metadata

license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: trainer_2f
    results: []

trainer_2f

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

Loss: 0.6467
Precision: 0.8276
Recall: 0.8207
F1: 0.8208
Accuracy: 0.8207

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: 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: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
1.8981	0.27	30	1.7350	0.4229	0.4146	0.3885	0.4146
1.5297	0.54	60	1.3572	0.4949	0.4286	0.3544	0.4286
1.2565	0.81	90	1.0154	0.7047	0.6891	0.6859	0.6891
0.9124	1.08	120	0.8039	0.7558	0.7535	0.7496	0.7535
0.6233	1.35	150	0.6860	0.7788	0.7731	0.7692	0.7731
0.5281	1.62	180	0.6874	0.7504	0.7395	0.7383	0.7395
0.4313	1.89	210	0.6302	0.7992	0.7899	0.7888	0.7899
0.3041	2.16	240	0.6437	0.7706	0.7619	0.7610	0.7619
0.2096	2.43	270	0.6585	0.7847	0.7759	0.7731	0.7759
0.2161	2.7	300	0.6198	0.8121	0.8039	0.8027	0.8039
0.1888	2.97	330	0.6286	0.8298	0.8207	0.8201	0.8207
0.1107	3.24	360	0.6106	0.8297	0.8263	0.8260	0.8263
0.0834	3.51	390	0.6133	0.8223	0.8179	0.8170	0.8179
0.0858	3.78	420	0.6481	0.8244	0.8179	0.8178	0.8179

Framework versions

Transformers 4.39.3
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2