BERT base model (uncased) Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is uncased: it does not make a difference between english and English.
Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team.
The Dataset is Annotated Corpus for Named Entity Recognition From ABHINAV Dataset discription: Annotated Corpus for Named Entity Recognition using GMB(Groningen Meaning Bank) corpus for entity classification with enhanced and popular features by Natural Language Processing applied to the data set.
In this project I used PyTorch and the model and dataset in the top and in the deep in traning loop I used 3 and 3e-6 for learning rate, you can fined more about finetuning step in the model file (config.py, bert_model.py and engine.py etc..)
I did not have the resources, such as the Internet, electricity, device, etc., to train the model well and choose the appropriate learning rate, so there were no results.
To contribute to the project, please contribute directly. I am happy to do so, and if you have any comments, advice, job opportunities, or want me to contribute to a project, please contact me V3xlrm1nOwo1@gmail.com