Training complete
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README.md
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---
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license: apache-2.0
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base_model: albert-base-v2
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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: albert-base-v2-finetuned-ner-cadec-no-iob
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results: []
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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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# albert-base-v2-finetuned-ner-cadec-no-iob
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This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5037
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- Precision: 0.5849
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- Recall: 0.6227
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- F1: 0.6032
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- Accuracy: 0.9311
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- Adr Precision: 0.5065
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- Adr Recall: 0.5608
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- Adr F1: 0.5323
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- Disease Precision: 0.52
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- Disease Recall: 0.4062
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- Disease F1: 0.4561
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- Drug Precision: 0.9121
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- Drug Recall: 0.9222
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- Drug F1: 0.9171
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- Finding Precision: 0.1875
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- Finding Recall: 0.1875
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- Finding F1: 0.1875
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- Symptom Precision: 0.4839
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- Symptom Recall: 0.5172
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- Symptom F1: 0.5000
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- Macro Avg F1: 0.5186
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- Weighted Avg F1: 0.6047
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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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: 8
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- eval_batch_size: 8
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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: 35
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### Training results
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| 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 |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|:------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------:|:-------:|:-----------------:|:--------------:|:----------:|:-----------------:|:--------------:|:----------:|:------------:|:---------------:|
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| No log | 1.0 | 125 | 0.2244 | 0.5211 | 0.6029 | 0.5590 | 0.9215 | 0.4547 | 0.6103 | 0.5211 | 0.3864 | 0.5312 | 0.4474 | 0.8276 | 0.8 | 0.8136 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3564 | 0.5455 |
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| No log | 2.0 | 250 | 0.2082 | 0.5448 | 0.5937 | 0.5682 | 0.9240 | 0.4700 | 0.5649 | 0.5131 | 0.4348 | 0.3125 | 0.3636 | 0.8722 | 0.8722 | 0.8722 | 0.1892 | 0.2188 | 0.2029 | 0.6667 | 0.0690 | 0.125 | 0.4154 | 0.5641 |
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| No log | 3.0 | 375 | 0.2113 | 0.5416 | 0.6016 | 0.5700 | 0.9273 | 0.4863 | 0.5505 | 0.5164 | 0.48 | 0.375 | 0.4211 | 0.8182 | 0.85 | 0.8338 | 0.1622 | 0.1875 | 0.1739 | 0.4091 | 0.6207 | 0.4932 | 0.4877 | 0.5724 |
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| 0.187 | 4.0 | 500 | 0.2257 | 0.5418 | 0.6069 | 0.5725 | 0.9281 | 0.4739 | 0.5423 | 0.5058 | 0.3548 | 0.3438 | 0.3492 | 0.8944 | 0.8944 | 0.8944 | 0.1607 | 0.2812 | 0.2045 | 0.5926 | 0.5517 | 0.5714 | 0.5051 | 0.5813 |
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| 0.187 | 5.0 | 625 | 0.2483 | 0.5788 | 0.6253 | 0.6011 | 0.9283 | 0.4957 | 0.5918 | 0.5395 | 0.6111 | 0.3438 | 0.4400 | 0.8883 | 0.8833 | 0.8858 | 0.2083 | 0.1562 | 0.1786 | 0.6316 | 0.4138 | 0.5 | 0.5088 | 0.6008 |
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| 0.187 | 6.0 | 750 | 0.2584 | 0.5572 | 0.6042 | 0.5797 | 0.9242 | 0.4843 | 0.5423 | 0.5117 | 0.4 | 0.375 | 0.3871 | 0.8989 | 0.8889 | 0.8939 | 0.1951 | 0.25 | 0.2192 | 0.5 | 0.5172 | 0.5085 | 0.5041 | 0.5847 |
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| 0.187 | 7.0 | 875 | 0.2676 | 0.5640 | 0.5989 | 0.5809 | 0.9261 | 0.4836 | 0.5464 | 0.5131 | 1.0 | 0.0938 | 0.1714 | 0.9096 | 0.8944 | 0.9020 | 0.1964 | 0.3438 | 0.25 | 0.6667 | 0.4828 | 0.56 | 0.4793 | 0.5817 |
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| 0.0608 | 8.0 | 1000 | 0.2623 | 0.5797 | 0.6187 | 0.5986 | 0.9335 | 0.5121 | 0.5670 | 0.5382 | 0.5556 | 0.3125 | 0.4000 | 0.8944 | 0.8944 | 0.8944 | 0.1562 | 0.1562 | 0.1562 | 0.4286 | 0.6207 | 0.5070 | 0.4992 | 0.5996 |
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| 0.0608 | 9.0 | 1125 | 0.2968 | 0.5754 | 0.6293 | 0.6011 | 0.9314 | 0.5162 | 0.5897 | 0.5505 | 0.4062 | 0.4062 | 0.4062 | 0.8840 | 0.8889 | 0.8864 | 0.1282 | 0.1562 | 0.1408 | 0.5652 | 0.4483 | 0.5000 | 0.4968 | 0.6050 |
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| 0.0608 | 10.0 | 1250 | 0.3169 | 0.5485 | 0.5897 | 0.5683 | 0.9289 | 0.4887 | 0.5361 | 0.5113 | 0.3333 | 0.3125 | 0.3226 | 0.8820 | 0.8722 | 0.8771 | 0.1389 | 0.1562 | 0.1471 | 0.3846 | 0.5172 | 0.4412 | 0.4598 | 0.5721 |
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| 0.0608 | 11.0 | 1375 | 0.3367 | 0.5673 | 0.6227 | 0.5937 | 0.9261 | 0.5081 | 0.5794 | 0.5414 | 0.5625 | 0.2812 | 0.375 | 0.8798 | 0.8944 | 0.8871 | 0.175 | 0.2188 | 0.1944 | 0.35 | 0.4828 | 0.4058 | 0.4807 | 0.5966 |
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| 0.0214 | 12.0 | 1500 | 0.3600 | 0.5917 | 0.6425 | 0.6161 | 0.9314 | 0.5325 | 0.5918 | 0.5605 | 0.4516 | 0.4375 | 0.4444 | 0.8684 | 0.9167 | 0.8919 | 0.2258 | 0.2188 | 0.2222 | 0.4375 | 0.4828 | 0.4590 | 0.5156 | 0.6162 |
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| 0.0214 | 13.0 | 1625 | 0.3514 | 0.5606 | 0.6161 | 0.5871 | 0.9279 | 0.4882 | 0.5546 | 0.5193 | 0.4412 | 0.4688 | 0.4545 | 0.8967 | 0.9167 | 0.9066 | 0.1351 | 0.1562 | 0.1449 | 0.4815 | 0.4483 | 0.4643 | 0.4979 | 0.5906 |
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| 0.0214 | 14.0 | 1750 | 0.3994 | 0.5654 | 0.5871 | 0.5761 | 0.9270 | 0.5090 | 0.5258 | 0.5172 | 0.3333 | 0.2812 | 0.3051 | 0.9034 | 0.8833 | 0.8933 | 0.125 | 0.1875 | 0.15 | 0.4571 | 0.5517 | 0.5 | 0.4731 | 0.5814 |
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| 0.0214 | 15.0 | 1875 | 0.4133 | 0.5858 | 0.5989 | 0.5923 | 0.9292 | 0.5276 | 0.5526 | 0.5398 | 0.4737 | 0.2812 | 0.3529 | 0.8807 | 0.8611 | 0.8708 | 0.1538 | 0.1875 | 0.1690 | 0.4848 | 0.5517 | 0.5161 | 0.4897 | 0.5939 |
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| 0.0089 | 16.0 | 2000 | 0.4126 | 0.5695 | 0.6108 | 0.5894 | 0.9301 | 0.4935 | 0.5505 | 0.5205 | 0.5 | 0.375 | 0.4286 | 0.9056 | 0.9056 | 0.9056 | 0.1951 | 0.25 | 0.2192 | 0.4815 | 0.4483 | 0.4643 | 0.5076 | 0.5932 |
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| 0.0089 | 17.0 | 2125 | 0.4195 | 0.5856 | 0.6095 | 0.5973 | 0.9288 | 0.5057 | 0.5505 | 0.5271 | 0.6923 | 0.2812 | 0.4 | 0.9157 | 0.9056 | 0.9106 | 0.1765 | 0.1875 | 0.1818 | 0.4722 | 0.5862 | 0.5231 | 0.5085 | 0.5981 |
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| 0.0089 | 18.0 | 2250 | 0.4177 | 0.5856 | 0.6227 | 0.6036 | 0.9300 | 0.5036 | 0.5711 | 0.5353 | 0.5 | 0.375 | 0.4286 | 0.9171 | 0.9222 | 0.9197 | 0.1667 | 0.1562 | 0.1613 | 0.5714 | 0.4138 | 0.4800 | 0.5050 | 0.6041 |
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| 0.0089 | 19.0 | 2375 | 0.4675 | 0.5623 | 0.5897 | 0.5757 | 0.9257 | 0.5038 | 0.5402 | 0.5214 | 0.4118 | 0.2188 | 0.2857 | 0.9023 | 0.8722 | 0.8870 | 0.0943 | 0.1562 | 0.1176 | 0.5161 | 0.5517 | 0.5333 | 0.4690 | 0.5817 |
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| 0.004 | 20.0 | 2500 | 0.4435 | 0.5604 | 0.6055 | 0.5821 | 0.9276 | 0.4878 | 0.5340 | 0.5098 | 0.4643 | 0.4062 | 0.4333 | 0.9066 | 0.9167 | 0.9116 | 0.15 | 0.1875 | 0.1667 | 0.4211 | 0.5517 | 0.4776 | 0.4998 | 0.5863 |
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| 95 |
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| 0.004 | 21.0 | 2625 | 0.4669 | 0.5516 | 0.5989 | 0.5743 | 0.9277 | 0.4822 | 0.5299 | 0.5049 | 0.4828 | 0.4375 | 0.4590 | 0.8962 | 0.9111 | 0.9036 | 0.1463 | 0.1875 | 0.1644 | 0.3514 | 0.4483 | 0.3939 | 0.4852 | 0.5790 |
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| 96 |
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| 0.004 | 22.0 | 2750 | 0.4732 | 0.5820 | 0.6042 | 0.5929 | 0.9285 | 0.5058 | 0.5381 | 0.5215 | 0.4643 | 0.4062 | 0.4333 | 0.9153 | 0.9 | 0.9076 | 0.2105 | 0.25 | 0.2286 | 0.5 | 0.4828 | 0.4912 | 0.5164 | 0.5959 |
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| 97 |
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| 0.004 | 23.0 | 2875 | 0.4922 | 0.5816 | 0.6016 | 0.5914 | 0.9258 | 0.5048 | 0.5402 | 0.5219 | 0.5 | 0.3438 | 0.4074 | 0.9091 | 0.8889 | 0.8989 | 0.2162 | 0.25 | 0.2319 | 0.5 | 0.5172 | 0.5085 | 0.5137 | 0.5938 |
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| 98 |
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| 0.0016 | 24.0 | 3000 | 0.4747 | 0.5789 | 0.6148 | 0.5963 | 0.9294 | 0.5038 | 0.5526 | 0.5270 | 0.4667 | 0.4375 | 0.4516 | 0.9056 | 0.9056 | 0.9056 | 0.2 | 0.1875 | 0.1935 | 0.4545 | 0.5172 | 0.4839 | 0.5123 | 0.5980 |
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| 99 |
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| 0.0016 | 25.0 | 3125 | 0.4849 | 0.5851 | 0.6121 | 0.5983 | 0.9300 | 0.5085 | 0.5526 | 0.5296 | 0.4783 | 0.3438 | 0.4 | 0.9011 | 0.9111 | 0.9061 | 0.2069 | 0.1875 | 0.1967 | 0.4688 | 0.5172 | 0.4918 | 0.5048 | 0.5981 |
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| 100 |
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| 0.0016 | 26.0 | 3250 | 0.4692 | 0.5821 | 0.6266 | 0.6036 | 0.9307 | 0.5009 | 0.5629 | 0.5301 | 0.48 | 0.375 | 0.4211 | 0.9176 | 0.9278 | 0.9227 | 0.2424 | 0.25 | 0.2462 | 0.4839 | 0.5172 | 0.5000 | 0.5240 | 0.6056 |
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| 101 |
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| 0.0016 | 27.0 | 3375 | 0.4785 | 0.5752 | 0.6108 | 0.5925 | 0.9299 | 0.5 | 0.5443 | 0.5212 | 0.4615 | 0.375 | 0.4138 | 0.9011 | 0.9111 | 0.9061 | 0.2105 | 0.25 | 0.2286 | 0.4839 | 0.5172 | 0.5000 | 0.5139 | 0.5949 |
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| 102 |
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| 0.001 | 28.0 | 3500 | 0.4873 | 0.5810 | 0.6201 | 0.5999 | 0.9322 | 0.5103 | 0.5629 | 0.5353 | 0.4815 | 0.4062 | 0.4407 | 0.8962 | 0.9111 | 0.9036 | 0.1613 | 0.1562 | 0.1587 | 0.4545 | 0.5172 | 0.4839 | 0.5044 | 0.6009 |
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| 103 |
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| 0.001 | 29.0 | 3625 | 0.4825 | 0.5813 | 0.6227 | 0.6013 | 0.9318 | 0.5028 | 0.5629 | 0.5311 | 0.52 | 0.4062 | 0.4561 | 0.8962 | 0.9111 | 0.9036 | 0.2333 | 0.2188 | 0.2258 | 0.4839 | 0.5172 | 0.5000 | 0.5233 | 0.6023 |
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| 104 |
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| 0.001 | 30.0 | 3750 | 0.4883 | 0.5769 | 0.6135 | 0.5946 | 0.9307 | 0.4944 | 0.5505 | 0.5210 | 0.52 | 0.4062 | 0.4561 | 0.9111 | 0.9111 | 0.9111 | 0.2069 | 0.1875 | 0.1967 | 0.4688 | 0.5172 | 0.4918 | 0.5154 | 0.5961 |
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| 105 |
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| 0.001 | 31.0 | 3875 | 0.4964 | 0.5734 | 0.6135 | 0.5927 | 0.9308 | 0.4963 | 0.5526 | 0.5229 | 0.5 | 0.4062 | 0.4483 | 0.9011 | 0.9111 | 0.9061 | 0.1613 | 0.1562 | 0.1587 | 0.4688 | 0.5172 | 0.4918 | 0.5056 | 0.5942 |
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| 106 |
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| 0.0005 | 32.0 | 4000 | 0.4977 | 0.5817 | 0.6201 | 0.6003 | 0.9309 | 0.5047 | 0.5588 | 0.5303 | 0.52 | 0.4062 | 0.4561 | 0.9066 | 0.9167 | 0.9116 | 0.1875 | 0.1875 | 0.1875 | 0.4688 | 0.5172 | 0.4918 | 0.5155 | 0.6018 |
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| 107 |
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| 0.0005 | 33.0 | 4125 | 0.5008 | 0.5810 | 0.6201 | 0.5999 | 0.9312 | 0.5047 | 0.5567 | 0.5294 | 0.5 | 0.4062 | 0.4483 | 0.9121 | 0.9222 | 0.9171 | 0.1765 | 0.1875 | 0.1818 | 0.4688 | 0.5172 | 0.4918 | 0.5137 | 0.6019 |
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| 108 |
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| 0.0005 | 34.0 | 4250 | 0.5028 | 0.5829 | 0.6214 | 0.6015 | 0.9310 | 0.5047 | 0.5588 | 0.5303 | 0.52 | 0.4062 | 0.4561 | 0.9121 | 0.9222 | 0.9171 | 0.1875 | 0.1875 | 0.1875 | 0.4688 | 0.5172 | 0.4918 | 0.5166 | 0.6031 |
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| 109 |
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| 0.0005 | 35.0 | 4375 | 0.5037 | 0.5849 | 0.6227 | 0.6032 | 0.9311 | 0.5065 | 0.5608 | 0.5323 | 0.52 | 0.4062 | 0.4561 | 0.9121 | 0.9222 | 0.9171 | 0.1875 | 0.1875 | 0.1875 | 0.4839 | 0.5172 | 0.5000 | 0.5186 | 0.6047 |
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| 110 |
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### Framework versions
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- Transformers 4.35.2
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- Pytorch 2.1.0+cu121
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- Datasets 2.15.0
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| 117 |
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- Tokenizers 0.15.0
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