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