metadata

tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
widget:
  - text: I'm ecstatic my flight was just delayed
model-index:
  - name: bertweet-base-finetuned-SARC-DS
    results: []

bertweet-base-finetuned-SARC-DS

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

Loss: 1.7094
Accuracy: 0.7636
Precision: 0.7637
Recall: 0.7636
F1: 0.7636

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-05
train_batch_size: 16
eval_batch_size: 32
seed: 43
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.4978	1.0	44221	0.4899	0.7777	0.7787	0.7778	0.7775
0.4413	2.0	88442	0.4833	0.7798	0.7803	0.7798	0.7797
0.3943	3.0	132663	0.5387	0.7784	0.7784	0.7784	0.7784
0.3461	4.01	176884	0.6184	0.7690	0.7701	0.7690	0.7688
0.3024	5.01	221105	0.6899	0.7684	0.7691	0.7684	0.7682
0.2653	6.01	265326	0.7805	0.7654	0.7660	0.7654	0.7653
0.2368	7.01	309547	0.9066	0.7643	0.7648	0.7643	0.7642
0.2166	8.01	353768	1.0548	0.7612	0.7620	0.7611	0.7610
0.2005	9.01	397989	1.0649	0.7639	0.7639	0.7639	0.7639
0.1837	10.02	442210	1.1805	0.7621	0.7624	0.7621	0.7621
0.1667	11.02	486431	1.3017	0.7658	0.7659	0.7659	0.7658
0.1531	12.02	530652	1.2947	0.7627	0.7628	0.7627	0.7627
0.1377	13.02	574873	1.3877	0.7639	0.7639	0.7639	0.7639
0.1249	14.02	619094	1.4468	0.7613	0.7616	0.7613	0.7612
0.1129	15.02	663315	1.4951	0.7620	0.7621	0.7620	0.7620
0.103	16.02	707536	1.5599	0.7619	0.7624	0.7619	0.7618
0.0937	17.03	751757	1.6270	0.7615	0.7616	0.7615	0.7615
0.0864	18.03	795978	1.6918	0.7622	0.7624	0.7622	0.7621
0.0796	19.03	840199	1.7094	0.7636	0.7637	0.7636	0.7636

Framework versions

Transformers 4.20.1
Pytorch 1.10.1+cu111
Datasets 2.3.2
Tokenizers 0.12.1