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# albert-base-v2-finetuned-ner-cadec-no-iob

This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5037
- Precision: 0.5849
- Recall: 0.6227
- F1: 0.6032
- Accuracy: 0.9311
- Adr Precision: 0.5065
- Adr Recall: 0.5608
- Adr F1: 0.5323
- Disease Precision: 0.52
- Disease Recall: 0.4062
- Disease F1: 0.4561
- Drug Precision: 0.9121
- Drug Recall: 0.9222
- Drug F1: 0.9171
- Finding Precision: 0.1875
- Finding Recall: 0.1875
- Finding F1: 0.1875
- Symptom Precision: 0.4839
- Symptom Recall: 0.5172
- Symptom F1: 0.5000
- Macro Avg F1: 0.5186
- Weighted Avg F1: 0.6047

## 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: 35

### 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  | 0.2244          | 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |
| 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          |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "albert-base-v2", "model-index": [{"name": "albert-base-v2-finetuned-ner-cadec-no-iob", "results": []}]}
transformers tensorboard safetensors albert token-classification generated_from_trainer base_model:albert-base-v2 license:apache-2.0 autotrain_compatible endpoints_compatible region:us