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CureLink/curelink-biomed-nli-v2

sourceHugging Faceupdated 6mo agoView on Hugging Face

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curelink-biomed-nli-v2

This model is a fine-tuned version of CureLink/curelink-biomed-nli-v2 on the None dataset. It achieves the following results on the evaluation set:

—Loss: 1.4186
—Accuracy: 0.5950
—F1 Macro: 0.7088

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: 5e-06
—trainbatchsize: 1
—evalbatchsize: 1
—seed: 42
—gradientaccumulationsteps: 8
—totaltrainbatch_size: 8
—optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
—lrschedulertype: linear
—lrschedulerwarmup_steps: 50
—num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro
3.5224	0.3264	450	0.9331	0.6052	0.7123
3.9077	0.6529	900	0.9590	0.5969	0.7002
2.9425	0.9793	1350	0.9358	0.6042	0.7145
2.7302	1.3054	1800	1.2142	0.5921	0.7003
2.8113	1.6318	2250	1.1171	0.6005	0.7108
3.9271	1.9583	2700	1.1055	0.6031	0.7138
1.7452	2.2844	3150	1.4016	0.6031	0.7131
2.0138	2.6108	3600	1.5166	0.5976	0.7080
2.5576	2.9373	4050	1.3747	0.6092	0.7172
2.3544	3.2633	4500	1.8074	0.6023	0.7107
2.2960	3.5898	4950	1.7112	0.5947	0.7070
1.9046	3.9162	5400	1.8980	0.5958	0.7078
2.0248	4.2423	5850	2.0792	0.5991	0.7109
3.1171	4.5687	6300	2.2402	0.5950	0.7080
3.7094	4.8952	6750	2.1351	0.5914	0.7054
3.0557	5.2213	7200	1.5186	0.5940	0.7076
2.5539	5.5477	7650	1.2877	0.5980	0.7109
3.0154	5.8741	8100	1.2530	0.5940	0.7079
2.7087	6.2002	8550	1.2919	0.5954	0.7090
3.2482	6.5267	9000	1.3340	0.5972	0.7103
4.0220	6.8531	9450	1.2812	0.5983	0.7109
2.3237	7.1792	9900	1.3556	0.5987	0.7108
2.7216	7.5056	10350	1.3533	0.5980	0.7107
2.8498	7.8321	10800	1.3957	0.5972	0.7104
1.3549	8.1581	11250	1.4166	0.5950	0.7088
3.4563	8.4846	11700	1.4216	0.5940	0.7080
2.2327	8.8110	12150	1.3692	0.5950	0.7088
1.9137	9.1371	12600	1.3708	0.5925	0.7069
2.2507	9.4635	13050	1.4109	0.5980	0.7109
2.3204	9.7900	13500	1.4172	0.5947	0.7085
2.0721	10.0	13790	1.4186	0.5950	0.7088

Framework versions

—Transformers 5.4.0
—Pytorch 2.11.0
—Datasets 4.8.4
—Tokenizers 0.22.2