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distilbert-base-uncased-finetuned-ner-cadec-no-iob

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metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: distilbert-base-uncased-finetuned-ner-cadec-no-iob
    results: []

distilbert-base-uncased-finetuned-ner-cadec-no-iob

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

  • Loss: 0.4152
  • Precision: 0.5826
  • Recall: 0.6187
  • F1: 0.6001
  • Accuracy: 0.9303
  • Adr Precision: 0.5109
  • Adr Recall: 0.5773
  • Adr F1: 0.5421
  • Disease Precision: 0.4643
  • Disease Recall: 0.4062
  • Disease F1: 0.4333
  • Drug Precision: 0.8743
  • Drug Recall: 0.8889
  • Drug F1: 0.8815
  • Finding Precision: 0.2143
  • Finding Recall: 0.1875
  • Finding F1: 0.2000
  • Symptom Precision: 0.5556
  • Symptom Recall: 0.3448
  • Symptom F1: 0.4255
  • Macro Avg F1: 0.4965
  • Weighted Avg F1: 0.5992

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: 2e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 40

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy Adr Precision Adr Recall Adr F1 Disease Precision Disease Recall Disease F1 Drug Precision Drug Recall Drug F1 Finding Precision Finding Recall Finding F1 Symptom Precision Symptom Recall Symptom F1 Macro Avg F1 Weighted Avg F1
No log 1.0 125 0.2402 0.4952 0.5462 0.5194 0.9140 0.3893 0.5258 0.4474 0.0 0.0 0.0 0.8883 0.8833 0.8858 0.0 0.0 0.0 0.0 0.0 0.0 0.2666 0.4966
No log 2.0 250 0.2136 0.5380 0.5976 0.5663 0.9239 0.4412 0.5649 0.4955 0.6818 0.4688 0.5556 0.8503 0.8833 0.8665 0.2857 0.0625 0.1026 0.6 0.1034 0.1765 0.4393 0.5573
No log 3.0 375 0.2199 0.5283 0.5660 0.5465 0.9191 0.4492 0.5010 0.4737 0.5455 0.375 0.4444 0.8674 0.8722 0.8698 0.1515 0.1562 0.1538 0.3429 0.4138 0.375 0.4634 0.5492
0.2232 4.0 500 0.2292 0.5622 0.5726 0.5673 0.9228 0.4971 0.5278 0.512 0.0 0.0 0.0 0.8791 0.8889 0.8840 0.1852 0.3125 0.2326 0.4211 0.2759 0.3333 0.3924 0.5601
0.2232 5.0 625 0.2474 0.5863 0.6095 0.5977 0.9265 0.5055 0.5732 0.5372 0.6 0.375 0.4615 0.8785 0.8833 0.8809 0.2273 0.1562 0.1852 0.5333 0.2759 0.3636 0.4857 0.5941
0.2232 6.0 750 0.2474 0.5635 0.5910 0.5769 0.9244 0.4842 0.5381 0.5098 0.375 0.375 0.375 0.8840 0.8889 0.8864 0.16 0.125 0.1404 0.6111 0.3793 0.4681 0.4759 0.5763
0.2232 7.0 875 0.2709 0.5758 0.5963 0.5859 0.9275 0.4991 0.5423 0.5198 0.5 0.2812 0.36 0.8710 0.9 0.8852 0.2683 0.3438 0.3014 0.5385 0.2414 0.3333 0.4799 0.5835
0.0707 8.0 1000 0.2611 0.5752 0.6003 0.5875 0.9282 0.4991 0.5485 0.5226 0.6923 0.2812 0.4 0.8689 0.8833 0.8760 0.2895 0.3438 0.3143 0.4167 0.3448 0.3774 0.4981 0.5870
0.0707 9.0 1125 0.2664 0.5749 0.6227 0.5978 0.9297 0.5111 0.5711 0.5394 0.4375 0.4375 0.4375 0.8710 0.9 0.8852 0.2093 0.2812 0.24 0.5556 0.3448 0.4255 0.5055 0.6003
0.0707 10.0 1250 0.3066 0.5537 0.5778 0.5655 0.9268 0.4761 0.5134 0.4940 0.4545 0.3125 0.3704 0.8610 0.8944 0.8774 0.25 0.2812 0.2647 0.3913 0.3103 0.3462 0.4705 0.5645
0.0707 11.0 1375 0.2980 0.5751 0.5910 0.5830 0.9282 0.4971 0.5340 0.5149 0.4615 0.375 0.4138 0.8602 0.8889 0.8743 0.2917 0.2188 0.25 0.4545 0.3448 0.3922 0.4890 0.5801
0.0293 12.0 1500 0.3272 0.5932 0.6174 0.6050 0.9303 0.5082 0.5732 0.5388 0.6316 0.375 0.4706 0.8901 0.9 0.8950 0.2609 0.1875 0.2182 0.5556 0.3448 0.4255 0.5096 0.6026
0.0293 13.0 1625 0.3161 0.5664 0.6187 0.5914 0.9288 0.4937 0.5691 0.5287 0.3846 0.4688 0.4225 0.8804 0.9 0.8901 0.2308 0.1875 0.2069 0.5 0.3448 0.4082 0.4913 0.5919
0.0293 14.0 1750 0.3529 0.5736 0.6016 0.5873 0.9269 0.4806 0.5361 0.5068 0.5652 0.4062 0.4727 0.8913 0.9111 0.9011 0.3077 0.25 0.2759 0.5238 0.3793 0.44 0.5193 0.5867
0.0293 15.0 1875 0.3381 0.5608 0.6082 0.5835 0.9290 0.5074 0.5649 0.5346 0.2857 0.1875 0.2264 0.8757 0.9 0.8877 0.1731 0.2812 0.2143 0.4167 0.3448 0.3774 0.4481 0.5859
0.0133 16.0 2000 0.3275 0.5833 0.6187 0.6005 0.9307 0.5064 0.5711 0.5368 0.4286 0.375 0.4000 0.8852 0.9 0.8926 0.2759 0.25 0.2623 0.5882 0.3448 0.4348 0.5053 0.6000
0.0133 17.0 2125 0.3623 0.5787 0.6161 0.5968 0.9310 0.4928 0.5649 0.5264 0.6 0.4688 0.5263 0.8852 0.9 0.8926 0.24 0.1875 0.2105 0.5556 0.3448 0.4255 0.5163 0.5962
0.0133 18.0 2250 0.3466 0.5699 0.6187 0.5933 0.9299 0.4937 0.5691 0.5287 0.3889 0.4375 0.4118 0.8901 0.9 0.8950 0.25 0.2188 0.2333 0.5556 0.3448 0.4255 0.4989 0.5944
0.0133 19.0 2375 0.3496 0.5751 0.6214 0.5973 0.9321 0.5101 0.5753 0.5407 0.4 0.375 0.3871 0.8798 0.8944 0.8871 0.1860 0.25 0.2133 0.6875 0.3793 0.4889 0.5034 0.6007
0.0075 20.0 2500 0.3676 0.5898 0.6280 0.6083 0.9314 0.5090 0.5814 0.5428 0.5185 0.4375 0.4746 0.8852 0.9 0.8926 0.2692 0.2188 0.2414 0.6471 0.3793 0.4783 0.5259 0.6078
0.0075 21.0 2625 0.3658 0.5816 0.6253 0.6027 0.9306 0.4991 0.5753 0.5345 0.5185 0.4375 0.4746 0.8811 0.9056 0.8932 0.2593 0.2188 0.2373 0.6471 0.3793 0.4783 0.5236 0.6024
0.0075 22.0 2750 0.3803 0.5804 0.6187 0.5990 0.9294 0.5148 0.5753 0.5433 0.3846 0.3125 0.3448 0.8859 0.9056 0.8956 0.2059 0.2188 0.2121 0.4545 0.3448 0.3922 0.4776 0.5988
0.0075 23.0 2875 0.3795 0.5954 0.6174 0.6062 0.9305 0.5139 0.5711 0.5410 0.5652 0.4062 0.4727 0.8852 0.9 0.8926 0.2609 0.1875 0.2182 0.5556 0.3448 0.4255 0.5100 0.6036
0.0051 24.0 3000 0.3849 0.5774 0.6148 0.5955 0.9295 0.5093 0.5670 0.5366 0.4444 0.375 0.4068 0.8798 0.8944 0.8871 0.2121 0.2188 0.2154 0.4583 0.3793 0.4151 0.4922 0.5961
0.0051 25.0 3125 0.3847 0.5911 0.6293 0.6096 0.9303 0.5247 0.5918 0.5562 0.4828 0.4375 0.4590 0.875 0.8944 0.8846 0.1724 0.1562 0.1639 0.5556 0.3448 0.4255 0.4979 0.6085
0.0051 26.0 3250 0.3917 0.5901 0.6266 0.6078 0.9298 0.5165 0.5794 0.5462 0.4667 0.4375 0.4516 0.8804 0.9 0.8901 0.2759 0.25 0.2623 0.5556 0.3448 0.4255 0.5151 0.6072
0.0051 27.0 3375 0.3915 0.5901 0.6306 0.6097 0.9306 0.5182 0.5876 0.5507 0.4828 0.4375 0.4590 0.8852 0.9 0.8926 0.2414 0.2188 0.2295 0.5263 0.3448 0.4167 0.5097 0.6093
0.0034 28.0 3500 0.4010 0.5881 0.6253 0.6061 0.9305 0.5240 0.5856 0.5531 0.4167 0.3125 0.3571 0.8757 0.9 0.8877 0.2162 0.25 0.2319 0.5556 0.3448 0.4255 0.4911 0.6058
0.0034 29.0 3625 0.4136 0.5955 0.6293 0.6119 0.9313 0.5212 0.5835 0.5506 0.4828 0.4375 0.4590 0.8859 0.9056 0.8956 0.2692 0.2188 0.2414 0.5263 0.3448 0.4167 0.5127 0.6105
0.0034 30.0 3750 0.4072 0.5918 0.6293 0.6100 0.9312 0.5191 0.5876 0.5513 0.4615 0.375 0.4138 0.8804 0.9 0.8901 0.2581 0.25 0.2540 0.625 0.3448 0.4444 0.5107 0.6093
0.0034 31.0 3875 0.4081 0.5995 0.6240 0.6115 0.9307 0.5294 0.5753 0.5514 0.4375 0.4375 0.4375 0.8804 0.9 0.8901 0.32 0.25 0.2807 0.4762 0.3448 0.4000 0.5119 0.6098
0.0025 32.0 4000 0.4022 0.5885 0.6319 0.6094 0.9312 0.5152 0.5938 0.5517 0.5185 0.4375 0.4746 0.875 0.8944 0.8846 0.25 0.1875 0.2143 0.5 0.3448 0.4082 0.5067 0.6078
0.0025 33.0 4125 0.4066 0.5821 0.6266 0.6036 0.9312 0.5108 0.5876 0.5465 0.4643 0.4062 0.4333 0.8743 0.8889 0.8815 0.2414 0.2188 0.2295 0.5556 0.3448 0.4255 0.5033 0.6033
0.0025 34.0 4250 0.4049 0.5865 0.6306 0.6078 0.9318 0.5198 0.5959 0.5552 0.4815 0.4062 0.4407 0.8696 0.8889 0.8791 0.2 0.1875 0.1935 0.5556 0.3448 0.4255 0.4988 0.6071
0.0025 35.0 4375 0.4129 0.5741 0.6187 0.5956 0.9294 0.5009 0.5773 0.5364 0.5 0.4375 0.4667 0.8689 0.8833 0.8760 0.2069 0.1875 0.1967 0.5556 0.3448 0.4255 0.5003 0.5955
0.002 36.0 4500 0.4134 0.5843 0.6266 0.6047 0.9303 0.5117 0.5876 0.5470 0.5 0.4375 0.4667 0.8743 0.8889 0.8815 0.2222 0.1875 0.2034 0.5556 0.3448 0.4255 0.5048 0.6039
0.002 37.0 4625 0.4138 0.5828 0.6266 0.6039 0.9303 0.5099 0.5856 0.5451 0.4815 0.4062 0.4407 0.875 0.8944 0.8846 0.2414 0.2188 0.2295 0.5556 0.3448 0.4255 0.5051 0.6034
0.002 38.0 4750 0.4126 0.5804 0.6187 0.5990 0.9297 0.5100 0.5794 0.5425 0.4444 0.375 0.4068 0.8743 0.8889 0.8815 0.2069 0.1875 0.1967 0.5556 0.3448 0.4255 0.4906 0.5982
0.002 39.0 4875 0.4139 0.5797 0.6187 0.5986 0.9301 0.5118 0.5794 0.5435 0.4286 0.375 0.4000 0.8743 0.8889 0.8815 0.1935 0.1875 0.1905 0.5556 0.3448 0.4255 0.4882 0.5983
0.0017 40.0 5000 0.4152 0.5826 0.6187 0.6001 0.9303 0.5109 0.5773 0.5421 0.4643 0.4062 0.4333 0.8743 0.8889 0.8815 0.2143 0.1875 0.2000 0.5556 0.3448 0.4255 0.4965 0.5992

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.15.0
  • Tokenizers 0.15.0