metadata

license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: hubert-classifier-aug
    results: []

hubert-classifier-aug

This model is a fine-tuned version of facebook/hubert-base-ls960 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 4.4217
Accuracy: 0.0135
Precision: 0.0002
Recall: 0.0135
F1: 0.0004
Binary: 0.1334

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: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Binary
No log	0.19	50	4.4268	0.0135	0.0002	0.0135	0.0004	0.1315
No log	0.38	100	4.4280	0.0135	0.0002	0.0135	0.0004	0.1261
No log	0.58	150	4.4240	0.0135	0.0002	0.0135	0.0004	0.1334
No log	0.77	200	4.4235	0.0135	0.0002	0.0135	0.0004	0.1353
No log	0.96	250	4.4229	0.0135	0.0002	0.0135	0.0004	0.1353
No log	1.15	300	4.4210	0.0162	0.0003	0.0162	0.0005	0.1280
No log	1.34	350	4.4216	0.0162	0.0003	0.0162	0.0005	0.1280
No log	1.53	400	4.4225	0.0135	0.0002	0.0135	0.0004	0.1334
No log	1.73	450	4.4229	0.0135	0.0002	0.0135	0.0004	0.1353
4.4254	1.92	500	4.4217	0.0135	0.0002	0.0135	0.0004	0.1334
4.4254	2.11	550	4.4214	0.0135	0.0002	0.0135	0.0004	0.1334
4.4254	2.3	600	4.4213	0.0135	0.0002	0.0135	0.0004	0.1315
4.4254	2.49	650	4.4213	0.0135	0.0002	0.0135	0.0004	0.1261
4.4254	2.68	700	4.4228	0.0135	0.0002	0.0135	0.0004	0.1334
4.4254	2.88	750	4.4209	0.0135	0.0002	0.0135	0.0004	0.1334
4.4254	3.07	800	4.4210	0.0135	0.0002	0.0135	0.0004	0.1261
4.4254	3.26	850	4.4216	0.0135	0.0002	0.0135	0.0004	0.1315

Framework versions

Transformers 4.38.2
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.15.1