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**french-document-embedding** is an embedding model for documents in the French-English language, with a context length of up to 8096 tokens. This model is a specialized text-embedding model trained specifically for the French-English language. It is built upon gte-multilingual and trained using the [SimilarityLoss], Multi-Negative Ranking Loss, Matryoshka2dLoss and GISTEmbedLoss using guide model. This model embeds and converts long texts or documents into vectors with 786 dimensions, making it useful for vector databases serving semantic search or RAG (Retrieval-Augmented Generation). @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} } @article{zhang2024mgte, title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval}, author={Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Wen and Dai, Ziqi and Tang, Jialong and Lin, Huan and Yang, Baosong and Xie, Pengjun and Huang, Fei and others}, journal={arXiv preprint arXiv:2407.19669}, year={2024} }

Embeddings & similarity
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Model is Fine-tuned using pre-trained facebook/camembert-base and Siamese BERT-Networks with 'sentences-transformers' on dataset stsb **Test Result**: The performance is measured using Pearson and Spearman correlation: - On dev @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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**Test Result**: The performance is measured using Pearson and Spearman correlation: - On dev @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} } @article{martin2020camembert, title={CamemBERT: a Tasty French Language Mode}, author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t}, journal={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, year={2020} }

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The model underwent a rigorous four-stage training and fine-tuning process, each tailored to enhance its ability to generate precise and contextually relevant sentence embeddings for the Vietnamese language. Below is an outline of these stages: - Dataset: XNLI-vn - Method: Training using Multi-Negative Ranking Loss and Matryoshka2dLoss. This stage focused on improving the model's ability to discern and rank nuanced differences in sentence semantics. - Dataset: STSB-vn - Method: Fine-tuning specifically for the semantic textual similarity benchmark using Siamese BERT-Networks configured with the 'sentence-transformers' library. This stage honed the model's precision in capturing semantic similarity across various types of Vietnamese texts. Using this model becomes easy when you have sentence-transformers installed: The model can be evaluated as follows on the Vienamese data of stsb.

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**vietnamese-embedding** is the Embedding Model for Vietnamese language. This model is a specialized sentence-embedding trained specifically for the Vietnamese language, leveraging the robust capabilities of PhoBERT, a pre-trained language model based on the RoBERTa architecture. The model utilizes PhoBERT to encode Vietnamese sentences into a 768-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of Vietnamese sentences, reflecting both the lexical and contextual layers of the language. The model underwent a rigorous four-stage training and fine-tuning process, each tailored to enhance its ability to generate precise and contextually relevant sentence embeddings for the Vietnamese language. Below is an outline of these stages: - Dataset: ViNLI-SimCSE-supervised - Method: Trained using the SimCSE approach which employs a supervised contrastive learning framework. The model was optimized using Triplet Loss to effectively learn from high-quality annotated sentence pairs. - Dataset: XNLI-vn - Method: Continued fine-tuning using Multi-Negative Ranking Loss. This stage focused on improving the model's ability to discern and rank nuanced differences in sentence semantics. - Dataset: STSB-vn - Method: Fine-tuning specifically for the semantic textual similarity benchmark using Siamese BERT-Networks configured with the 'sentence-transformers' library. This stage honed the model's precision in capturing semantic similarity across various types of Vietnamese texts. - Dataset: STSB-vn with generate silver sample from gold sample - Method: Employed an advanced strategy using Augmented SBERT with Pair Sampling Strategies, integrating both Cross-Encoder and Bi-Encoder models. This stage further refined the embeddings by enriching the training data dynamically, enhancing the model's robustness and accuracy in understanding and processing complex Vietnamese language constructs. The performance is measured using Pearson and Spearman correlation: - On dev | Model | Pearson correlation | Spearman correlation | #params | | ------------- | ------------- | ------------- |------------

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Minimax-H3-ComfyUI

daniel042310

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Model trained with the innovative ORPO method, based on the robust VAGOsolutions/SauerkrautLM-Phi-3-medium. The model was trained with the dataset: mlabonne/orpo-dpo-mix-40k, which combines diverse data sources to enhance conversational capabilities and contextual understanding.

Text generation
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Model trained with the novel new ORPO method, based on the recent Llama 3 8b, specifically: VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct The model was trained with the dataset: reciperesearch/dolphin-sft-v0.1-preference which uses Dolphin data with GPT 4 to improve its conversation sections.

Text generation
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This is a fixed version of the Qwen3-Reranker-4B model converted to sequence classification format, optimized for use with vLLM. This model is a pre-converted version of Qwen/Qwen3-Reranker-4B that: - Has been converted from CausalLM to SequenceClassification architecture - Includes proper configuration for vLLM compatibility - Provides ~75,000x reduction in classification head size - Offers ~150,000x fewer operations per token compared to using the full LM head The original converted model (tomaarsen/Qwen3-Reranker-4B-seq-cls) was missing critical vLLM configuration attributes. This version adds:

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