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Explore AI models 2428
bert-emotion-classifier
logasanjeev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 ↗snap1-2b-GGUF
logitlab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 ↗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 ↗bert-for-japanese-twitter-sentiment
LoneWolfgang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 ↗jailbreak-classifier
lordofthejars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 ↗emo-mobilebert
lordtt13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 ↗roberta-large-dapt-fomc-hawkish-dovish
LorenzoAleCon29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 ↗roberta-large-fomc-hawkish-dovish
LorenzoAleCon29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 ↗BLEURT-20
lucadilielloTake a look at this repository for the definition of `BleurtConfig`, `BleurtForSequenceClassification` and `BleurtTokenizer` in PyTorch.
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View details ↗BLEURT-20-D12
lucadilielloTake a look at this repository for the definition of `BleurtConfig`, `BleurtForSequenceClassification` and `BleurtTokenizer` in PyTorch.
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View details ↗FinBERT-PT-BR
lucas-lemeFinBERT-PT-BR is a pre-trained NLP model to analyze sentiment of Brazilian Portuguese financial texts. The model was trained in two main stages: language modeling and sentiment modeling. In the first stage, a language model was trained with more than 1.4 million texts of financial news in Portuguese. From this first training, it was possible to build a sentiment classifier with few labeled texts (500) that presented a satisfactory convergence. At the end of the work, a comparative analysis with other models and the possible applications of the developed model are presented. In the comparative analysis, it was possible to observe that the developed model presented better results than the current models in the state of the art. Among the applications, it was demonstrated that the model can be used to build sentiment indices, investment strategies and macroeconomic data analysis, such as inflation.
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View details ↗pos_thai
lunarlist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 ↗distilbert-imdb
lvwerraThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset (training notebook is here). It achieves the following results on the evaluation set: - Loss: 0.1903 - Accuracy: 0.928 The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
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View details ↗This model is distilled from the zero-shot classification pipeline on the Multilingual Sentiment dataset using this script. In reality the multilingual-sentiment dataset is annotated of course, but we'll pretend and ignore the annotations for the sake of example. Teacher model: MoritzLaurer/mDeBERTa-v3-base-mnli-xnli Teacher hypothesis template: "The sentiment of this text is {}." Student model: distilbert-base-multilingual-cased
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View details ↗URM-LLaMa-3.1-8B
LxzGordon- **Paper:** https://arxiv.org/pdf/2410.00847 - **Model:** URM-LLaMa-3.1-8B - Fine-tuned from Skywork-Reward-Llama-3.1-8B
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View details ↗The model is based on DeBERTa-v3-Large that predicts a binary label - 1 for supported and 0 for unsupported. The model is doing predictions on the *sentence-level*. It takes as input a document and a sentence and determine whether the sentence is supported by the document: **MiniCheck-Model(document, claim) -> {0, 1}** MiniCheck-DeBERTa-v3-Large is fine tuned from `microsoft/deberta-v3-large` (He et al., 2023) on the combination of 35K data: - 21K ANLI data (Nie et al., 2020) - 14K synthetic data generated from scratch in a structed way (more details in the paper). The performance of these models is evaluated on our new collected benchmark (unseen by our models during training), LLM-AggreFact, from 11 recent human annotated datasets on fact-checking and grounding LLM generations. MiniCheck-DeBERTa-v3-Large outperform all exisiting specialized fact-checkers with a similar scale. See full results in our work.
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View details ↗MiniCheck-Flan-T5-Large
lytang📃 **MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents**,(EMNLP 2024, GitHub Repo) The model is based on Flan-T5-Large that predicts a binary label - 1 for supported and 0 for unsupported. The model is doing predictions on the *sentence-level*. It takes as input a document and a sentence and determine whether the sentence is supported by the document: **MiniCheck-Model(document, claim) -> {0, 1}** The performance of these models is evaluated on our new collected benchmark (unseen by our models during training), LLM-AggreFact, from 11 recent human annotated datasets on fact-checking and grounding LLM generations. MiniCheck-Flan-T5-Large outperform all exisiting specialized fact-checkers with a similar scale by a large margin (4-10% absolute increase) and is on par with GPT-4, but 400x cheaper. See full results in our work.
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View details ↗MiniCheck-RoBERTa-Large
lytangThe model is based on RoBERTA-Large that predicts a binary label - 1 for supported and 0 for unsupported. The model is doing predictions on the *sentence-level*. It takes as input a document and a sentence and determine whether the sentence is supported by the document: **MiniCheck-Model(document, claim) -> {0, 1}** MiniCheck-RoBERTa-Large is fine tuned from the trained RoBERTA-Large model from AlignScore (Zha et al., 2023) on 14K synthetic data generated from scratch in a structed way (more details in the paper). We also have other three MiniCheck model variants: - bespokelabs/Bespoke-Minicheck-7B (Model Size: 7B) - lytang/MiniCheck-Flan-T5-Large (Model Size: 0.8B) - lytang/MiniCheck-DeBERTa-v3-Large (Model Size: 0.4B)
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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 ↗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 ↗deepct
macavaney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 ↗autonlp-Gibberish-Detector-492513457
madhurjindal{ "@context": "https://schema.org", "@type": "SoftwareApplication", "name": "Gibberish Detector - High-Accuracy Text Classification Model", "url": "https://huggingface.co/madhurjindal/autonlp-Gibberish-Detector-492513457", "applicationCategory": "NaturalLanguageProcessing", "description": "State-of-the-art gibberish detection model using DistilBERT. Detect nonsensical text, spam, and incoherent input with 97.36% accuracy. Perfect for chatbots, content moderation, and text validation.", "keywords": "gibberish detector, gibberish detection, text classification, spam filter, content moderation, text validation, NLP model, DistilBERT, AutoNLP, text quality, input validation, chatbot filter", "creator": { "@type": "Person", "name": "Madhur Jindal" }, "datePublished": "2021-05-01", "softwareVersion": "1.0", "operatingSystem": "Cross-platform", "offers": { "@type": "Offer", "price": "0", "priceCurrency": "USD" } } **State-of-the-art gibberish detection model** that accurately identifies nonsensical text, spam, and incoherent input in English. Built with DistilBERT and AutoNLP, this model achieves **97.36% accuracy** in multi-class text classification, making it the ideal solution for content moderation, chatbot input validation, and text quality assurance. - **🎯 97.36% Accuracy**: Industry-leading performance in gibberish detection - **⚡ Fast Inference**: Optimized DistilBERT architecture for real-time applications - **🏷️ Multi-Class Detection**: Distinguishes between Noise, Word Salad, Mild Gibberish, and Clean text - **🔧 Easy Integration**: Simple API with transformers pipeline - **🌐 Production Ready**: Tested on diverse real-world datasets - **💚 Eco-Friendly**: Low carbon footprint (5.53g CO2 emissions)
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View details ↗Jailbreak-Detector
madhurjindalWelcome to the **Jailbreak-Detector** model, an advanced AI solution engineered for detecting jailbreak attempts in user interactions. This state-of-the-art model is pivotal for maintaining the security, integrity, and reliability of AI systems across various applications, including automated customer service, content moderation, and other interactive AI platforms. By leveraging this model, organizations can enhance their AI system's defenses against malicious activities, ensuring safe and secure user interactions. In the rapidly evolving field of artificial intelligence (AI), ensuring the security and integrity of deployed models is of paramount importance. One critical challenge is the potential for "jailbreaking"—a process where users exploit vulnerabilities to manipulate AI systems into performing unintended or harmful actions. To mitigate this risk, we present the **Jailbreak-Detector** model, meticulously trained to identify and classify jailbreak attempts.
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