metadata

library_name: transformers
base_model: allenai/scibert_scivocab_uncased
tags:
  - generated_from_trainer
model-index:
  - name: allenai-scibert_scivocab_uncased_20241126-033516
    results: []

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