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NuNER-multilingual-v0.1
numind**Checkout other models by NuMind:** * SOTA Entity Recognition Foundation Model in English: link * SOTA Sentiment Analysis Foundation Model: English, Multilingual Multilingual BERT finetunned on an artificially annotated multilingual subset of Oscar dataset. This model provides domain & language independent embedding for Entity Recognition Task. We fine-tunned it only on 9 languages but the model can generalize over other languages that are supported by the Multilingual BERT.
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View details ↗NuNER-v0.1
numind**Checkout other models by NuMind:** * SOTA Multilingual Entity Recognition Foundation Model: link * SOTA Sentiment Analysis Foundation Model: English, Multilingual
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View details ↗NuNER-v2.0
numindThis model provides embeddings for the Entity Recognition task in English. It is an improved version of the model from our **paper**. **Checkout other models by NuMind:** * SOTA Zero-shot NER Model NuNER Zero * SOTA Multilingual Entity Recognition Foundation Model: link * SOTA Sentiment Analysis Foundation Model: English, Multilingual Roberta-base fine-tuned on the expanded version of NuNER data using contrastive learning from **NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data**.
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View details ↗NuNER_Zero
numindNuNER Zero is a zero-shot Named Entity Recognition (NER) Model. (Check NuNER for the few-shot setting). NuNER Zero uses the GLiNER architecture: its input should be a concatenation of entity types and text. NuNER Zero was trained on NuNER v2.0 dataset, which combines subsets of Pile and C4 annotated via LLMs using NuNER's procedure.
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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 ↗alpajudge
NVIDIA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 ↗gliner-PII
NVIDIAGLiNER-PII is inspired by the Gretel GLiNER PII/PHI models. Built on the GLiNER large-v2.1 base, it detects and classifies a broad range of Personally Identifiable Information (PII) and Protected Health Information (PHI) in structured and unstructured text. It is non-generative and produces span-level entity annotations with confidence scores across 55+ categories. This model was developed by NVIDIA. GLiNER-PII supports detection and redaction of sensitive information across regulated and enterprise scenarios. Note: performance varies by domain, format, and threshold, so validation and human review are recommended for high‑stakes deployments.
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View details ↗* A RoBERTa [[Liu et al., 2019]](https://arxiv.org/pdf/1907.11692.pdf) model fine-tuned for de-identification of medical notes. * Sequence Labeling (token classification): The model was trained to predict protected health information (PHI/PII) entities (spans). A list of protected health information categories is given by HIPAA. * A token can either be classified as non-PHI or as one of the 11 PHI types. Token predictions are aggregated to spans by making use of BILOU tagging. * The PHI labels that were used for training and other details can be found here: Annotation Guidelines * More details on how to use this model, the format of data and other useful information is present in the GitHub repo: Robust DeID. * A demo on how the model works (using model predictions to de-identify a medical note) is on this space: Medical-Note-Deidentification. * Steps on how this model can be used to run a forward pass can be found here: Forward Pass * In brief, the steps are: * Sentencize (the model aggregates the sentences back to the note level) and tokenize the dataset. * Use the predict function of this model to gather the predictions (i.e., predictions for each token). * Additionally, the model predictions can be used to remove PHI from the original note/text. * The I2B2 2014 [[Stubbs and Uzuner, 2015]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4978170/) dataset was used to train this model.
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View details ↗glyph-v1.1
ogmatrixllm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 ↗resume-ner
oksomu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 ↗fullstop-punctuation-multilang-large
oliverguhrThis model predicts the punctuation of English, Italian, French and German texts. We developed it to restore the punctuation of transcribed spoken language. This multilanguage model was trained on the Europarl Dataset provided by the SEPP-NLG Shared Task. *Please note that this dataset consists of political speeches. Therefore the model might perform differently on texts from other domains.* The model restores the following punctuation markers: **"." "," "?" "-" ":"** We provide a simple python package that allows you to process text of any length.
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View details ↗fullstop-punctuation-multilingual-base
oliverguhr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 predicts the punctuation of English, Italian, French and German texts. We developed it to restore the punctuation of transcribed spoken language. This multilanguage model was trained on the Europarl Dataset provided by the SEPP-NLG Shared Task and for the Dutch language we included the SoNaR Dataset. *Please note that this dataset consists of political speeches. Therefore the model might perform differently on texts from other domains.* The model restores the following punctuation markers: **"." "," "?" "-" ":"** We provide a simple python package that allows you to process text of any length.
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View details ↗german-sentiment-bert
oliverguhrThis model was trained for sentiment classification of German language texts. To achieve the best results all model inputs needs to be preprocessed with the same procedure, that was applied during the training. To simplify the usage of the model, we provide a Python package that bundles the code need for the preprocessing and inferencing. The model uses the Googles Bert architecture and was trained on 1.834 million German-language samples. The training data contains texts from various domains like Twitter, Facebook and movie, app and hotel reviews. You can find more information about the dataset and the training process in the paper. To get started install the package from pypi:
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