tiny-audio-granite-qwen-500

Model · Feature extraction

social-jev-0.6B-preview

Social Jev

SocialJev is a family of classification foundation models for the social sciences, inspired by TypeSafe’s Jev. The models combine the flexibility of modern language models pretrained on trillions of tokens of text with the speed of lightweight NLP methods. Define your task through a simple typed interface and SocialJev returns typed outputs. Note: SocialJev is under active development. Interfaces may change between early releases. The model can be used directly through Transformers without installing the SocialJev package: Results are ordered first by document, then by query for that document. Each result provides.choice and.probabilities; the probabilities above are illustrative. The…

Model · Feature extraction

Qwen3-Embedding-0.6B

Qwen

The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining. Exceptional Versatility: The…

Model · Feature extraction

d1-600m-text2sql

Jirameth

Fine-tuned from LiquidAI/d1-omni-600M to select one of four supplied SQL queries using a schema, question, and conversation history. Your application supplies and executes the queries. On 1,000 synthetic development turns, v1 scored 45.6% versus 24.9% for the unchanged base, a gain of 20.7 percentage points. Both used FP32, complete inputs, original query order, batch size 4, and highest-score selection. The set contains 250 four-turn conversations across 10 synthetic clinical task families excluded from training. Each turn receives a fixed supplied history, independent of earlier predictions. This development set was reused for model selection. Results do not establish independent-test or…

Model · Feature extraction

w2v-bert-2.0

AI at Meta

We are open-sourcing our Conformer-based W2v-BERT 2.0 speech encoder as described in Section 3.2.1 of the paper, which is at the core of our Seamless models. This model was pre-trained on 4.5M hours of unlabeled audio data covering more than 143 languages. It requires finetuning to be used for downstream tasks such as Automatic Speech Recognition (ASR), or Audio Classification. This model and its training are supported by Transformers, more on it in the docs. This is a bare checkpoint without any modeling head, and thus requires finetuning to be used for downstream tasks such as ASR. You can however use it to extract audio embeddings from the top layer with this code snippet: To learn more…

Model · Feature extraction

jina-embeddings-v3

Jina AI

jina-embeddings-v3 is a multilingual multi-task text embedding model designed for a variety of NLP applications. Based on the Jina-XLM-RoBERTa architecture, this model supports Rotary Position Embeddings to handle long input sequences up to 8192 tokens. Additionally, it features 5 LoRA adapters to generate task-specific embeddings efficiently. - retrieval.query: Used for query embeddings in asymmetric retrieval tasks - retrieval.passage: Used for passage embeddings in asymmetric retrieval tasks - separation: Used for embeddings in clustering and re-ranking applications - classification: Used for embeddings in classification tasks - text-matching: Used for embeddings in tasks that quantify…

Model · Feature extraction

CoVisco-L-14

Ernie Research

CoVisco-L-14 is the Stage 1 vision encoder of the CoVisco pipeline (Codec-Native Vision Encoder with Native Token Compression for Efficient Unified Visual Understanding). It encodes images and videos in a unified way and is trained by SigLIP-style distillation against two teacher branches. This repository releases only the vision-encoder weights in the safe.safetensors format, together with the minimal model code and a self-contained loader. The model is a transformers.PreTrainedModel and is loadable via frompretrained (see below). - 4D RoPE (ropetheta=10000) for spatio-temporal positions - Three projection heads (each LayerNorm + Linear, output L2-normalized): - toimagecaption → 1536-d…