Part of the Dolphin 3.0 Collection Curated and trained by Eric Hartford, Ben Gitter, BlouseJury and Cognitive Computations Our appreciation for the generous sponsors of Dolphin 3.0: - Crusoe Cloud - provided 16x L40s for training and evals - Akash - provided on-demand 8x H100 for training - Lazarus - provided 16x H100 for training - Cerebras - provided excellent and fast inference services for data labeling - Andreessen Horowitz - provided a grant that make Dolphin 1.0 possible and enabled me to bootstrap my homelab
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This model is a fine-tuned version of facebook/wav2vec2-base-960h for **Speech Emotion Recognition (SER)**. It has been trained using a **Frozen Feature Extractor** strategy to preserve the model's acoustic understanding while adapting to emotion detection. This approach ensures stable performance and prevents "Catastrophic Forgetting," achieving nearly **80% accuracy** on the validation set. **Update:** The "Calm" and "Neutral" classes have been merged to improve classification consistency, resulting in **7 distinct emotion classes**.
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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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Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. And the score can be mapped to a float value in [0,1] by sigmoid function. - Base model : BAAI/bge-reranker-v2-m3 - The multilingual model has been optimized for Korean. Bi-Encoders convert texts into fixed-size vectors and efficiently calculate similarities between them. They are fast and ideal for tasks like semantic search and classification, making them suitable for processing large datasets quickly.
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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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Fine-tuned dragonSwing/wav2vec2-base-pretrain-vietnamese on Vietnamese Speech Recognition task using 100h labelled data from VSLP dataset. When using this model, make sure that your speech input is sampled at 16kHz. The model can be used directly (without a language model) as follows:
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