Open-Weight Models Under Creative Commons Attribution-NonCommercial-ShareAlike 4.0

This model is an Italian sequence-to-sequence model fine-tuned from the IT5-large for the task of inclusive language rewriting. It has been trained to analyze and rewrite sentences in Italian to make them more inclusive (if needed). For example, the sentence I professori devono essere preparati (The professors must be prepared) is rewritten as Il personale docente deve essere preparato (The teaching staff must be prepared). The model has been trained on a dataset containing a total of 4705 pairs of sentences, each pair containing an inclusive and a non-inclusive sentence. The dataset has been split as follows: We also leverage a small set of synthetic data (generated using a set of rules)…

Model · Audio classification

wav2vec2-large-robust-24-ft-age-gender

audEERING GmbH

The model expects a raw audio signal as input and outputs predictions for age in a range of approximately 0...1 (0...100 years) and gender expressing the probababilty for being child, female, or male. In addition, it also provides the pooled states of the last transformer layer. The model was created by fine-tuning Wav2Vec2-Large-Robust Timit and For this version of the model we trained all 24 transformer layers. An ONNX export of the model is available from Further details are given in the associated paper and tutorial.

Model

layoutlmv3-base

Microsoft

LayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre-trained model. For example, LayoutLMv3 can be fine-tuned for both text-centric tasks, including form understanding, receipt understanding, and document visual question answering, and image-centric tasks such as document image classification and document layout analysis. Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei, ACM Multimedia 2022. If you find LayoutLM useful in your research, please cite the following paper: The content of this project itself is licensed under the…

Model · Audio classification

wav2vec2-large-robust-12-ft-emotion-msp-dim

audEERING GmbH

Please note that this model is for research purpose only. A commercial license for a model that has been trained on much more data can be acquired with audEERING. The model expects a raw audio signal as input, and outputs predictions for arousal, dominance and valence in a range of approximately 0...1. In addition, it provides the pooled states of the last transformer layer. The model was created by fine-tuning The model was pruned from 24 to 12 transformer layers before fine-tuning. An ONNX export of the model is available from doi:10.5281/zenodo.6221127. Further details are given in the associated paper and tutorial.

Model · Token classification

wikineural-multilingual-ner

Babelscape

This is the model card for the EMNLP 2021 paper WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER. We fine-tuned a multilingual language model (mBERT) for 3 epochs on our WikiNEuRal dataset for Named Entity Recognition (NER). The resulting multilingual NER model supports the 9 languages covered by WikiNEuRal (de, en, es, fr, it, nl, pl, pt, ru), and it was trained on all 9 languages jointly. If you use the model, please reference this work in your paper: The original repository for the paper can be found at https://github.com/Babelscape/wikineural. You can use this model with Transformers pipeline for NER. This model is trained on WikiNEuRal, a…

Model · Audio classification

wav2vec2-large-robust-6-ft-age-gender

audEERING GmbH

The model expects a raw audio signal as input and outputs predictions for age in a range of approximately 0...1 (0...100 years) and gender expressing the probababilty for being child, female, or male. In addition, it also provides the pooled states of the last transformer layer. The model was created by fine-tuning Wav2Vec2-Large-Robust Timit and For this version of the model we only trained the first six transformer layers. An ONNX export of the model is available from Further details are given in the associated paper and tutorial.

This model is an Italian classification model fine-tuned from the Italian BERT model for the classification of inclusive language in Italian. It has been trained to detect three classes: - inclusive: the sentence is inclusive (e.g. "Il personale docente e non docente") - notinclusive: the sentence is not inclusive (e.g. "I professori") - notpertinent: the sentence is not pertinent to the task (e.g. "La scuola è chiusa") The model has been trained on a dataset containing: - 8580 training sentences - 1073 validation sentences - 1072 test sentences The data collection has been manually annotated by experts in the field of inclusive language (dataset is not publicly available yet). The model…

Model

t5-base-grammar-correction

Vennify AI

This model generates a revised version of inputted text with the goal of containing fewer grammatical errors. It was trained with Happy Transformer using a dataset called JFLEG. Here's a full article on how to train a similar model. pip install happytransformer

Model · Time series forecasting

patchtst-fm-r1

IBM Research

This model card is for the non-commercial, research version of PatchTST-FM-r1. Please also check-out the Apache-2.0 licensed IBM Granite version. PatchTST was originally released prior to the interest in creating pre-trained, zero-shot time series foundation models that were capable of state-of-the-art performance on out of sample datasets. PatchTST-FM (patched time-series transformer-based foundation model) essentially has the architectural simplicity of PatchTST, but differs in some crucial ways. Coupled with a revised training strategy and a significantly larger training corpus, we are able to train a model that achieves state-of-the-art results on GiftEval. The architecture incorporates…

Model · Audio classification

discogs-maest-10s-pw-129e

Music Technology Group (Universitat Pompeu Fabra)

MAEST is a family of Transformer models based on PASST and focused on music analysis applications. The MAEST models are also available for inference in the Essentia library and for inference and training in the official repository. You can try the MAEST interactive demo on replicate. MAEST is a music audio representation model pre-trained on the task of music style classification. According to the evaluation reported in the original paper, it reports good performance in several downstream music analysis tasks. The MAEST models can make predictions for a taxonomy of 400 music styles derived from the public metadata of Discogs. The MAEST models have reported good performance in downstream…

Model · Summarization

ptt5-base-summ-xlsum

Recogna NLP

PTT5 Summ is a fine-tuned PTT5 model to perform Abstractive Summarization in Brazilian Portuguese texts. This model was fine-tuned on the datasets: RecognaSumm, WikiLingua, XL-Sum, TeMário.pdf) and CSTNews. For further information, please go to PTT5 Summ repository. author="Paiola, Pedro H. and de Rosa, Gustavo H. and Papa, Jo{\~a}o P.", editor="Xavier-Junior, Jo{\~a}o Carlos and Rios, Ricardo Ara{\'u}jo", title="Deep Learning-Based Abstractive Summarization for Brazilian Portuguese Texts", booktitle="BRACIS 2022: Intelligent Systems", year="2022", publisher="Springer International Publishing", address="Cham", pages="479--493", isbn="978-3-031-21689-3"} This model was fine-tuned using the…

Model · Tabular classification

rt-plurel

Stanford Tabular and Relational (STAR) Project

RT-PluRel is a Relational Transformer checkpoint pair for in-context / few-shot entity prediction over multi-table relational databases (no per-task gradient training). Same architecture and file layout as stanford-star/rt-j — drop-in replacement. Architecture: ~85.6M blocks, dmodel 512, 8 heads, dff 2048 · text columns embedded with all-MiniLM-L12-v2 (dtext 384). Each folder contains model.safetensors (weights) and config.json (dims + text-embedding model). The paper/ subdirectory preserves the earlier RT-PluRel release:.pt checkpoints (12 blocks, dmodel 256, dff 1024) pretrained on synthetic relational databases generated by PluRel, plus the continued-pretraining and fine-tuned RelBench…

Model · Speech recognition

koelsch-wav2vec2-ipa

Chem

IPA phoneme recognition for Kölsch (Ripuarian German, Cologne). Fine-tuned from facebook/wav2vec2-xls-r-300m with a CTC head over a 48-symbol IPA inventory. This is also the forced-alignment model. Its frame-level CTC posteriors drive torchaudio.functional.forcedalign to produce Praat TextGrids with word and phone tiers — see notebook 9 in the repository. Trained during the CIF Tandem Fellowship at IfL-Phonetik, University of Cologne, on Alles Kölsch (Bhatt & Lindlar 1998) — 4,670 utterances, 4.5 hours of spontaneous Cologne dialect speech from 105 speakers aged 10–88 across 49 neighbourhoods. Kölsch is Ripuarian German. It has no public speech dataset and no standardised spelling: 94.1 %…

arabic - handwritten-text-recognition - htr - ocr - historical-manuscripts - kraken libraryname: kraken arman-kraken-ocr is a Kraken OCR/HTR model for line-level recognition of handwritten Arabic manuscripts. The model is based on the Muharaf-trained Kraken checkpoint muharafrecbest.mlmodel and was subsequently fine-tuned on the ArMan dataset for historical Arabic manuscript recognition. This checkpoint is a fine-tuned derivative of the Arabic HTR model trained on the Muharaf Corpus: The model was fine-tuned on ArMan as part of our work on historical Arabic manuscript HTR and the AraMS-28k dataset. If you use AraMS-28k in your research, please cite: If you use RefLAM or its annotation…

Model

LIBE

Ca' Foscari NLP

basemodel: - sentence-transformers/LaBSE - language-identification - langID - sentence-embedding - similarity-search - low-resource - low-resource-languages