rtm_DistilBERT_5E

Etelis/rtm_DistilBERT_5E

rtm_DistilBERT_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: distilbert
Access: Public
Created: Nov 29, 2022
Updated: Nov 29, 2022
SHA: 259851d5

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Datasets

rotten_tomatoes

Model card

From the Hugging Face model README

rtm_DistilBERT_5E

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

Loss: 0.6835
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
0.6822	0.09	50	0.6391	0.76
0.5531	0.19	100	0.4684	0.7667
0.4546	0.28	150	0.4479	0.7733
0.4495	0.37	200	0.3953	0.8067
0.4239	0.47	250	0.4211	0.7933
0.3951	0.56	300	0.4126	0.7933
0.3861	0.66	350	0.3950	0.7933
0.4108	0.75	400	0.4091	0.82
0.3778	0.84	450	0.4107	0.7933
0.3627	0.94	500	0.4203	0.7933
0.3648	1.03	550	0.4190	0.8
0.2899	1.12	600	0.4436	0.8
0.2637	1.22	650	0.4504	0.82
0.2885	1.31	700	0.4406	0.82
0.3226	1.4	750	0.4398	0.8333
0.3147	1.5	800	0.4239	0.82
0.2937	1.59	850	0.4227	0.8133
0.3149	1.69	900	0.3791	0.82
0.3227	1.78	950	0.3888	0.8133
0.2727	1.87	1000	0.4215	0.82
0.2722	1.97	1050	0.4099	0.8333
0.1908	2.06	1100	0.4595	0.82
0.2276	2.15	1150	0.4572	0.84
0.2239	2.25	1200	0.4545	0.8333
0.1986	2.34	1250	0.4895	0.82
0.2388	2.43	1300	0.4352	0.86
0.1901	2.53	1350	0.4806	0.84
0.2227	2.62	1400	0.5473	0.8067
0.2221	2.72	1450	0.5010	0.84
0.1955	2.81	1500	0.5315	0.8267
0.2114	2.9	1550	0.5410	0.8133
0.1827	3.0	1600	0.5721	0.8133
0.1527	3.09	1650	0.5616	0.8133
0.1464	3.18	1700	0.5935	0.8067
0.135	3.28	1750	0.6145	0.82
0.1668	3.37	1800	0.6901	0.8067
0.1702	3.46	1850	0.6067	0.8133
0.1738	3.56	1900	0.5981	0.82
0.1506	3.65	1950	0.6073	0.8267
0.1584	3.75	2000	0.6549	0.8133
0.1698	3.84	2050	0.6660	0.8267
0.1626	3.93	2100	0.6645	0.8267
0.1483	4.03	2150	0.6497	0.82
0.1342	4.12	2200	0.6643	0.82
0.1064	4.21	2250	0.6775	0.82
0.1302	4.31	2300	0.6876	0.82
0.1847	4.4	2350	0.6821	0.8133
0.1055	4.49	2400	0.6928	0.8133
0.1372	4.59	2450	0.6877	0.8133
0.131	4.68	2500	0.6769	0.8267
0.1242	4.78	2550	0.6769	0.8267
0.1289	4.87	2600	0.6810	0.82
0.1488	4.96	2650	0.6835	0.82

Framework versions

Transformers 4.24.0
Pytorch 1.13.0
Datasets 2.7.1
Tokenizers 0.13.2

Task: Text Classification
Library: transformers
Type: distilbert
License: apache-2.0
Created: Nov 29, 2022
Updated: Nov 29, 2022