twitter_ner

This model is a fine-tuned version of distilbert-base-cased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0142
• Precision: 0.9328
• Recall: 0.9504
• F1: 0.9415
• Accuracy: 0.9978

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 8e-05
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	30	0.2020	0.2932	0.0479	0.0824	0.9516
No log	2.0	60	0.1365	0.4653	0.3328	0.3880	0.9651
No log	3.0	90	0.0783	0.6292	0.6125	0.6207	0.9803
No log	4.0	120	0.0465	0.7196	0.7793	0.7483	0.9877
No log	5.0	150	0.0285	0.8493	0.8725	0.8608	0.9936
No log	6.0	180	0.0197	0.9012	0.9204	0.9107	0.9963
No log	7.0	210	0.0157	0.9124	0.9350	0.9235	0.9969
No log	8.0	240	0.0142	0.9328	0.9504	0.9415	0.9978

Framework versions

• Transformers 4.30.1
• Pytorch 2.0.1+cu117
• Datasets 2.12.0
• Tokenizers 0.13.3