mformer-authority
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View details ↗DISCOVER & COMPARE
Explore AI models 2428
mformer-care
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View details ↗mformer-fairness
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View details ↗mformer-loyalty
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View details ↗mformer-sanctity
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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 is a SciBERT-based model fine-tuned to perform Named Entity Recognition for drug names and adverse drug effects. - `B-DRUG`: beginning of a drug entity - `I-DRUG`: within a drug entity - `B-EFFECT`: beginning of an AE entity - `I-EFFECT`: within an AE entity - `O`: outside either of the above entities To get started using this model for inference, simply set up an NER `pipeline` like below:
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View details ↗BC5CDR_PubMedBERT_NER
judithrosell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-ner-multilingual
julian-schelb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 ↗xlm-v-base-language-id
juliensimon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-plutchik
JuliusAlphonso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-toxic-comment-classification
JungleLeeThis model is a fine-tuned version of the bert-base-uncased model to classify toxic comments. The training data comes from this Kaggle competition. We use 90% of the `train.csv` data to train the model.
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View details ↗bert-ner-japanese
jurabi抽出される固有表現のタイプは、以下の8種類です。 - 人名 - 法人名（法人または法人に類する組織） - 政治的組織名（政治的組織名、政党名、政府組織名、行政組織名、軍隊名、国際組織名） - その他の組織名 （競技組織名、公演組織名、その他） - 地名 - 施設名 - 製品名（商品名、番組名、映画名、書籍名、歌名、ブランド名等） - イベント名
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View details ↗selfjev-4b
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View details ↗selfjev-4b-vision
Jwuthrich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 ↗Fake-News-Bert-Detect
jy46604790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 ↗ettin-32m-nemotron-pii
kalyan-ks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 ↗ettin-68m-nemotron-pii
kalyan-ks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-cased-ner-conll2003
kamalkraj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 ↗BioELECTRA-PICO
kamalkraj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 ↗finbert-ner
KansallisarkistoThe model performs named entity recognition from text input in Finnish. It was trained by fine-tuning bert-base-finnish-cased-v1, using 10 named entity categories. Training data contains for instance the Turku OntoNotes Entities Corpus, the Finnish part of the NewsEye dataset as well as an annotated dataset consisting of Finnish document data from the 1970s onwards, digitized by the National Archives of Finland. Since the latter dataset contains also sensitive data, it has not been made publicly available. An example of how the model can be used for named entity recognition is provided in this Colab notebook. Motivations behind model development and the data selection and annotation processes have been described in more detail in the article Making sense of bureaucratic documents – Named entity recognition for state authority archives.
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View details ↗Medical-NER
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View details ↗roberta-large-fomc
karoldobiczek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 ↗Arch-Guard
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