hist-l2_tenQ_finetune-itemseg_v8

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 32.7778
• Accuracy: 0.9686
• Macro F1: 0.9018

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: 0.0001
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 2995
• training_steps: 29950

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Macro F1
1.2685	0.0100	299	34.9748	0.6363	0.1697
1.026	1.0099	598	34.6751	0.6966	0.1965
0.8411	2.0099	897	34.4601	0.7365	0.2728
0.78	3.0099	1196	34.3536	0.7596	0.3707
0.7394	4.0098	1495	34.3028	0.7663	0.4627
0.653	5.0098	1794	34.2250	0.7856	0.5101
0.6215	6.0098	2093	34.1354	0.8090	0.5544
0.5732	7.0097	2392	34.0536	0.8292	0.5778
0.5133	8.0097	2691	33.9659	0.8470	0.6061
0.4579	9.0097	2990	33.9220	0.8545	0.6128
0.3896	10.0096	3289	33.8966	0.8537	0.6237
0.3351	11.0096	3588	33.8490	0.8648	0.6321
0.3423	12.0096	3887	33.7544	0.8908	0.6752
0.2804	13.0095	4186	33.7103	0.8972	0.6933
0.2438	14.0095	4485	33.6696	0.9030	0.7080
0.4695	15.0095	4784	33.6596	0.9002	0.7192
0.4444	16.0094	5083	33.6422	0.9031	0.7203
0.4388	17.0094	5382	33.5571	0.9228	0.7382
0.4175	18.0094	5681	33.5144	0.9273	0.7515
0.3967	19.0093	5980	33.4644	0.9338	0.7629
0.229	20.0093	6279	33.4337	0.9351	0.7675
0.2144	21.0093	6578	33.4054	0.9376	0.7568
0.1572	22.0092	6877	33.3489	0.9457	0.7761
0.1658	23.0092	7176	33.3164	0.9478	0.7820
0.1415	24.0092	7475	33.2885	0.9474	0.7835
0.0699	25.0091	7774	33.2516	0.9518	0.8032
0.0705	26.0091	8073	33.2352	0.9499	0.7887
0.0628	27.0091	8372	33.2109	0.9507	0.7950
0.0607	28.0090	8671	33.1872	0.9504	0.7981
0.0616	29.0090	8970	33.1532	0.9545	0.8082
0.0564	30.0090	9269	33.1544	0.9522	0.8191
0.0554	31.0089	9568	33.1144	0.9570	0.8193
0.0531	32.0089	9867	33.1014	0.9571	0.8239
0.0529	33.0089	10166	33.0896	0.9563	0.8263
0.0482	34.0088	10465	33.0666	0.9588	0.8342
0.0854	35.0088	10764	33.0391	0.9571	0.8405
0.0674	36.0088	11063	33.0381	0.9577	0.8404
0.0676	37.0087	11362	33.0198	0.9582	0.8454
0.0702	38.0087	11661	32.9992	0.9587	0.8485
0.0607	39.0087	11960	33.0109	0.9548	0.8464
0.0388	40.0086	12259	32.9839	0.9548	0.8437
0.0372	41.0086	12558	32.9556	0.9601	0.8460
0.0358	42.0086	12857	32.9679	0.9538	0.8452
0.0333	43.0085	13156	32.9179	0.9633	0.8592
0.0334	44.0085	13455	32.9477	0.9555	0.8592
0.0315	45.0085	13754	32.9154	0.9620	0.8674
0.0303	46.0084	14053	32.8858	0.9622	0.8639
0.028	47.0084	14352	32.8813	0.9656	0.8757
0.0277	48.0084	14651	32.8685	0.9670	0.8783
0.0252	49.0083	14950	32.8750	0.9634	0.8849
0.0267	50.0083	15249	32.8726	0.9630	0.8778
0.0273	51.0083	15548	32.8475	0.9661	0.8863
0.0275	52.0082	15847	32.8484	0.9632	0.8799
0.0241	53.0082	16146	32.8455	0.9664	0.8883
0.0222	54.0082	16445	32.8335	0.9666	0.8871
0.0271	55.0081	16744	32.8369	0.9644	0.8856
0.0253	56.0081	17043	32.8267	0.9660	0.8837
0.0238	57.0081	17342	32.8170	0.9678	0.8868
0.0239	58.0080	17641	32.8245	0.9646	0.8849
0.0211	59.0080	17940	32.8155	0.9671	0.8902
0.0178	60.0080	18239	32.8045	0.9682	0.8886
0.0201	61.0079	18538	32.7928	0.9684	0.8839
0.0295	62.0079	18837	32.8005	0.9666	0.8974
0.0189	63.0079	19136	32.7990	0.9667	0.8865
0.0196	64.0078	19435	32.7801	0.9708	0.9003
0.0165	65.0078	19734	32.7962	0.9677	0.8973
0.0161	66.0078	20033	32.7786	0.9691	0.8999
0.0149	67.0077	20332	32.7906	0.9681	0.8965
0.0142	68.0077	20631	32.7710	0.9690	0.8988
0.0145	69.0077	20930	32.7792	0.9693	0.8991
0.0155	70.0076	21229	32.7751	0.9689	0.8979
0.0138	71.0076	21528	32.7740	0.9693	0.8998
0.0131	72.0076	21827	32.7698	0.9692	0.9001
0.0139	73.0075	22126	32.7762	0.9692	0.9013
0.0137	74.0075	22425	32.7778	0.9686	0.9018
0.0134	75.0075	22724	32.7762	0.9686	0.9008
0.0136	76.0074	23023	32.7738	0.9690	0.8985
0.0123	77.0074	23322	32.7787	0.9689	0.8980
0.0129	78.0074	23621	32.7760	0.9686	0.8988
0.0115	79.0073	23920	32.7776	0.9690	0.8990
0.0128	80.0073	24219	32.7680	0.9696	0.8990
0.0146	81.0073	24518	32.7882	0.9681	0.8983
0.0134	82.0072	24817	32.7767	0.9684	0.8979
0.0133	83.0072	25116	32.7761	0.9696	0.8986
0.0123	84.0072	25415	32.7729	0.9695	0.9013
0.0119	85.0071	25714	32.7716	0.9698	0.8987
0.012	86.0071	26013	32.7734	0.9695	0.9003
0.0115	87.0071	26312	32.7738	0.9695	0.8983
0.0118	88.0070	26611	32.7687	0.9703	0.8985
0.0116	89.0070	26910	32.7742	0.9699	0.8993

Framework versions

• Transformers 4.46.0
• Pytorch 2.3.1+cu121
• Datasets 2.20.0
• Tokenizers 0.20.1