videberta-sentiment-analysis

shayonhuggingface/videberta-sentiment-analysis

videberta-sentiment-analysis is a text classification model from shayonhuggingface. Use it when you need a label for a piece of text. It is set up for transformers.

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

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At a glance

Task: Text Classification
Library: transformers
Model type: deberta-v2
Access: Public
Created: Jul 19, 2023
Updated: Jul 19, 2023
SHA: c7a80c9b

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Base models

Fsoft-AIC/videberta-xsmall

Datasets

vietnamese_students_feedback

Model card

From the Hugging Face model README

videberta-sentiment-analysis

This model is a fine-tuned version of Fsoft-AIC/videberta-xsmall on the vietnamese_students_feedback dataset. It achieves the following results on the evaluation set:

Loss: 0.2787
Accuracy: 0.9470
Precision: 0.9481
Recall: 0.9528
F1: 0.9504

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: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.6152	0.58	100	0.4777	0.8007	0.8580	0.7503	0.8005
0.408	1.16	200	0.3241	0.8669	0.8943	0.8509	0.8721
0.3268	1.74	300	0.2726	0.8954	0.8837	0.9255	0.9041
0.2654	2.33	400	0.2296	0.9199	0.9212	0.9292	0.9252
0.253	2.91	500	0.2088	0.9159	0.9206	0.9217	0.9212
0.2014	3.49	600	0.2318	0.9172	0.9028	0.9466	0.9242
0.1939	4.07	700	0.2131	0.9212	0.9224	0.9304	0.9264
0.1698	4.65	800	0.2005	0.9311	0.9499	0.9193	0.9343
0.1822	5.23	900	0.2249	0.9245	0.9089	0.9540	0.9309
0.1441	5.81	1000	0.2038	0.9311	0.9311	0.9404	0.9357
0.1403	6.4	1100	0.2044	0.9338	0.9315	0.9453	0.9383
0.1377	6.98	1200	0.1991	0.9417	0.9567	0.9329	0.9447
0.1191	7.56	1300	0.2955	0.9119	0.8792	0.9677	0.9213
0.1227	8.14	1400	0.2362	0.9318	0.9199	0.9553	0.9372
0.1023	8.72	1500	0.2221	0.9358	0.9286	0.9528	0.9405
0.1049	9.3	1600	0.1940	0.9424	0.9454	0.9466	0.9460
0.1002	9.88	1700	0.1949	0.9404	0.9649	0.9217	0.9428
0.0946	10.47	1800	0.2232	0.9404	0.9625	0.9242	0.9430
0.0911	11.05	1900	0.2016	0.9457	0.9641	0.9329	0.9482
0.0818	11.63	2000	0.2636	0.9311	0.9128	0.9627	0.9371
0.0889	12.21	2100	0.2279	0.9450	0.9524	0.9441	0.9482
0.0668	12.79	2200	0.2460	0.9411	0.9409	0.9491	0.9450
0.0635	13.37	2300	0.2764	0.9424	0.9465	0.9453	0.9459
0.072	13.95	2400	0.2519	0.9437	0.9390	0.9565	0.9477
0.0697	14.53	2500	0.2705	0.9404	0.9408	0.9478	0.9443
0.0602	15.12	2600	0.2686	0.9450	0.9513	0.9453	0.9483
0.065	15.7	2700	0.2629	0.9450	0.9501	0.9466	0.9484
0.0628	16.28	2800	0.2644	0.9450	0.9547	0.9416	0.9481
0.0505	16.86	2900	0.2704	0.9424	0.9400	0.9528	0.9463
0.0471	17.44	3000	0.2787	0.9470	0.9481	0.9528	0.9504
0.0568	18.02	3100	0.2766	0.9450	0.9424	0.9553	0.9488
0.0523	18.6	3200	0.2659	0.9424	0.9421	0.9503	0.9462
0.0487	19.19	3300	0.3091	0.9338	0.9222	0.9565	0.9390
0.0529	19.77	3400	0.3575	0.9272	0.9045	0.9652	0.9339
0.0484	20.35	3500	0.3228	0.9358	0.9214	0.9615	0.9410
0.0456	20.93	3600	0.2694	0.9437	0.9412	0.9540	0.9476
0.0424	21.51	3700	0.2793	0.9404	0.9376	0.9516	0.9445
0.045	22.09	3800	0.2953	0.9417	0.9356	0.9565	0.9459
0.0395	22.67	3900	0.2840	0.9417	0.9377	0.9540	0.9458
0.0418	23.26	4000	0.3527	0.9305	0.9108	0.9640	0.9366

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3

Task: Text Classification
Library: transformers
Type: deberta-v2
Created: Jul 19, 2023
Updated: Jul 19, 2023