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This is a IndicSBERT model ( l3cube-pune/indic-sentence-bert-nli ) trained on the STS dataset of ten major Indian Languages. The single model works for English, Hindi, Marathi, Kannada, Tamil, Telugu, Gujarati, Oriya, Punjabi, Malayalam, and Bengali. The model also has cross-lingual capabilities. Released as a part of project MahaNLP: https://github.com/l3cube-pune/MarathiNLP Generic Indic Sentence BERT model is shared here : l3cube-pune/indic-sentence-bert-nli More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2304.11434) Other Monolingual similarity models are listed below: Marathi Similarity Hindi Similarity Kannada Similarity Telugu Similarity Malayalam Similarity Tamil Similarity Gujarati Similarity Oriya Similarity Bengali Similarity Punjabi Similarity Indic Similarity (multilingual)

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埋め込みと類似度
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CLAP is to audio what CLIP is to image. This is an improved CLAP checkpoint, specifically trained on general audio, music and speech. CLAP (Contrastive Language-Audio Pretraining) is a neural network trained on a variety of (audio, text) pairs. It can be instructed in to predict the most relevant text snippet, given an audio, without directly optimizing for the task. The CLAP model uses a SWINTransformer to get audio features from a log-Mel spectrogram input, and a RoBERTa model to get text features. Both the text and audio features are then projected to a latent space with identical dimension. The dot product between the projected audio and text features is then used as a similar score. You can use this model for zero shot audio classification or extracting audio and/or textual features.

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埋め込みと類似度
入力 テキスト→ 出力 埋め込み
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CLAP is to audio what CLIP is to image. This is an improved CLAP checkpoint, specifically trained on music. CLAP (Contrastive Language-Audio Pretraining) is a neural network trained on a variety of (audio, text) pairs. It can be instructed in to predict the most relevant text snippet, given an audio, without directly optimizing for the task. The CLAP model uses a SWINTransformer to get audio features from a log-Mel spectrogram input, and a RoBERTa model to get text features. Both the text and audio features are then projected to a latent space with identical dimension. The dot product between the projected audio and text features is then used as a similar score. You can use this model for zero shot audio classification or extracting audio and/or textual features.

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埋め込みと類似度
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗
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CLAP is to audio what CLIP is to image. This is an improved CLAP checkpoint, specifically trained on music and speech. CLAP (Contrastive Language-Audio Pretraining) is a neural network trained on a variety of (audio, text) pairs. It can be instructed in to predict the most relevant text snippet, given an audio, without directly optimizing for the task. The CLAP model uses a SWINTransformer to get audio features from a log-Mel spectrogram input, and a RoBERTa model to get text features. Both the text and audio features are then projected to a latent space with identical dimension. The dot product between the projected audio and text features is then used as a similar score. You can use this model for zero shot audio classification or extracting audio and/or textual features.

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埋め込みと類似度
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A rank-16 LoRA finetune of **LCO-Embedding-Omni-7B** (Qwen2.5-Omni thinker) trained with **InfoNCE + Prototypical Contrastive loss (PCL)** on the VoiceCLAP 9-corpus mix with MOSS-Audio k=2 sampled captions. Successor to `laion/voiceclap-large` — **better on every benchmark we measure**. 1. **PCL auxiliary loss (weight 0.1)**: 39 learned emotion prototypes; cross-entropy of audio embeddings vs prototypes on pseudo-labeled clips. 2. **z-scored pseudo-labels**: emolia's `emotion_annotation` scalars, argmax over per-emotion z-scores vs corpus base rates (raw argmax is degenerate — high-base-rate dimensions win ~99% of clips). z ≥ 1.5 labels ~80-98% of emolia across all 39 emotion classes. 3. LoRA rank 16 (α=32) — rank shown equivalent to r=32 in a controlled A/B. Ensemble notes: averaging this model's similarities with `gijs/voiceclap-lco-7b-lora` and the k=10 MOSS variant sets the current VoiceNet records (Emo bal@pp 0.7102; Ext bal@pp 0.6883).

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埋め込みと類似度
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Bilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of XLM-RoBERTa, a pre-trained language model based on the XLM-RoBERTa architecture. The model utilizes xlm-roberta to encode english-french sentences into a 1024-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language. @article{conneau2019unsupervised, title={Unsupervised cross-lingual representation learning at scale}, author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin}, journal={arXiv preprint arXiv:1911.02116}, year={2019} } @article{reimers2019sentence, title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks}, author={Nils Reimers, Iryna Gurevych}, journal={https://arxiv.org/abs/1908.10084}, year={2019} }

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埋め込みと類似度
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Bilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of Multilingual-MiniLM-L12-H384, a pre-trained language model is built upon multilingual-e5 architecture. The model utilizes MiniLM to encode english-french sentences into a 384-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language. @article{conneau2019unsupervised, title={Unsupervised cross-lingual representation learning at scale}, author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin}, journal={arXiv preprint arXiv:1911.02116}, year={2019} } @article{reimers2019sentence, title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks}, author={Nils Reimers, Iryna Gurevych}, journal={https://arxiv.org/abs/1908.10084}, year={2019} }

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埋め込みと類似度
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This is a sentence-transformers model fine-tuned for Ukrainian language: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The original model used for fine-tuning is `sentence-transformers/paraphrase-multilingual-mpnet-base-v2`. See our paper Contextual Embeddings for Ukrainian: A Large Language Model Approach to Word Sense Disambiguation for details. Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

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This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

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埋め込みと類似度
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