distilbert_base_SST2

Vishnou/distilbert_base_SST2

distilbert_base_SST2 is a text classification model from Vishnou. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Nov 15, 2023
Updated: Nov 18, 2023
SHA: 06bb8d20

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Base models

Datasets

sst2

Model card

From the Hugging Face model README

distilbert_base_SST2

This model is a fine-tuned version of distilbert-base-uncased on the sst2 dataset. It achieves the following results on the evaluation set:

Loss: 0.4181
Accuracy: 0.8991

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.4378	0.06	500	0.3452	0.8601
0.343	0.12	1000	0.3483	0.8578
0.3342	0.18	1500	0.3373	0.8704
0.308	0.24	2000	0.4102	0.8819
0.2932	0.3	2500	0.3546	0.8830
0.3116	0.36	3000	0.3609	0.8716
0.2805	0.42	3500	0.3800	0.8945
0.2655	0.48	4000	0.4131	0.8842
0.2504	0.53	4500	0.4299	0.8830
0.2543	0.59	5000	0.5196	0.8727
0.2409	0.65	5500	0.4387	0.8807
0.2414	0.71	6000	0.4121	0.8922
0.2319	0.77	6500	0.3772	0.8830
0.247	0.83	7000	0.4179	0.8876
0.2233	0.89	7500	0.3544	0.8945
0.2202	0.95	8000	0.4160	0.8865
0.2242	1.01	8500	0.5125	0.8784
0.1296	1.07	9000	0.4212	0.8842
0.1429	1.13	9500	0.4675	0.8968
0.1466	1.19	10000	0.5034	0.8922
0.1626	1.25	10500	0.4431	0.8945
0.1459	1.31	11000	0.5001	0.8922
0.1489	1.37	11500	0.4739	0.8968
0.1591	1.43	12000	0.3852	0.8945
0.1211	1.48	12500	0.4648	0.8945
0.1275	1.54	13000	0.5281	0.8956
0.1302	1.6	13500	0.4411	0.8933
0.1313	1.66	14000	0.4914	0.8979
0.134	1.72	14500	0.3923	0.8979
0.1355	1.78	15000	0.4164	0.8956
0.1263	1.84	15500	0.4293	0.8945
0.1326	1.9	16000	0.4185	0.8933
0.1315	1.96	16500	0.4181	0.8991

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

Task: Text Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Nov 15, 2023
Updated: Nov 18, 2023