categorization-finetuned-20220721-164940-distilled-20220811-013354

This model is a fine-tuned version of carted-nlp/categorization-finetuned-20220721-164940 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0645
• Accuracy: 0.8776
• F1: 0.8768

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: 96
• seed: 314
• gradient_accumulation_steps: 4
• total_train_batch_size: 256
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 1500
• num_epochs: 30.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.2702	0.56	2500	0.1290	0.7832	0.7783
0.1246	1.12	5000	0.1047	0.8169	0.8137
0.1066	1.69	7500	0.0945	0.8301	0.8276
0.0975	2.25	10000	0.0888	0.8386	0.8367
0.0917	2.81	12500	0.0849	0.8445	0.8428
0.0865	3.37	15000	0.0818	0.8496	0.8484
0.0835	3.94	17500	0.0796	0.8526	0.8509
0.08	4.5	20000	0.0777	0.8552	0.8542
0.0778	5.06	22500	0.0763	0.8580	0.8567
0.0753	5.62	25000	0.0744	0.8604	0.8592
0.0739	6.19	27500	0.0738	0.8614	0.8603
0.0716	6.75	30000	0.0729	0.8630	0.8620
0.0701	7.31	32500	0.0719	0.8645	0.8638
0.0689	7.87	35000	0.0708	0.8657	0.8647
0.067	8.43	37500	0.0705	0.8671	0.8660
0.0669	9.0	40000	0.0699	0.8681	0.8674
0.0647	9.56	42500	0.0697	0.8683	0.8673
0.0641	10.12	45000	0.0693	0.8691	0.8681
0.063	10.68	47500	0.0685	0.8702	0.8694
0.0618	11.25	50000	0.0681	0.8709	0.8701
0.0614	11.81	52500	0.0675	0.8720	0.8712
0.0601	12.37	55000	0.0678	0.8724	0.8713
0.0598	12.93	57500	0.0670	0.8732	0.8725
0.0584	13.5	60000	0.0670	0.8732	0.8723
0.0584	14.06	62500	0.0665	0.8740	0.8732
0.0572	14.62	65000	0.0665	0.8744	0.8734
0.0567	15.18	67500	0.0661	0.8753	0.8745
0.0561	15.74	70000	0.0660	0.8756	0.8750
0.0554	16.31	72500	0.0661	0.8759	0.8751
0.0552	16.87	75000	0.0656	0.8755	0.8749
0.0544	17.43	77500	0.0657	0.8762	0.8754
0.0544	17.99	80000	0.0654	0.8767	0.8760
0.0534	18.56	82500	0.0654	0.8767	0.8759
0.0534	19.12	85000	0.0653	0.8773	0.8767
0.0528	19.68	87500	0.0649	0.8775	0.8768
0.0525	20.24	90000	0.0651	0.8776	0.8769
0.0523	20.81	92500	0.0649	0.8775	0.8768
0.0517	21.37	95000	0.0648	0.8782	0.8775
0.0516	21.93	97500	0.0648	0.8783	0.8776
0.0511	22.49	100000	0.0648	0.8781	0.8774
0.0511	23.05	102500	0.0647	0.8783	0.8776
0.0508	23.62	105000	0.0647	0.8785	0.8778
0.0505	24.18	107500	0.0647	0.8785	0.8777
0.0505	24.74	110000	0.0646	0.8788	0.8781
0.0503	25.3	112500	0.0646	0.8786	0.8779
0.0502	25.87	115000	0.0646	0.8789	0.8782
0.0501	26.43	117500	0.0646	0.8788	0.8781
0.0501	26.99	120000	0.0645	0.8791	0.8784
0.05	27.55	122500	0.0646	0.8790	0.8783
0.0497	28.12	125000	0.0645	0.8792	0.8785
0.0499	28.68	127500	0.0645	0.8791	0.8784
0.0499	29.24	130000	0.0645	0.8792	0.8785
0.0497	29.8	132500	0.0645	0.8791	0.8784

Framework versions

• Transformers 4.17.0
• Pytorch 1.11.0+cu113
• Datasets 2.3.2
• Tokenizers 0.11.6