Dolphin-Xgen-RL
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View details ↗DISCOVER & COMPARE
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View details ↗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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View details ↗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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View details ↗dqnCode-v1
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View details ↗dqnCode-v1-GGUF
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View details ↗dqnGPT-v1-GGUF
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View details ↗dqncode-tiny-test1
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View details ↗DrBERT-7GB
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View details ↗NTDB-GPT2
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View details ↗pixai-tagger-v1.0-mixed-bf16
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View details ↗ToriiGate-0.5-GGUF
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View details ↗L3-8B-Stheno-v3.2-GGUF
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View details ↗BGE-m3-ko
dragonkueThis 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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View details ↗bge-reranker-v2-m3-ko
dragonkueDifferent 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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View details ↗multilingual-e5-small-ko
dragonkueThis 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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View details ↗multilingual-e5-small-ko-v2
dragonkueThis 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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View details ↗snowflake-arctic-embed-l-v2.0-ko
dragonkueThis 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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View details ↗vibert-capu
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