granite-guardian-hap-38m
ibm-granite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 ↗DISCOVER & COMPARE
Explore AI models 2428
re2g-reranker-nq
ibm-research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 ↗PropagandaDetection
IDA-SERICS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 ↗Erlangshen-Roberta-110M-NLI
IDEA-CCNL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 ↗Erlangshen-Roberta-110M-Sentiment
IDEA-CCNLThis is the fine-tuned version of the Chinese RoBERTa-wwm-ext-base model on several sentiment analysis datasets. Based on chinese-roberta-wwm-ext-base, we fine-tuned a sentiment analysis version on 8 Chinese sentiment analysis datasets, with totaling 227,347 samples.
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View details ↗Erlangshen-Roberta-330M-NLI
IDEA-CCNL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 ↗ModerationBERT-En-02
ifmain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 ↗AnE-LID
igorsterner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 ↗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 ↗Piiranha (cc-by-nc-nd-4.0 license) is trained to **detect 17 types** of Personally Identifiable Information (PII) across six languages. It successfully **catches 98.27% of PII** tokens, with an overall classification **accuracy of 99.44%**. Piiranha is especially accurate at detecting passwords, emails (100%), phone numbers, and usernames. Performance on PII vs. Non PII classification task: - **Precision: 98.48%** (98.48% of tokens classified as PII are actually PII) - **Recall: 98.27%** (correctly identifies 98.27% of PII tokens) - **Specificity: 99.84%** (correctly identifies 99.84% of Non PII tokens) Piiranha was trained on H100 GPUs generously sponsored by the Akash Network
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View details ↗ru-word-stress-transformer
IlyaGusev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 ↗rubertconv_toxic_clf
IlyaGusev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 ↗ner-newsagency-bert-multilingual
impresso-project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 ↗genderize
imranali291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 ↗hate_speech_en
IMSyPP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 ↗laya-onnx
inferenceprince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 ↗cmf-decision
infosave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 ↗This model is a fine-tuned version of bert-base-uncased on the **GLUE MRPC dataset**. The GLUE MRPC dataset, from The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent. It is a pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. This model, bert-base-uncased-mrpc, is uncased: it does not make a difference between **"english"** and **"English"**. Masked language modeling predicts a masked token in a sequence, and the model can attend to tokens bidirectionally. This means the model has full access to the tokens on the left and right. Masked language modeling is great for tasks that require a good contextual understanding of an entire sequence. BERT is an example of a masked language model. For this model, you don’t need labels (also known as an unsupervised task) because the next word (MLM) is the label BERT base model (uncased) It provides: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally masks the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not.
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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 ↗polite-guard
Intel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 ↗internlm2-1_8b-reward
internlm**InternLM2-1.8B-Reward** is a reward model trained on the foundation of InternLM2-Chat-1.8B-SFT. This model has been trained using over 2.4 million preference samples, both human-annotated and AI-synthesized, achieving outstanding performance while ensuring a balance between helpful and harmless. - **Variety of Sizes Available**: Our open-sourced reward models are available in sizes of **1.8B, 7B, and 20B**, each demonstrating exceptional performance across various metrics. We aim for these different-sized models to facilitate research on the scaling laws of reward models, providing valuable insights to the community. - **Comprehensive Coverage of Preference**: Trained with **2.4 million** preference pairs derived from both human annotations and AI synthesis, covering diverse areas such as dialogue, writing, poetry, summarization, coding, mathematics, etc. It also maintains a balance between helpful and harmless. - **Multilingual Support**: InternLM2-Reward was trained on high-quality **English and Chinese** preference data, delivering robust performance in both languages. This model was applied to the RLHF training process of InternLM2-Chat. The reward model training techniques from the InternLM2 Technical Report have been open-sourced in XTuner, try it out here!
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