learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 200
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro
1.4013	0.0197	450	0.6913	0.512	0.3867
1.4021	0.0394	900	0.6921	0.5155	0.4002
1.3830	0.0591	1350	0.6825	0.529	0.4880
1.3802	0.0788	1800	0.6896	0.5125	0.3638
1.3801	0.0985	2250	0.6949	0.524	0.4536
1.3764	0.1181	2700	0.6807	0.5565	0.5452
1.3699	0.1378	3150	0.6808	0.541	0.5297
1.3597	0.1575	3600	0.6857	0.559	0.5491
1.3611	0.1772	4050	0.6965	0.5385	0.5038
1.3618	0.1969	4500	0.6845	0.5495	0.5495
1.3565	0.2166	4950	0.6977	0.545	0.5384
1.3623	0.2363	5400	0.6923	0.557	0.5564
1.3666	0.2560	5850	0.6963	0.546	0.4942
1.3635	0.2757	6300	0.6821	0.54	0.5134
1.3663	0.2954	6750	0.6891	0.54	0.5388
1.3604	0.3151	7200	0.6851	0.548	0.5358
1.3490	0.3347	7650	0.6787	0.5475	0.5140
1.3495	0.3544	8100	0.6740	0.5535	0.5509
1.3668	0.3741	8550	0.6746	0.5645	0.5590
1.3608	0.3938	9000	0.6740	0.5585	0.5541
1.3476	0.4135	9450	0.6718	0.5645	0.5638
1.3520	0.4332	9900	0.6774	0.565	0.5396
1.3358	0.4529	10350	0.6747	0.5655	0.5584
1.3289	0.4726	10800	0.6844	0.5515	0.5167
1.3443	0.4923	11250	0.6776	0.5675	0.5605
1.3358	0.5120	11700	0.6759	0.567	0.5586
1.3408	0.5317	12150	0.6734	0.566	0.5545
1.3324	0.5513	12600	0.6780	0.5705	0.5702
1.3340	0.5710	13050	0.6758	0.568	0.5635
1.3260	0.5907	13500	0.6750	0.5735	0.5507
1.3267	0.6104	13950	0.6773	0.5675	0.5577
1.3259	0.6301	14400	0.6776	0.5795	0.5753
1.3298	0.6498	14850	0.6784	0.574	0.5740
1.3321	0.6695	15300	0.6730	0.569	0.5606
1.3346	0.6892	15750	0.6823	0.575	0.5711
1.3228	0.7089	16200	0.6826	0.5855	0.5854
1.3420	0.7286	16650	0.6690	0.574	0.5721
1.3064	0.7483	17100	0.6723	0.5745	0.5682
1.3286	0.7680	17550	0.6723	0.5795	0.5789
1.3127	0.7876	18000	0.6682	0.587	0.5863
1.3056	0.8073	18450	0.6796	0.572	0.5525
1.3176	0.8270	18900	0.6671	0.577	0.5718
1.3316	0.8467	19350	0.6678	0.585	0.5835
1.3295	0.8664	19800	0.6735	0.5855	0.5831
1.3147	0.8861	20250	0.6711	0.5805	0.5771
1.3059	0.9058	20700	0.6854	0.57	0.5570
1.3157	0.9255	21150	0.6773	0.5705	0.5702
1.2997	0.9452	21600	0.6769	0.5735	0.5668
1.3019	0.9649	22050	0.6693	0.5605	0.5378
1.2910	0.9846	22500	0.6738	0.571	0.5542
1.3125	1.0042	22950	0.6987	0.572	0.5664
1.2218	1.0239	23400	0.6946	0.5915	0.5896
1.2406	1.0436	23850	0.6924	0.565	0.5414
1.2474	1.0633	24300	0.6766	0.5745	0.5707
1.2552	1.0830	24750	0.6939	0.577	0.5762
1.2506	1.1027	25200	0.6828	0.588	0.5860
1.2579	1.1224	25650	0.6847	0.5735	0.5709
1.2332	1.1421	26100	0.6929	0.5785	0.5773
1.2346	1.1618	26550	0.6852	0.5685	0.5630
1.2434	1.1815	27000	0.7072	0.56	0.5530
1.2307	1.2012	27450	0.7199	0.5695	0.5627
1.2239	1.2208	27900	0.6923	0.5885	0.5885
1.2393	1.2405	28350	0.6831	0.578	0.5780
1.2297	1.2602	28800	0.6906	0.5705	0.5705
1.2563	1.2799	29250	0.6728	0.579	0.5762
1.2396	1.2996	29700	0.7035	0.573	0.5684
1.2519	1.3193	30150	0.6819	0.57	0.5658
1.2073	1.3390	30600	0.7145	0.569	0.5684
1.2384	1.3587	31050	0.6958	0.5735	0.5734
1.2392	1.3784	31500	0.6810	0.5765	0.5751
1.2388	1.3981	31950	0.6941	0.5705	0.5685
1.2235	1.4178	32400	0.6849	0.5765	0.5760
1.2361	1.4374	32850	0.6978	0.5745	0.5736
1.2308	1.4571	33300	0.7015	0.574	0.5740
1.2059	1.4768	33750	0.6852	0.574	0.5740
1.1956	1.4965	34200	0.7094	0.5735	0.5721
1.2251	1.5162	34650	0.6939	0.5815	0.5815
1.2196	1.5359	35100	0.6846	0.5805	0.5801
1.2165	1.5556	35550	0.6988	0.572	0.5649
1.2283	1.5753	36000	0.7010	0.573	0.5645
1.2201	1.5950	36450	0.7057	0.577	0.5761
1.2032	1.6147	36900	0.7044	0.575	0.5695
1.2065	1.6344	37350	0.6869	0.5805	0.5800
1.2245	1.6540	37800	0.6804	0.5835	0.5823
1.2182	1.6737	38250	0.7069	0.582	0.5820
1.2257	1.6934	38700	0.6862	0.5805	0.5778
1.2215	1.7131	39150	0.6889	0.582	0.5797
1.2082	1.7328	39600	0.6760	0.588	0.5874
1.2043	1.7525	40050	0.6930	0.5975	0.5959
1.2388	1.7722	40500	0.7120	0.598	0.5977
1.2131	1.7919	40950	0.7042	0.5885	0.5872
1.2426	1.8116	41400	0.6788	0.591	0.5909
1.2001	1.8313	41850	0.6789	0.59	0.5900
1.2187	1.8510	42300	0.7149	0.5885	0.5846
1.2016	1.8707	42750	0.6900	0.5865	0.5852
1.2196	1.8903	43200	0.6806	0.591	0.5901
1.2233	1.9100	43650	0.6859	0.5885	0.5882
1.2273	1.9297	44100	0.6905	0.576	0.5653
1.2093	1.9494	44550	0.6886	0.5865	0.5865
1.2068	1.9691	45000	0.6786	0.593	0.5929
1.1979	1.9888	45450	0.6849	0.591	0.5905
1.1446	2.0085	45900	0.7037	0.578	0.5760
1.1307	2.0282	46350	0.7235	0.5795	0.5766
1.1280	2.0479	46800	0.7243	0.581	0.5804
1.1420	2.0676	47250	0.7434	0.5925	0.5924
1.1258	2.0873	47700	0.7300	0.5845	0.5844
1.1340	2.1069	48150	0.7020	0.585	0.5842
1.1287	2.1266	48600	0.7338	0.5855	0.5854
1.0998	2.1463	49050	0.6928	0.59	0.5897
1.1382	2.1660	49500	0.7380	0.586	0.5857
1.1250	2.1857	49950	0.6958	0.579	0.5746
1.1001	2.2054	50400	0.7172	0.583	0.5830
1.1256	2.2251	50850	0.7432	0.5905	0.5899
1.1356	2.2448	51300	0.7219	0.584	0.5840
1.0950	2.2645	51750	0.7137	0.592	0.5917
1.1083	2.2842	52200	0.7092	0.591	0.5905
1.1277	2.3039	52650	0.7213	0.59	0.5899
1.1305	2.3235	53100	0.7245	0.5855	0.5837
1.1104	2.3432	53550	0.7129	0.5845	0.5837
1.1229	2.3629	54000	0.7215	0.602	0.6020
1.1152	2.3826	54450	0.7186	0.6025	0.6023
1.1077	2.4023	54900	0.7341	0.592	0.5913
1.1423	2.4220	55350	0.6966	0.583	0.5779
1.1338	2.4417	55800	0.6997	0.5975	0.5973
1.1200	2.4614	56250	0.7254	0.5855	0.5855
1.1264	2.4811	56700	0.7254	0.5955	0.5953
1.1033	2.5008	57150	0.7139	0.5995	0.5987
1.1153	2.5205	57600	0.7248	0.599	0.5990
1.1129	2.5401	58050	0.7027	0.595	0.5950
1.0966	2.5598	58500	0.7165	0.588	0.5846
1.1004	2.5795	58950	0.7342	0.5945	0.5943
1.1360	2.5992	59400	0.7105	0.5865	0.5830
1.1338	2.6189	59850	0.7212	0.5955	0.5953
1.1236	2.6386	60300	0.7203	0.5935	0.5925
1.1021	2.6583	60750	0.7234	0.6015	0.5986
1.0803	2.6780	61200	0.7392	0.592	0.5920
1.0910	2.6977	61650	0.7340	0.599	0.5972
1.1189	2.7174	62100	0.7129	0.5965	0.5958
1.1060	2.7371	62550	0.7302	0.589	0.5882
1.1250	2.7567	63000	0.7127	0.5885	0.5870
1.1084	2.7764	63450	0.7207	0.589	0.5877
1.1150	2.7961	63900	0.7070	0.597	0.5968
1.1078	2.8158	64350	0.7093	0.59	0.5869
1.1003	2.8355	64800	0.7057	0.587	0.5833
1.1118	2.8552	65250	0.7067	0.5975	0.5974
1.0986	2.8749	65700	0.7140	0.583	0.5792
1.1129	2.8946	66150	0.7016	0.583	0.5817
1.1327	2.9143	66600	0.7219	0.5935	0.5931
1.1400	2.9340	67050	0.7099	0.5985	0.5980
1.1078	2.9537	67500	0.7259	0.5915	0.5888
1.1146	2.9734	67950	0.7136	0.605	0.6050
1.1257	2.9930	68400	0.6999	0.59	0.5862
1.0734	3.0127	68850	0.7354	0.5945	0.5945
1.0655	3.0324	69300	0.7652	0.587	0.5839
1.0712	3.0521	69750	0.7638	0.5845	0.5838
1.0342	3.0718	70200	0.7486	0.577	0.5728
1.0580	3.0915	70650	0.7477	0.5915	0.5893
1.0558	3.1112	71100	0.7372	0.5835	0.5814
1.0249	3.1309	71550	0.7454	0.5885	0.5883
1.0697	3.1506	72000	0.7387	0.588	0.5866
1.0378	3.1703	72450	0.7541	0.597	0.5958
1.0626	3.1900	72900	0.7383	0.597	0.5970
1.0753	3.2096	73350	0.7505	0.5985	0.5982
1.0402	3.2293	73800	0.7364	0.5875	0.5864
1.0501	3.2490	74250	0.7274	0.598	0.5976
1.0303	3.2687	74700	0.7395	0.5965	0.5960
1.0466	3.2884	75150	0.7597	0.5855	0.5851
1.0489	3.3081	75600	0.7396	0.587	0.5854
1.0597	3.3278	76050	0.7276	0.59	0.5891
1.0350	3.3475	76500	0.7437	0.5935	0.5932
1.0581	3.3672	76950	0.7331	0.594	0.5935
1.0265	3.3869	77400	0.7423	0.59	0.5891
1.0074	3.4066	77850	0.7391	0.596	0.5958
1.0323	3.4262	78300	0.7377	0.591	0.5892
1.0594	3.4459	78750	0.7473	0.5985	0.5972
1.0669	3.4656	79200	0.7441	0.596	0.5952
1.0356	3.4853	79650	0.7475	0.601	0.6006
1.0523	3.5050	80100	0.7411	0.597	0.5970
1.0560	3.5247	80550	0.7397	0.594	0.5938
1.0784	3.5444	81000	0.7379	0.591	0.5905
1.0160	3.5641	81450	0.7512	0.587	0.5857
1.0266	3.5838	81900	0.7407	0.585	0.5842
1.0174	3.6035	82350	0.7444	0.5885	0.5882
1.0506	3.6232	82800	0.7338	0.589	0.5887
1.0596	3.6428	83250	0.7394	0.588	0.5870
1.0699	3.6625	83700	0.7264	0.5875	0.5870
1.0440	3.6822	84150	0.7375	0.592	0.5918
1.0528	3.7019	84600	0.7422	0.598	0.5980
1.0176	3.7216	85050	0.7474	0.5865	0.5860
1.0545	3.7413	85500	0.7503	0.588	0.5876
1.0316	3.7610	85950	0.7445	0.591	0.5908
1.0425	3.7807	86400	0.7385	0.584	0.5824
1.0422	3.8004	86850	0.7498	0.5835	0.5821
1.0473	3.8201	87300	0.7407	0.5865	0.5857
1.0308	3.8398	87750	0.7477	0.5855	0.5839
1.0705	3.8594	88200	0.7425	0.587	0.5857
1.0324	3.8791	88650	0.7486	0.5845	0.5837
1.0356	3.8988	89100	0.7449	0.585	0.5838
1.0254	3.9185	89550	0.7490	0.585	0.5838
1.0381	3.9382	90000	0.7479	0.588	0.5875
1.0099	3.9579	90450	0.7477	0.588	0.5874
1.0303	3.9776	90900	0.7477	0.5865	0.5859
1.0356	3.9973	91350	0.7485	0.5865	0.5859
1.0040	4.0	91412	0.7486	0.5865	0.5859

Framework versions

Transformers 5.5.0
Pytorch 2.11.0
Datasets 4.8.4
Tokenizers 0.22.2

CureLink/curelink-biomed-nli-v4

curelink-biomed-nli-v4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

CureLink/
curelink-biomed-nli-v4