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View details ↗russian-ai-text-detector-modernbert
toiletsandpaper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 ↗NeoHorse-Jev-4B
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View details ↗NeoHorse-Jev-4B-GGUF
TokenRhythm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 ↗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 ↗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 ↗span-marker-bert-tiny-conll03
tomaarsen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 ↗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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View details ↗line_item_categories
tomerRestThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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View details ↗toxigen_hatebert
tomhThis model comes from the paper ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection and can be used to detect implicit hate speech.
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View details ↗toxigen_roberta
tomhThis model comes from the paper ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection and can be used to detect implicit hate speech.
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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 ↗clef-flash-mlx-8bit
TrevorJS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 ↗tiny-GPTNeoXForSequenceClassification
trl-internal-testing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 ↗tiny-LlamaForSequenceClassification-3.2
trl-internal-testingThis is a minimal model built for unit tests in the TRL library.
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View details ↗tiny-Qwen2ForSequenceClassification-2.5
trl-internal-testingThis is a minimal model built for unit tests in the TRL library.
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View details ↗tiny-Qwen3ForSequenceClassification
trl-internal-testing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 ↗RADAR-Vicuna-7B
TrustSafeAIRADAR-Vicuna-7B is an AI-text detector trained via adversarial learning between the detector and a paraphraser on human-text corpus (OpenWebText) and AI-text corpus generated based on OpenWebText. - **Developed by:** TrustSafeAI - **Model type:** An encoder-only language model based on the transformer architecture (RoBERTa). - **License:** Non-commercial license (inherited from Vicuna-7B-v1.1) - **Trained from model:** RoBERTa - **Project Page:** https://radar.vizhub.ai/ - **Paper:** https://arxiv.org/abs/2307.03838 - **IBM Blog Post:** https://research.ibm.com/blog/AI-forensics-attribution
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View details ↗xlm-roberta-ner-japanese
tsmatz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 ↗modernbert-fingpt
tsphua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 ↗bge-reranker-v2-m3-onnx-int8
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