metadata

license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
model-index:
  - name: results
    results: []

results

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

Loss: 0.2919
Precision: 0.8957
Recall: 0.8226
F1: 0.8576

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: 0.0001
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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1
0.3611	0.2	500	0.3194	0.8640	0.8324	0.8479
0.3106	0.4	1000	0.3039	0.8905	0.8013	0.8435
0.3027	0.6	1500	0.2954	0.9022	0.7927	0.8439
0.2952	0.81	2000	0.2864	0.8966	0.8185	0.8558
0.2905	1.01	2500	0.2875	0.8973	0.8150	0.8542
0.2605	1.21	3000	0.2841	0.8924	0.8369	0.8637
0.2591	1.41	3500	0.2820	0.8926	0.8444	0.8678
0.2574	1.61	4000	0.2826	0.8916	0.8359	0.8629
0.2602	1.81	4500	0.2764	0.8989	0.8291	0.8626
0.2561	2.01	5000	0.2813	0.8891	0.8454	0.8667
0.2195	2.22	5500	0.2869	0.9072	0.8110	0.8564
0.2209	2.42	6000	0.2845	0.9002	0.8216	0.8591
0.2178	2.62	6500	0.2827	0.8991	0.8285	0.8624
0.22	2.82	7000	0.2919	0.8957	0.8226	0.8576

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2