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biomedical-ner-all-finetuned-ner

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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: biomedical-ner-all-finetuned-ner
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # biomedical-ner-all-finetuned-ner
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+
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+ This model is a fine-tuned version of [d4data/biomedical-ner-all](https://huggingface.co/d4data/biomedical-ner-all) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0020
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+ - Precision: 1.0
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+ - Recall: 1.0
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+ - F1: 1.0
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+ - Accuracy: 1.0
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 100
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 4 | 0.1905 | 0.7660 | 0.8480 | 0.8049 | 0.9447 |
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+ | No log | 2.0 | 8 | 0.1689 | 0.7619 | 0.8565 | 0.8065 | 0.9482 |
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+ | No log | 3.0 | 12 | 0.1535 | 0.7950 | 0.8801 | 0.8354 | 0.9552 |
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+ | No log | 4.0 | 16 | 0.1337 | 0.816 | 0.8737 | 0.8438 | 0.9598 |
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+ | No log | 5.0 | 20 | 0.1206 | 0.8191 | 0.9015 | 0.8583 | 0.9636 |
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+ | No log | 6.0 | 24 | 0.1047 | 0.8285 | 0.9101 | 0.8673 | 0.9689 |
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+ | No log | 7.0 | 28 | 0.0943 | 0.8509 | 0.9165 | 0.8825 | 0.9703 |
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+ | No log | 8.0 | 32 | 0.0848 | 0.8495 | 0.9186 | 0.8827 | 0.9734 |
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+ | No log | 9.0 | 36 | 0.0756 | 0.8732 | 0.9293 | 0.9004 | 0.9780 |
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+ | No log | 10.0 | 40 | 0.0671 | 0.8884 | 0.9379 | 0.9125 | 0.9825 |
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+ | No log | 11.0 | 44 | 0.0625 | 0.8912 | 0.9293 | 0.9099 | 0.9836 |
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+ | No log | 12.0 | 48 | 0.0548 | 0.9048 | 0.9358 | 0.92 | 0.9857 |
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+ | No log | 13.0 | 52 | 0.0492 | 0.9129 | 0.9422 | 0.9273 | 0.9881 |
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+ | No log | 14.0 | 56 | 0.0466 | 0.9187 | 0.9443 | 0.9314 | 0.9885 |
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+ | No log | 15.0 | 60 | 0.0400 | 0.9308 | 0.9507 | 0.9407 | 0.9902 |
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+ | No log | 16.0 | 64 | 0.0369 | 0.9414 | 0.9636 | 0.9524 | 0.9920 |
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+ | No log | 17.0 | 68 | 0.0342 | 0.9356 | 0.9636 | 0.9494 | 0.9920 |
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+ | No log | 18.0 | 72 | 0.0300 | 0.9514 | 0.9636 | 0.9574 | 0.9937 |
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+ | No log | 19.0 | 76 | 0.0284 | 0.9558 | 0.9722 | 0.9639 | 0.9941 |
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+ | No log | 20.0 | 80 | 0.0270 | 0.9580 | 0.9764 | 0.9671 | 0.9944 |
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+ | No log | 21.0 | 84 | 0.0234 | 0.9621 | 0.9786 | 0.9703 | 0.9948 |
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+ | No log | 22.0 | 88 | 0.0215 | 0.9662 | 0.9807 | 0.9734 | 0.9955 |
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+ | No log | 23.0 | 92 | 0.0199 | 0.9662 | 0.9807 | 0.9734 | 0.9955 |
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+ | No log | 24.0 | 96 | 0.0179 | 0.9725 | 0.9829 | 0.9776 | 0.9955 |
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+ | No log | 25.0 | 100 | 0.0164 | 0.9851 | 0.9893 | 0.9872 | 0.9965 |
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+ | No log | 26.0 | 104 | 0.0158 | 0.9851 | 0.9914 | 0.9883 | 0.9969 |
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+ | No log | 27.0 | 108 | 0.0145 | 0.9809 | 0.9914 | 0.9862 | 0.9979 |
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+ | No log | 28.0 | 112 | 0.0130 | 0.9809 | 0.9914 | 0.9862 | 0.9983 |
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+ | No log | 29.0 | 116 | 0.0122 | 0.9809 | 0.9914 | 0.9862 | 0.9983 |
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+ | No log | 30.0 | 120 | 0.0114 | 0.9872 | 0.9936 | 0.9904 | 0.9986 |
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+ | No log | 31.0 | 124 | 0.0104 | 0.9872 | 0.9936 | 0.9904 | 0.9990 |
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+ | No log | 32.0 | 128 | 0.0098 | 0.9830 | 0.9914 | 0.9872 | 0.9986 |
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+ | No log | 33.0 | 132 | 0.0094 | 0.9830 | 0.9914 | 0.9872 | 0.9986 |
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+ | No log | 34.0 | 136 | 0.0091 | 0.9915 | 0.9957 | 0.9936 | 0.9993 |
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+ | No log | 35.0 | 140 | 0.0085 | 0.9915 | 0.9957 | 0.9936 | 0.9990 |
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+ | No log | 36.0 | 144 | 0.0080 | 0.9915 | 0.9957 | 0.9936 | 0.9990 |
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+ | No log | 37.0 | 148 | 0.0073 | 0.9915 | 0.9957 | 0.9936 | 0.9993 |
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+ | No log | 38.0 | 152 | 0.0069 | 0.9915 | 0.9957 | 0.9936 | 0.9993 |
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+ | No log | 39.0 | 156 | 0.0067 | 0.9915 | 0.9957 | 0.9936 | 0.9993 |
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+ | No log | 40.0 | 160 | 0.0063 | 0.9915 | 0.9957 | 0.9936 | 0.9993 |
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+ | No log | 41.0 | 164 | 0.0059 | 0.9915 | 0.9957 | 0.9936 | 0.9993 |
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+ | No log | 42.0 | 168 | 0.0056 | 0.9957 | 0.9979 | 0.9968 | 0.9997 |
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+ | No log | 43.0 | 172 | 0.0053 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 44.0 | 176 | 0.0050 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 45.0 | 180 | 0.0049 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 46.0 | 184 | 0.0049 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 47.0 | 188 | 0.0046 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 48.0 | 192 | 0.0043 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 49.0 | 196 | 0.0041 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 50.0 | 200 | 0.0041 | 0.9957 | 0.9979 | 0.9968 | 0.9997 |
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+ | No log | 51.0 | 204 | 0.0041 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 52.0 | 208 | 0.0038 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 53.0 | 212 | 0.0036 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 54.0 | 216 | 0.0034 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 55.0 | 220 | 0.0033 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 56.0 | 224 | 0.0033 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 57.0 | 228 | 0.0033 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 58.0 | 232 | 0.0032 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 59.0 | 236 | 0.0031 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 60.0 | 240 | 0.0030 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 61.0 | 244 | 0.0029 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 62.0 | 248 | 0.0029 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 63.0 | 252 | 0.0028 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 64.0 | 256 | 0.0028 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 65.0 | 260 | 0.0027 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 66.0 | 264 | 0.0027 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 67.0 | 268 | 0.0026 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 68.0 | 272 | 0.0025 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 69.0 | 276 | 0.0025 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 70.0 | 280 | 0.0024 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 71.0 | 284 | 0.0024 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 72.0 | 288 | 0.0024 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 73.0 | 292 | 0.0024 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 74.0 | 296 | 0.0023 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 75.0 | 300 | 0.0023 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 76.0 | 304 | 0.0023 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 77.0 | 308 | 0.0023 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 78.0 | 312 | 0.0022 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 79.0 | 316 | 0.0022 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 80.0 | 320 | 0.0022 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 81.0 | 324 | 0.0022 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 82.0 | 328 | 0.0022 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 83.0 | 332 | 0.0021 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 84.0 | 336 | 0.0021 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 85.0 | 340 | 0.0021 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 86.0 | 344 | 0.0021 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 87.0 | 348 | 0.0021 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 88.0 | 352 | 0.0021 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 89.0 | 356 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 90.0 | 360 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 91.0 | 364 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 92.0 | 368 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 93.0 | 372 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 94.0 | 376 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 95.0 | 380 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 96.0 | 384 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 97.0 | 388 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 98.0 | 392 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 99.0 | 396 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | No log | 100.0 | 400 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.26.0
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+ - Pytorch 1.13.1+cu116
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+ - Datasets 2.8.0
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+ - Tokenizers 0.13.2