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

Loss: 0.9347
Accuracy: 0.7204
F1: 0.7203
Recall: 0.7203
Precision: 0.7204

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Recall	Precision
0.5756	1.0	2406	0.5321	0.7265	0.7265	0.7265	0.7265
0.4547	2.0	4812	0.5510	0.7346	0.7345	0.7346	0.7347
0.2839	3.0	7218	0.7041	0.7266	0.7266	0.7266	0.7266
0.1549	4.0	9624	0.9347	0.7204	0.7203	0.7203	0.7204

Framework versions

Transformers 4.42.4
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

arthd24/distill_sarcasm_en

distill_sarcasm_en

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

arthd24/
distill_sarcasm_en