metadata

license: apache-2.0
base_model: alex-miller/ODABert
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: cdp-multi-classifier-weighted
    results: []

cdp-multi-classifier-weighted

This model is a fine-tuned version of alex-miller/ODABert on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.8564
Accuracy: 0.9716
F1: 0.8484
Precision: 0.7788
Recall: 0.9316

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: 1e-06
train_batch_size: 24
eval_batch_size: 24
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
1.0497	1.0	11302	1.5640	0.9621	0.8011	0.7244	0.8958
0.9103	2.0	22604	1.4417	0.9663	0.8203	0.7522	0.9021
0.7629	3.0	33906	0.9562	0.9661	0.8235	0.7406	0.9272
0.6321	4.0	45208	0.9106	0.9697	0.8376	0.7720	0.9153
0.5464	5.0	56510	0.9811	0.9705	0.8419	0.7760	0.9200
0.5043	6.0	67812	0.9484	0.9700	0.8409	0.7677	0.9296
0.4647	7.0	79114	0.8569	0.9713	0.8465	0.7781	0.9281
0.4215	8.0	90416	0.8620	0.9703	0.8430	0.7682	0.9338
0.3794	9.0	101718	0.8569	0.9704	0.8437	0.7682	0.9357
0.344	10.0	113020	0.8305	0.9708	0.8448	0.7720	0.9328
0.3247	11.0	124322	0.7900	0.9707	0.8446	0.7709	0.9338
0.3159	12.0	135624	0.7838	0.9711	0.8463	0.7734	0.9344
0.3166	13.0	146926	0.8381	0.9710	0.8462	0.7727	0.9351
0.279	14.0	158228	0.8694	0.9718	0.8487	0.7821	0.9277
0.281	15.0	169530	0.8564	0.9716	0.8484	0.7788	0.9316

Framework versions

Transformers 4.40.1
Pytorch 2.0.1
Datasets 2.19.0
Tokenizers 0.19.1