internlm2-20b-reward
internlm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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internlm2-7b-reward
internlm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 ↗POLAR-1_8B
internlm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 ↗POLAR-1_8B-Base
internlm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 ↗POLAR-7B
internlm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 ↗POLAR-7B-Base
internlm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 ↗e5-base
intfloat**News (May 2023): please switch to e5-base-v2, which has better performance and same method of usage.** Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. Please refer to our paper at https://arxiv.org/pdf/2212.03533.pdf.
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View details ↗e5-base-v2
intfloatBelow is an example to encode queries and passages from the MS-MARCO passage ranking dataset. Please refer to our paper at https://arxiv.org/pdf/2212.03533.pdf. Check out unilm/e5 to reproduce evaluation results on the BEIR and MTEB benchmark.
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View details ↗e5-large-v2
intfloatBelow is an example to encode queries and passages from the MS-MARCO passage ranking dataset. Please refer to our paper at https://arxiv.org/pdf/2212.03533.pdf. Check out unilm/e5 to reproduce evaluation results on the BEIR and MTEB benchmark.
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View details ↗e5-small-v2
intfloatBelow is an example to encode queries and passages from the MS-MARCO passage ranking dataset. Please refer to our paper at https://arxiv.org/pdf/2212.03533.pdf. Check out unilm/e5 to reproduce evaluation results on the BEIR and MTEB benchmark.
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View details ↗multilingual-e5-base
intfloatMultilingual E5 Text Embeddings: A Technical Report. Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model is initialized from xlm-roberta-base and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation. Here are some rules of thumb: - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
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View details ↗multilingual-e5-large
intfloatMultilingual E5 Text Embeddings: A Technical Report. Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model is initialized from xlm-roberta-large and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation. Here are some rules of thumb: - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
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View details ↗multilingual-e5-large-instruct
intfloatBelow are examples to encode queries and passages from the MS-MARCO passage ranking dataset. This model is initialized from xlm-roberta-large and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation. **Initialization**: xlm-roberta-large
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View details ↗multilingual-e5-small
intfloat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-finetuned-ner-ontonotes5
IreNkweke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-base-goemotions
IsaacZhy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 ↗modernbert-ner-conll2003
IsmaelMousa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 microsoft/deberta-v3-base on the ai4privacy/pii-masking-200k dataset. This model has been finetuned on the World's largest open source privacy dataset. The purpose of the trained models is to remove personally identifiable information (PII) from text, especially in the context of AI assistants and LLMs.
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View details ↗This model is a fine-tuned version of distilbert-base-german-cased on the German subset of pii-masking-200k dataset. It achieves the following results on the evaluation set: - Loss: 0.0821 - Overall Precision: 0.9086 - Overall Recall: 0.9379 - Overall F1: 0.9230 - Overall Accuracy: 0.9679 The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 5
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View details ↗distilbert_finetuned_ai4privacy
Isotonic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 distilbert-base-uncased on the English Subset of ai4privacy/pii-masking-200k dataset. The purpose of the trained models is to remove personally identifiable information (PII) from text, especially in the context of AI assistants and LLMs. The example texts have 54 PII classes (types of sensitive data), targeting 229 discussion subjects / use cases split across business, education, psychology and legal fields, and 5 interactions styles (e.g. casual conversation, formal document, emails etc...).
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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 ↗emotion-english-distilroberta-base
j-hartmannWith this model, you can classify emotions in English text data. The model was trained on 6 diverse datasets (see Appendix below) and predicts Ekman's 6 basic emotions, plus a neutral class: 1) anger 🤬 2) disgust 🤢 3) fear 😨 4) joy 😀 5) neutral 😐 6) sadness 😭 7) surprise 😲 The model is a fine-tuned checkpoint of DistilRoBERTa-base. For a 'non-distilled' emotion model, please refer to the model card of the RoBERTa-large version.
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