This is a sentence-transformers model. It maps sentences & paragraphs (text) into a 1024 dimensional dense vector space. The model is intended to be used together with SetFit to improve German few-shot text classification. It has a sibling model called deutsche-telekom/gbert-large-paraphrase-cosine. **Loss Function**\ We have used BatchHardSoftMarginTripletLoss with eucledian distance as the loss function: **Training Data**\ The model is trained on a carefully filtered dataset of deutsche-telekom/ger-backtrans-paraphrase. We deleted the following pairs of sentences: - `min_char_len` less than 15 - `jaccard_similarity` greater than 0.3 - `de_token_count` greater than 30 - `en_de_token_count` greater than 30 - `cos_sim` less than 0.85
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The UnivNet model is a state-of-the-art neural vocoder which synthesizes audio waveforms from full-band MEL spectrograms, introduced in "UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation" by Won Jang, Dan Lim, Jaesam Yoon, Bongwan Kim, Juntae Kim. UnivNet is a generative adversarial network (GAN) in which the generator is trained to convert real (or fake, during training) log MEL spectrograms to waveforms, and the discriminator is trained to classify whether input waveforms are real or fake. From the original paper abstract: > Most neural vocoders employ band-limited mel-spectrograms to generate waveforms. If full-band spectral features are used as the input, the vocoder can be provided with as much acoustic information as possible. However, in some models employing full-band mel-spectrograms, an over-smoothing problem occurs as part of which non-sharp spectrograms are generated. To address this problem, we propose UnivNet, a neural vocoder that synthesizes high-fidelity waveforms in real time. Inspired by works in the field of voice activity detection, we added a multi-resolution spectrogram discriminator that employs multiple linear spectrogram magnitudes computed using various parameter sets. Using full-band mel-spectrograms as input, we expect to generate high-resolution signals by adding a discriminator that employs spectrograms of multiple resolutions as the input. In an evaluation on a dataset containing information on hundreds of speakers, UnivNet obtained the best objective and subjective results among competing models for both seen and unseen speakers. These results, including the best subjective score for text-to-speech, demonstrate the potential for fast adaptation to new speakers without a need for training from scratch. This checkpoint was released as part of an unofficial implementation by maum-ai (on which the `transformers` implementation is also based). As far as I know, there is no official model or code release by the original authors from Kakao Enterprise.
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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. For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: https://seb.sbert.net
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State-of-the-art language model for Hebrew, released here. - Prefix Segmentation - Morphological Disabmgiuation - Lexicographical Analysis (Lemmatization) - Syntactical Parsing (Dependency-Tree) - Named-Entity Recognition A live demo of the model with instant visualization of the syntax tree can be found here.
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This is a sentence-transformers model trained on the train_set dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. - Learning other languages besides Chinese and English is insufficient, so additional learning is needed to optimize use of other languages. - This model is additionally trained on the Korean dataset. - **Model Type:** Sentence Transformer Transformer Encoder - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity
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This is a sentence-transformers model finetuned from intfloat/multilingual-e5-small on datasets that include Korean query-passage pairs for improved performance on Korean retrieval tasks. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. This model is a lightweight Korean retriever, designed for ease of use and strong performance in practical retrieval tasks. It is ideal for running demos or lightweight applications, offering a good balance between speed and accuracy. For even higher retrieval performance, we recommend combining it with a reranker. Suggested reranker models:
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This is a sentence-transformers model finetuned from intfloat/multilingual-e5-small on datasets that include Korean query-passage pairs for improved performance on Korean retrieval tasks. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. This model is a lightweight Korean retriever, designed for ease of use and strong performance in practical retrieval tasks. It is ideal for running demos or lightweight applications, offering a good balance between speed and accuracy. This small-sized model delivers **superior performance** on Korean benchmarks compared to the much larger 'intfloat/multilingual-e5-base' model (which has over 2x parameters). This means that you can enjoy performance superior to the base model while using half the computing resources.
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This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-l-v2.0 on the clustered datasets. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search. The **Snowflake/snowflake-arctic-embed-l-v2.0** model has been further trained with Korean data to enhance its performance in **Korean retrieval tasks**. It is a powerful model that achieves **state-of-the-art (SOTA) performance across multiple retrieval benchmarks**. - **Model Type:** Sentence Transformer - **Base model:** Snowflake/snowflake-arctic-embed-l-v2.0 - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity - **Training Datasets:** - AI Hub Dataset - 행정 문서 대상 기계 독해 - 기계 독해 - 뉴스 기사 기계독해 - 도서 자료 기계독해 - 숫자 연산 기계독해 - 금융 법률 문서 기계독해 - **Language:** Korean, English
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