Sentiment140_BERT_5E

Etelis/Sentiment140_BERT_5E

Sentiment140_BERT_5E is a text classification model from Etelis. 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 7, 2022
Updated: Nov 7, 2022
SHA: 7b605db2

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Datasets

sentiment140

Model card

From the Hugging Face model README

Sentiment140_BERT_5E

This model is a fine-tuned version of bert-base-cased on the sentiment140 dataset. It achieves the following results on the evaluation set:

Loss: 0.7061
Accuracy: 0.82

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: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6882	0.08	50	0.6047	0.7
0.6223	0.16	100	0.5137	0.8067
0.5463	0.24	150	0.4573	0.8067
0.4922	0.32	200	0.4790	0.8
0.4821	0.4	250	0.4207	0.8267
0.4985	0.48	300	0.4267	0.8067
0.4455	0.56	350	0.4301	0.8133
0.469	0.64	400	0.4294	0.82
0.4906	0.72	450	0.4059	0.8067
0.4006	0.8	500	0.4181	0.8133
0.445	0.88	550	0.3948	0.8267
0.4302	0.96	600	0.3976	0.84
0.4442	1.04	650	0.3887	0.8533
0.3424	1.12	700	0.4119	0.8267
0.3589	1.2	750	0.4083	0.8533
0.3737	1.28	800	0.4253	0.8333
0.334	1.36	850	0.4147	0.86
0.3637	1.44	900	0.3926	0.8533
0.3388	1.52	950	0.4084	0.8267
0.3375	1.6	1000	0.4132	0.8467
0.3725	1.68	1050	0.3965	0.8467
0.3649	1.76	1100	0.3956	0.8333
0.3799	1.84	1150	0.3923	0.8333
0.3695	1.92	1200	0.4266	0.84
0.3233	2.0	1250	0.4225	0.8333
0.2313	2.08	1300	0.4672	0.8333
0.231	2.16	1350	0.5212	0.8133
0.2526	2.24	1400	0.5392	0.8067
0.2721	2.32	1450	0.4895	0.82
0.2141	2.4	1500	0.5258	0.8133
0.2658	2.48	1550	0.5046	0.8267
0.2386	2.56	1600	0.4873	0.8267
0.2493	2.64	1650	0.4950	0.8333
0.2692	2.72	1700	0.5080	0.8267
0.2226	2.8	1750	0.5016	0.8467
0.2522	2.88	1800	0.5068	0.8267
0.2556	2.96	1850	0.4937	0.8267
0.2311	3.04	1900	0.5103	0.8267
0.1703	3.12	1950	0.5680	0.82
0.1744	3.2	2000	0.5501	0.82
0.1667	3.28	2050	0.6142	0.82
0.1863	3.36	2100	0.6355	0.82
0.2543	3.44	2150	0.6000	0.8133
0.1565	3.52	2200	0.6618	0.8267
0.1531	3.6	2250	0.6595	0.8133
0.1915	3.68	2300	0.6647	0.8267
0.1601	3.76	2350	0.6729	0.8267
0.176	3.84	2400	0.6699	0.82
0.1815	3.92	2450	0.6819	0.8067
0.1987	4.0	2500	0.6543	0.8333
0.1236	4.08	2550	0.6686	0.8333
0.1599	4.16	2600	0.6583	0.8267
0.1256	4.24	2650	0.6871	0.8267
0.1291	4.32	2700	0.6855	0.82
0.1198	4.4	2750	0.6901	0.82
0.1245	4.48	2800	0.7152	0.8267
0.1784	4.56	2850	0.7053	0.82
0.1705	4.64	2900	0.7016	0.82
0.1265	4.72	2950	0.7013	0.82
0.1192	4.8	3000	0.7084	0.82
0.174	4.88	3050	0.7062	0.82
0.1328	4.96	3100	0.7061	0.82

Framework versions

Transformers 4.24.0
Pytorch 1.12.1+cu113
Datasets 2.6.1
Tokenizers 0.13.1

Task: Text Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Nov 7, 2022
Updated: Nov 7, 2022