bert-base-cased-finetuned-Stromberg_NLP_Twitter-PoS_v2

bert-base-cased-finetuned-Stromberg_NLP_Twitter-PoS_v2

This model is a fine-tuned version of bert-base-cased on the twitter_pos_vcb dataset. It achieves the following results on the evaluation set:

• Loss: 0.0502

Overall

• Accuracy: 0.9853
• Macro avg:
  • Precision: 0.9296417163691048
  • Recall: 0.8931046018294694
  • F1-score: 0.8930917459781836
  • Support: 308833
• Weighted avg:
  • Precision: 0.985306457604231
  • Recall: 0.9853480683735223
  • F1-Score: 0.9852689858931941
  • Support: 308833

Model description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Token%20Classification/Monolingual/StrombergNLP-Twitter_pos_vcb/NER%20Project%20Using%20StrombergNLP%20Twitter_pos_vcb%20Dataset%20with%20PosEval.ipynb.

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

Training and evaluation data

Dataset Source: https://huggingface.co/datasets/strombergnlp/twitter_pos_vcb

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Framework versions

• Transformers 4.28.1
• Pytorch 2.0.0
• Datasets 2.11.0
• Tokenizers 0.13.3

License Notice

This model is a fine-tuned derivative of a pretrained model. Users must comply with the original model license.

Dataset Notice

This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions.