mpnet-base-nli
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mpnet-base-nli-matryoshka
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View details ↗ms-marco-ettin-68m-reranker
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View details ↗Qwen3-Reranker-0.6B
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View details ↗Qwen3-Reranker-0.6B-seq-cls
tomaarsen> [!NOTE] > This is a copy of the Qwen3-Reranker-0.6B model, part of the Qwen3 Reranker series, modified as a sequence classification model instead. See Updated Usage for details on how to use it, or Original Usage for the original usage. > > See this discussion for details on the conversion approach. The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining. **Exceptional Versatility**: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks No.1 in the MTEB multilingual leaderboard (as of June 5, 2025, score 70.58), while the reranking model excels in various text retrieval scenarios.
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View details ↗Qwen3-Reranker-4B-seq-cls
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View details ↗Qwen3-Reranker-8B-seq-cls
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View details ↗Qwen3-VL-Embedding-8B
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View details ↗Qwen3-VL-Reranker-8B
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View details ↗reranker-ModernBERT-large-gooaq-bce
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View details ↗This is a SetFit model trained on the SemEval 2014 Task 4 (Restaurants) dataset that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses BAAI/bge-small-en-v1.5 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification. In particular, this model is in charge of filtering aspect span candidates. 1. Fine-tuning a Sentence Transformer with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. 1. Use a spaCy model to select possible aspect span candidates. 2. **Use this SetFit model to filter these possible aspect span candidates.** 3. Use a SetFit model to classify the filtered aspect span candidates.
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View details ↗This is a SetFit model trained on the SemEval 2014 Task 4 (Restaurants) dataset that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses BAAI/bge-small-en-v1.5 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification. In particular, this model is in charge of classifying aspect polarities. 1. Fine-tuning a Sentence Transformer with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. 1. Use a spaCy model to select possible aspect span candidates. 2. Use a SetFit model to filter these possible aspect span candidates. 3. **Use this SetFit model to classify the filtered aspect span candidates.**
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View details ↗span-marker-bert-tiny-conll03
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View details ↗span-marker-mbert-base-multinerd
tomaarsenThis is a SpanMarker model that can be used for multilingual Named Entity Recognition trained on the MultiNERD dataset. In particular, this SpanMarker model uses bert-base-multilingual-cased as the underlying encoder. See train.py for the training script. Is your data not (always) capitalized correctly? Then consider using this uncased variant of this model by @lxyuan for better performance: lxyuan/span-marker-bert-base-multilingual-uncased-multinerd. The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1
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