important_phrase_finder

This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.8366
• Precision: 0.3250
• Recall: 0.0904
• F1: 0.1414
• Accuracy: 0.6376

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: 2e-06
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 5
• total_train_batch_size: 40
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	0.2647	100	0.8560	0.3347	0.0505	0.0878	0.6316
No log	0.5294	200	0.8479	0.3202	0.0663	0.1098	0.6341
No log	0.7941	300	0.8423	0.3127	0.0778	0.1246	0.6355
No log	1.0588	400	0.8409	0.3303	0.0811	0.1303	0.6368
0.8194	1.3235	500	0.8388	0.3257	0.0831	0.1325	0.6373
0.8194	1.5881	600	0.8371	0.3203	0.0868	0.1366	0.6373
0.8194	1.8528	700	0.8366	0.3250	0.0904	0.1414	0.6376

Framework versions

• Transformers 4.42.4
• Pytorch 2.3.1+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1