This model is a fine-tuned version of FacebookAI/xlm-roberta-large on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 8 - totaltrainbatchsize: 64 - lrschedulertype: linear - lrschedulerwarmupratio: 0.1 - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 4.57.6 - Pytorch 2.5.1+cu121 - Datasets 3.2.0 - Tokenizers 0.22.2
Open weights
mit
559M parameters
514 tokens
transformers
This model is a fine-tuned version of FacebookAI/xlm-roberta-large on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 8 - totaltrainbatchsize: 64 - lrschedulertype: linear - lrschedulerwarmupratio: 0.1 - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 4.57.6 - Pytorch 2.5.1+cu121 - Datasets 3.2.0 - Tokenizers 0.22.2
Open weights
mit
559M parameters
514 tokens
transformers
This model is a fine-tuned version of FacebookAI/xlm-roberta-large on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 3e-05 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 8 - totaltrainbatchsize: 64 - lrschedulertype: linear - lrschedulerwarmupratio: 0.1 - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 4.57.6 - Pytorch 2.5.1+cu121 - Datasets 3.2.0 - Tokenizers 0.22.2
Open weights
mit
559M parameters
514 tokens
transformers
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. 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. To get started install the package from pypi: output output The performance differs for the single punctuation markers as hyphens and colons, in many…
Open weights
mit
559M parameters
514 tokens
transformers
Model · Token classification
OpenMed
Specialized model for Gene Entity Recognition - Gene-related entities This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for gene entity recognition - gene-related entities. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research applications. This model can identify and classify the…
Open weights
apache-2.0
559M parameters
514 tokens
transformers
Model · Token classification
OpenMed
Specialized model for Chemical Entity Recognition - Identifies chemical compounds and substances in biomedical literature This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for chemical entity recognition - identifies chemical compounds and substances in biomedical literature. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with…
Open weights
apache-2.0
559M parameters
514 tokens
transformers
