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
This model is the finetuned version of the pre-trained contriever model available here https://huggingface.co/facebook/contriever, following the approach described in Towards Unsupervised Dense Information Retrieval with Contrastive Learning. The associated GitHub repository is available here https://github.com/facebookresearch/contriever. Using the model directly available in HuggingFace transformers requires to add a mean pooling operation to obtain a sentence embedding.
- Model Details - How To Get Started With the Model - Uses - Risks, Limitations and Biases - Training - Evaluation - Environmental Impact - Technical Specifications - Citation Information - Model Card Authors **Model Description:** Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art open-domain Q&A research. `dpr-question_encoder-single-nq-base` is the question encoder trained using the Natural Questions (NQ) dataset (Lee et al., 2019; Kwiatkowski et al., 2019). - **Developed by:** See GitHub repo for model developers - **Model Type:** BERT-based encoder - **Language(s):** CC-BY-NC-4.0, also see Code of Conduct - **License:** English - **Related Models:** - `dpr-ctx_encoder-single-nq-base` - `dpr-reader-single-nq-base` - `dpr-ctx_encoder-multiset-base` - `dpr-question_encoder-multiset-base` - `dpr-reader-multiset-base` - **Resources for more information:** - Research Paper - GitHub Repo - Hugging Face DPR docs - BERT Base Uncased Model Card
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