metadata

library_name: transformers
license: apache-2.0
base_model: google/bigbird-roberta-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: >-
      jackmedda/google-bigbird-roberta-base_finetuned_augmented_augmented_deepseek
    results: []

jackmedda/google-bigbird-roberta-base_finetuned_augmented_augmented_deepseek

This model is a fine-tuned version of google/bigbird-roberta-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.3040
Accuracy: 0.7647
F1: 0.8571
Precision: 0.8
Recall: 0.9231

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-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.4568	1.0	46	0.7344	0.7	0.8235	0.7	1.0
0.6053	2.0	92	0.6889	0.7	0.8235	0.7	1.0
0.5004	3.0	138	0.6289	0.7	0.8235	0.7	1.0
0.6076	4.0	184	0.6493	0.7	0.8235	0.7	1.0
0.3798	5.0	230	0.7945	0.7	0.8235	0.7	1.0
0.2401	6.0	276	1.2776	0.7	0.8235	0.7	1.0
0.4076	7.0	322	1.1325	0.7	0.8235	0.7	1.0
0.1193	8.0	368	1.1424	0.7	0.8235	0.7	1.0
0.3332	9.0	414	0.9214	0.8	0.875	0.7778	1.0
0.1445	10.0	460	0.2424	0.9	0.9333	0.875	1.0
0.0029	11.0	506	1.6181	0.7	0.8235	0.7	1.0
0.1174	12.0	552	0.0031	1.0	1.0	1.0	1.0
0.0014	13.0	598	1.2314	0.8	0.875	0.7778	1.0
0.0011	14.0	644	1.1105	0.8	0.875	0.7778	1.0
0.0008	15.0	690	1.2286	0.8	0.875	0.7778	1.0
0.0008	16.0	736	1.2704	0.8	0.875	0.7778	1.0
0.0006	17.0	782	1.3157	0.8	0.875	0.7778	1.0
0.0005	18.0	828	1.3290	0.8	0.875	0.7778	1.0
0.0005	19.0	874	1.3752	0.8	0.875	0.7778	1.0
0.0004	20.0	920	1.3951	0.8	0.875	0.7778	1.0

Framework versions

Transformers 4.48.3
Pytorch 2.3.0+cu121
Datasets 3.2.0
Tokenizers 0.21.0