bert-base-uncased-english-sentweet-targeted-insult

This model is a fine-tuned version of textattack/bert-base-uncased-MNLI on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.0152
• Accuracy: 0.7604
• Precision: 0.7624
• Recall: 0.7646
• F1: 0.7602

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	81	0.4608	0.8056	0.8205	0.8160	0.8054
No log	2.0	162	0.4542	0.8160	0.8345	0.8275	0.8157
No log	3.0	243	0.5593	0.7882	0.7981	0.7967	0.7882
No log	4.0	324	0.7187	0.7951	0.8051	0.8037	0.7951
No log	5.0	405	0.9321	0.75	0.7484	0.7503	0.7488
No log	6.0	486	1.0152	0.7604	0.7624	0.7646	0.7602

Framework versions

• Transformers 4.24.0
• Pytorch 1.13.1+cu117
• Datasets 2.6.1
• Tokenizers 0.11.0