distilbert-base-uncased-sentiment-finetuned-memes-30epochs

jayanta/distilbert-base-uncased-sentiment-finetuned-memes-30epochs

distilbert-base-uncased-sentiment-finetuned-memes-30epochs is a text classification model from jayanta. 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: distilbert
Access: Public
Created: Sep 20, 2022
Updated: Sep 20, 2022
SHA: 112fcd05

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Model card

From the Hugging Face model README

distilbert-base-uncased-sentiment-finetuned-memes-30epochs

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

Loss: 1.8839
Accuracy: 0.8365
Precision: 0.8373
Recall: 0.8365
F1: 0.8368

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: 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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.4774	1.0	2147	0.4463	0.7453	0.7921	0.7453	0.7468
0.4036	2.0	4294	0.5419	0.7835	0.8072	0.7835	0.7858
0.3163	3.0	6441	0.6776	0.7982	0.7970	0.7982	0.7954
0.2613	4.0	8588	0.6988	0.7966	0.7953	0.7966	0.7956
0.229	5.0	10735	0.8523	0.8003	0.8033	0.8003	0.8013
0.1893	6.0	12882	1.0472	0.8056	0.8166	0.8056	0.8074
0.1769	7.0	15029	1.0321	0.8150	0.8193	0.8150	0.8161
0.1648	8.0	17176	1.1623	0.8129	0.8159	0.8129	0.8138
0.1366	9.0	19323	1.1932	0.8255	0.8257	0.8255	0.8256
0.1191	10.0	21470	1.2308	0.8349	0.8401	0.8349	0.8361
0.1042	11.0	23617	1.3166	0.8297	0.8288	0.8297	0.8281
0.0847	12.0	25764	1.3542	0.8286	0.8278	0.8286	0.8280
0.0785	13.0	27911	1.3925	0.8291	0.8293	0.8291	0.8292
0.0674	14.0	30058	1.4191	0.8255	0.8307	0.8255	0.8267
0.0694	15.0	32205	1.5601	0.8255	0.8281	0.8255	0.8263
0.0558	16.0	34352	1.6110	0.8265	0.8302	0.8265	0.8275
0.045	17.0	36499	1.5730	0.8270	0.8303	0.8270	0.8280
0.0436	18.0	38646	1.6081	0.8365	0.8361	0.8365	0.8363
0.028	19.0	40793	1.5569	0.8375	0.8371	0.8375	0.8373
0.0262	20.0	42940	1.6976	0.8286	0.8324	0.8286	0.8296
0.0183	21.0	45087	1.6368	0.8333	0.8354	0.8333	0.8340
0.0225	22.0	47234	1.7570	0.8318	0.8357	0.8318	0.8328
0.0118	23.0	49381	1.7233	0.8360	0.8369	0.8360	0.8363
0.0152	24.0	51528	1.8027	0.8360	0.8371	0.8360	0.8364
0.0079	25.0	53675	1.7908	0.8412	0.8423	0.8412	0.8416
0.0102	26.0	55822	1.8247	0.8344	0.8339	0.8344	0.8341
0.0111	27.0	57969	1.8123	0.8391	0.8394	0.8391	0.8392
0.0078	28.0	60116	1.8630	0.8354	0.8352	0.8354	0.8353
0.0058	29.0	62263	1.8751	0.8339	0.8343	0.8339	0.8341
0.0028	30.0	64410	1.8839	0.8365	0.8373	0.8365	0.8368

Framework versions

Transformers 4.13.0.dev0
Pytorch 1.11.0+cu102
Datasets 1.15.2.dev0
Tokenizers 0.10.1

Task: Text Classification
Library: transformers
Type: distilbert
License: apache-2.0
Created: Sep 20, 2022
Updated: Sep 20, 2022