allenai-scibert_scivocab_uncased_20241126-033516

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

• Loss: 0.0103
• Accuracy@0.01: 0.9841
• Accuracy@0.02: 0.9880
• Accuracy@0.03: 0.9898
• Accuracy@0.04: 0.9909
• Accuracy@0.05: 0.9916
• Accuracy@0.06: 0.9922
• Accuracy@0.07: 0.9926
• Accuracy@0.08: 0.9930
• Accuracy@0.09: 0.9933
• Accuracy@0.1: 0.9936
• Accuracy@0.11: 0.9938
• Accuracy@0.12: 0.9940
• Accuracy@0.13: 0.9942
• Accuracy@0.14: 0.9944
• Accuracy@0.15: 0.9945
• Accuracy@0.16: 0.9947
• Accuracy@0.17: 0.9948
• Accuracy@0.18: 0.9949
• Accuracy@0.19: 0.9951
• Accuracy@0.2: 0.9952
• Accuracy@0.21: 0.9953
• Accuracy@0.22: 0.9954
• Accuracy@0.23: 0.9955
• Accuracy@0.24: 0.9956
• Accuracy@0.25: 0.9956
• Accuracy@0.26: 0.9957
• Accuracy@0.27: 0.9958
• Accuracy@0.28: 0.9959
• Accuracy@0.29: 0.9959
• Accuracy@0.3: 0.9960
• Accuracy@0.31: 0.9961
• Accuracy@0.32: 0.9961
• Accuracy@0.33: 0.9962
• Accuracy@0.34: 0.9962
• Accuracy@0.35: 0.9963
• Accuracy@0.36: 0.9964
• Accuracy@0.37: 0.9964
• Accuracy@0.38: 0.9965
• Accuracy@0.39: 0.9965
• Accuracy@0.4: 0.9966
• Accuracy@0.41: 0.9966
• Accuracy@0.42: 0.9967
• Accuracy@0.43: 0.9967
• Accuracy@0.44: 0.9968
• Accuracy@0.45: 0.9968
• Accuracy@0.46: 0.9968
• Accuracy@0.47: 0.9969
• Accuracy@0.48: 0.9969
• Accuracy@0.49: 0.9970
• Accuracy@0.5: 0.9970
• Accuracy@0.51: 0.9970
• Accuracy@0.52: 0.9971
• Accuracy@0.53: 0.9971
• Accuracy@0.54: 0.9972
• Accuracy@0.55: 0.9972
• Accuracy@0.56: 0.9972
• Accuracy@0.57: 0.9973
• Accuracy@0.58: 0.9973
• Accuracy@0.59: 0.9974
• Accuracy@0.6: 0.9974
• Accuracy@0.61: 0.9974
• Accuracy@0.62: 0.9975
• Accuracy@0.63: 0.9975
• Accuracy@0.64: 0.9976
• Accuracy@0.65: 0.9976
• Accuracy@0.66: 0.9976
• Accuracy@0.67: 0.9977
• Accuracy@0.68: 0.9977
• Accuracy@0.69: 0.9977
• Accuracy@0.7: 0.9978
• Accuracy@0.71: 0.9978
• Accuracy@0.72: 0.9978
• Accuracy@0.73: 0.9979
• Accuracy@0.74: 0.9979
• Accuracy@0.75: 0.9979
• Accuracy@0.76: 0.9980
• Accuracy@0.77: 0.9980
• Accuracy@0.78: 0.9980
• Accuracy@0.79: 0.9981
• Accuracy@0.8: 0.9981
• Accuracy@0.81: 0.9982
• Accuracy@0.82: 0.9982
• Accuracy@0.83: 0.9982
• Accuracy@0.84: 0.9983
• Accuracy@0.85: 0.9983
• Accuracy@0.86: 0.9984
• Accuracy@0.87: 0.9984
• Accuracy@0.88: 0.9984
• Accuracy@0.89: 0.9985
• Accuracy@0.9: 0.9985
• Accuracy@0.91: 0.9986
• Accuracy@0.92: 0.9986
• Accuracy@0.93: 0.9987
• Accuracy@0.94: 0.9988
• Accuracy@0.95: 0.9988
• Accuracy@0.96: 0.9989
• Accuracy@0.97: 0.9990
• Accuracy@0.98: 0.9991
• Accuracy@0.99: 0.9993

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-06
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy@0.01	Accuracy@0.02	Accuracy@0.03	Accuracy@0.04	Accuracy@0.05	Accuracy@0.06	Accuracy@0.07	Accuracy@0.08	Accuracy@0.09	Accuracy@0.1	Accuracy@0.11	Accuracy@0.12	Accuracy@0.13	Accuracy@0.14	Accuracy@0.15	Accuracy@0.16	Accuracy@0.17	Accuracy@0.18	Accuracy@0.19	Accuracy@0.2	Accuracy@0.21	Accuracy@0.22	Accuracy@0.23	Accuracy@0.24	Accuracy@0.25	Accuracy@0.26	Accuracy@0.27	Accuracy@0.28	Accuracy@0.29	Accuracy@0.3	Accuracy@0.31	Accuracy@0.32	Accuracy@0.33	Accuracy@0.34	Accuracy@0.35	Accuracy@0.36	Accuracy@0.37	Accuracy@0.38	Accuracy@0.39	Accuracy@0.4	Accuracy@0.41	Accuracy@0.42	Accuracy@0.43	Accuracy@0.44	Accuracy@0.45	Accuracy@0.46	Accuracy@0.47	Accuracy@0.48	Accuracy@0.49	Accuracy@0.5	Accuracy@0.51	Accuracy@0.52	Accuracy@0.53	Accuracy@0.54	Accuracy@0.55	Accuracy@0.56	Accuracy@0.57	Accuracy@0.58	Accuracy@0.59	Accuracy@0.6	Accuracy@0.61	Accuracy@0.62	Accuracy@0.63	Accuracy@0.64	Accuracy@0.65	Accuracy@0.66	Accuracy@0.67	Accuracy@0.68	Accuracy@0.69	Accuracy@0.7	Accuracy@0.71	Accuracy@0.72	Accuracy@0.73	Accuracy@0.74	Accuracy@0.75	Accuracy@0.76	Accuracy@0.77	Accuracy@0.78	Accuracy@0.79	Accuracy@0.8	Accuracy@0.81	Accuracy@0.82	Accuracy@0.83	Accuracy@0.84	Accuracy@0.85	Accuracy@0.86	Accuracy@0.87	Accuracy@0.88	Accuracy@0.89	Accuracy@0.9	Accuracy@0.91	Accuracy@0.92	Accuracy@0.93	Accuracy@0.94	Accuracy@0.95	Accuracy@0.96	Accuracy@0.97	Accuracy@0.98	Accuracy@0.99
0.0105	1.0	1807	0.0168	0.9547	0.9681	0.9739	0.9772	0.9796	0.9814	0.9827	0.9839	0.9848	0.9856	0.9863	0.9870	0.9875	0.9880	0.9885	0.9889	0.9893	0.9897	0.9900	0.9903	0.9905	0.9908	0.9910	0.9913	0.9915	0.9917	0.9919	0.9921	0.9923	0.9925	0.9927	0.9929	0.9930	0.9932	0.9933	0.9935	0.9936	0.9937	0.9938	0.9940	0.9941	0.9942	0.9943	0.9944	0.9945	0.9946	0.9947	0.9948	0.9949	0.9950	0.9951	0.9952	0.9953	0.9954	0.9955	0.9955	0.9956	0.9957	0.9958	0.9958	0.9959	0.9960	0.9961	0.9962	0.9962	0.9963	0.9964	0.9964	0.9965	0.9966	0.9966	0.9967	0.9968	0.9969	0.9969	0.9970	0.9971	0.9971	0.9972	0.9973	0.9973	0.9974	0.9975	0.9975	0.9976	0.9977	0.9977	0.9978	0.9979	0.9979	0.9980	0.9981	0.9982	0.9983	0.9984	0.9985	0.9987	0.9989	0.9992
0.0059	2.0	3614	0.0127	0.9793	0.9845	0.9869	0.9883	0.9893	0.9901	0.9907	0.9912	0.9916	0.9919	0.9922	0.9925	0.9928	0.9930	0.9932	0.9934	0.9935	0.9937	0.9938	0.9940	0.9941	0.9942	0.9943	0.9944	0.9945	0.9946	0.9947	0.9949	0.9949	0.9950	0.9951	0.9952	0.9953	0.9953	0.9954	0.9955	0.9955	0.9956	0.9957	0.9958	0.9958	0.9959	0.9959	0.9960	0.9960	0.9961	0.9961	0.9962	0.9962	0.9963	0.9964	0.9964	0.9965	0.9965	0.9966	0.9966	0.9967	0.9967	0.9968	0.9968	0.9968	0.9969	0.9969	0.9970	0.9970	0.9971	0.9971	0.9972	0.9972	0.9972	0.9973	0.9973	0.9974	0.9974	0.9975	0.9975	0.9975	0.9976	0.9976	0.9977	0.9977	0.9978	0.9978	0.9979	0.9979	0.9980	0.9980	0.9981	0.9981	0.9982	0.9982	0.9983	0.9984	0.9985	0.9985	0.9986	0.9987	0.9989	0.9991
0.0042	3.0	5421	0.0103	0.9841	0.9880	0.9898	0.9909	0.9916	0.9922	0.9926	0.9930	0.9933	0.9936	0.9938	0.9940	0.9942	0.9944	0.9945	0.9947	0.9948	0.9949	0.9951	0.9952	0.9953	0.9954	0.9955	0.9956	0.9956	0.9957	0.9958	0.9959	0.9959	0.9960	0.9961	0.9961	0.9962	0.9962	0.9963	0.9964	0.9964	0.9965	0.9965	0.9966	0.9966	0.9967	0.9967	0.9968	0.9968	0.9968	0.9969	0.9969	0.9970	0.9970	0.9970	0.9971	0.9971	0.9972	0.9972	0.9972	0.9973	0.9973	0.9974	0.9974	0.9974	0.9975	0.9975	0.9976	0.9976	0.9976	0.9977	0.9977	0.9977	0.9978	0.9978	0.9978	0.9979	0.9979	0.9979	0.9980	0.9980	0.9980	0.9981	0.9981	0.9982	0.9982	0.9982	0.9983	0.9983	0.9984	0.9984	0.9984	0.9985	0.9985	0.9986	0.9986	0.9987	0.9988	0.9988	0.9989	0.9990	0.9991	0.9993

Framework versions

• Transformers 4.46.2
• Pytorch 2.5.1+cu124
• Datasets 3.1.0
• Tokenizers 0.20.3