Akdeniz27

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Average Model Cost: $0.0000

Number of Runs: 6,353

Models by this creator

bert-base-turkish-cased-ner

bert-base-turkish-cased-ner

akdeniz27

This model is the fine-tuned model of "dbmdz/bert-base-turkish-cased" using a reviewed version of well known Turkish NER dataset (https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt). Pls refer "https://huggingface.co/transformers/_modules/transformers/pipelines/token_classification.html" for entity grouping with aggregation_strategy parameter. Evaluation results with the test sets proposed in "K├╝├ž├╝k, D., K├╝├ž├╝k, D., Ar─▒c─▒, N. 2016. T├╝rk├že Varl─▒k ─░smi Tan─▒ma i├žin bir Veri K├╝mesi ("A Named Entity Recognition Dataset for Turkish"). IEEE Sinyal ─░┼čleme, ─░leti┼čim ve Uygulamalar─▒ Kurultay─▒. Zonguldak, T├╝rkiye." paper.

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$-/run

3.7K

Huggingface

roberta-large-cuad

roberta-large-cuad

This model is the fine-tuned version of "RoBERTa Large" using CUAD dataset The Contract Understanding Atticus Dataset (CUAD), pronounced "kwad", a dataset for legal contract review curated by the Atticus Project. Contract review is a task about "finding needles in a haystack." We find that Transformer models have nascent performance on CUAD, but that this performance is strongly influenced by model design and training dataset size. Despite some promising results, there is still substantial room for improvement. As one of the only large, specialized NLP benchmarks annotated by experts, CUAD can serve as a challenging research benchmark for the broader NLP community. Legal contract review More information needed The model should not be used to intentionally create hostile or alienating environments for people. Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recomendations. See cuad dataset card for further details More information needed More information needed More information needed Researchers may be interested in several gigabytes of unlabeled contract pretraining data, which is available here. More information needed More information needed We provide checkpoints for three of the best models fine-tuned on CUAD: RoBERTa-base (100M parameters), RoBERTa-large (300M parameters), and DeBERTa-xlarge (~900M parameters). More information needed Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). More information needed More information needed More information needed The HuggingFace Transformers library. It was tested with Python 3.8, PyTorch 1.7, and Transformers 4.3/4.4. BibTeX: @article{hendrycks2021cuad, title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review}, author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball}, journal={NeurIPS}, year={2021} } More information needed For more details about CUAD and legal contract review, see the Atticus Project website. TheAtticusProject TheAtticusProject, in collaboration with the Ezi Ozoani and the HuggingFace Team Use the code below to get started with the model.

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119

Huggingface

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