bert-it-sentiment
pysentimiento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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Explore AI models 2428
bert-pt-emotion
pysentimiento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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View details ↗robertuito-sentiment-analysis
pysentimientoRepository: https://github.com/pysentimiento/pysentimiento/ Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is RoBERTuito, a RoBERTa model trained in Spanish tweets. Use it directly with pysentimiento
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View details ↗thainer-corpus-v2-base-model
pythainlpValidation from the Validation set - Precision: 0.830336794125095 - Recall: 0.873701039168665 - F1: 0.8514671513892494 - Accuracy: 0.9736483416628805 Test from the Test set - Precision: 0.8199168093956447 - Recall: 0.8781446540880503 - F1: 0.8480323927622422 - Accuracy: 0.9724346779516247 Huggingface doesn't support inference token classification for Thai and It will give wrong tag. You must using this code.
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View details ↗51-languages-classifier
qanastek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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View details ↗SentimentArEng
qandos0This model is a fine-tuned version of cardiffnlp/twitter-xlm-roberta-base-sentiment on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.502831 - Accuracy: 0.798512 - The following hyperparameters were used during training: - learning_rate=2e-5 - num_train_epochs=20 - weight_decay=0.01 - batch_size=16, - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.11.0 - Tokenizers 0.14.1
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View details ↗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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View details ↗all-MiniLM-L6-v2-onnx
QdrantONNX port of sentence-transformers/all-MiniLM-L6-v2 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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View details ↗ONNX port of sentence-transformers/all-MiniLM-L6-v2 adjusted to return attention weights. This model is intended to be used for BM42 searches. Here's an example of performing inference using the model with FastEmbed.
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View details ↗bge-small-en-v1.5-onnx-Q
QdrantQuantized ONNX port of BAAI/bge-small-en-v1.5 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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View details ↗bm25
QdrantRepository with files to perform BM25 searches with FastEmbed. > Note: This model is supposed to be used with Qdrant. Vectors have to be configured with Modifier.IDF.
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View details ↗ONNX port of intfloat/multilingual-e5-large for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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View details ↗prompt-injection-sentinel
qualifire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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View details ↗astrlink-guard
QuantumNous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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View details ↗Xevn-4B-GGUF
quevirus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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View details ↗emotion-roberta-large-multilabel-genai-bce
quim-motger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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Qwen2.5-Math-7B-PRM800K
阿里巴巴 · Qwen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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View details ↗Qwen2.5-Math-PRM-72B
阿里巴巴 · Qwen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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View details ↗Qwen2.5-Math-RM-72B
阿里巴巴 · QwenQwen2.5-Math-RM-72B is specifically designed to guide the Qwen2.5-Math model throughout the training process by offering more granular feedback on the quality of reasoning and intermediate steps, ultimately facilitating more robust model improvements. - Multilingual and Multi-Modal Support: Offers preference signals across two languages (Chinese and English) and in dual modes (Chain-of-Thought and Tool-integrated Reasoning), enhancing versatility. - Model Training Guide: - Training Data Enhancement: Employs a data selection process via reward model scoring combined with Rejection Sampling to incrementally enhance the quality of responses - Reinforcement Learning Training: Integrates seamlessly into the reinforcement learning training and provide effective reward signal, further improving model performance.
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View details ↗Qwen2-Math-RM-72B
阿里巴巴 · Qwen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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View details ↗Qwen3-ForcedAligner-0.6B-hf
阿里巴巴 · QwenThe Qwen3-ASR family includes **Qwen3-ASR-1.7B** and **Qwen3-ASR-0.6B**, which support language identification and ASR for 52 languages and dialects. Both leverage large-scale speech training data and the strong audio understanding capability of their foundation model, Qwen3-Omni. The 1.7B version achieves state-of-the-art performance among open-source ASR models and is competitive with the strongest proprietary commercial APIs. - **All-in-one:** Supports language identification and speech recognition for 30 languages and 22 Chinese dialects, including English accents from multiple countries and regions. - **Excellent and Fast:** High-quality and robust recognition under complex acoustic environments. Qwen3-ASR-0.6B reaches 2000× throughput at a concurrency of 128. Both models support streaming/offline unified inference with a single model and handle long audio. - **Forced Alignment:** Qwen3-ForcedAligner-0.6B supports timestamp prediction for arbitrary units within up to 5 minutes of speech in 11 languages, surpassing E2E-based forced-alignment models in accuracy. Until Qwen3-ForcedAligner is part of an official Transformers release, install from source:
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View details ↗Qwen3-VL-Embedding-8B
阿里巴巴 · QwenThe **Qwen3-VL-Embedding** and **Qwen3-VL-Reranker** model series are the latest additions to the Qwen family, built upon the recently open-sourced and powerful Qwen3-VL foundation model. Specifically designed for multimodal information retrieval and cross-modal understanding, this suite accepts diverse inputs including text, images, screenshots, and videos, as well as inputs containing a mixture of these modalities. While the Embedding model generates high-dimensional vectors for broad applications like retrieval and clustering, the Reranker model is engineered to refine these results, establishing a comprehensive pipeline for state-of-the-art multimodal search. - **Multimodal Versatility**: Both models seamlessly handle a wide range of inputs—including text, images, screenshots, and video—within a unified framework. They deliver state-of-the-art performance across diverse multimodal tasks such as image-text retrieval, video-text matching, visual question answering (VQA), and multimodal content clustering.
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View details ↗WorldPM-72B
阿里巴巴 · Qwen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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View details ↗WorldPM-72B-HelpSteer2
阿里巴巴 · Qwen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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