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# roberta-basefinetuned-ner-cadec

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3874
- Precision: 0.4370
- Recall: 0.4719
- F1: 0.4538
- Accuracy: 0.8849
- Adr Precision: 0.3917
- Adr Recall: 0.4477
- Adr F1: 0.4178
- Disease Precision: 0.0
- Disease Recall: 0.0
- Disease F1: 0.0
- Drug Precision: 0.7184
- Drug Recall: 0.7576
- Drug F1: 0.7375
- Finding Precision: 0.1389
- Finding Recall: 0.1111
- Finding F1: 0.1235
- Symptom Precision: 0.2353
- Symptom Recall: 0.1481
- Symptom F1: 0.1818
- B-adr Precision: 0.6259
- B-adr Recall: 0.6488
- B-adr F1: 0.6371
- B-disease Precision: 0.0
- B-disease Recall: 0.0
- B-disease F1: 0.0
- B-drug Precision: 0.8589
- B-drug Recall: 0.8485
- B-drug F1: 0.8537
- B-finding Precision: 0.4
- B-finding Recall: 0.1778
- B-finding F1: 0.2462
- B-symptom Precision: 0.2667
- B-symptom Recall: 0.16
- B-symptom F1: 0.2
- I-adr Precision: 0.3877
- I-adr Recall: 0.4305
- I-adr F1: 0.4079
- I-disease Precision: 0.0
- I-disease Recall: 0.0
- I-disease F1: 0.0
- I-drug Precision: 0.7456
- I-drug Recall: 0.7636
- I-drug F1: 0.7545
- I-finding Precision: 0.1429
- I-finding Recall: 0.125
- I-finding F1: 0.1333
- I-symptom Precision: 0.5
- I-symptom Recall: 0.1
- I-symptom F1: 0.1667
- Macro Avg F1: 0.3399
- Weighted Avg F1: 0.5527

## 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: 10

### 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 | B-adr Precision | B-adr Recall | B-adr F1 | B-disease Precision | B-disease Recall | B-disease F1 | B-drug Precision | B-drug Recall | B-drug F1 | B-finding Precision | B-finding Recall | B-finding F1 | B-symptom Precision | B-symptom Recall | B-symptom F1 | I-adr Precision | I-adr Recall | I-adr F1 | I-disease Precision | I-disease Recall | I-disease F1 | I-drug Precision | I-drug Recall | I-drug F1 | I-finding Precision | I-finding Recall | I-finding F1 | I-symptom Precision | I-symptom Recall | I-symptom F1 | Macro Avg F1 | Weighted Avg F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|:------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------:|:-------:|:-----------------:|:--------------:|:----------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:-------------------:|:----------------:|:------------:|:----------------:|:-------------:|:---------:|:-------------------:|:----------------:|:------------:|:-------------------:|:----------------:|:------------:|:---------------:|:------------:|:--------:|:-------------------:|:----------------:|:------------:|:----------------:|:-------------:|:---------:|:-------------------:|:----------------:|:------------:|:-------------------:|:----------------:|:------------:|:------------:|:---------------:|
| No log        | 1.0   | 127  | 0.5344          | 0.3114    | 0.2247 | 0.2611 | 0.8487   | 0.1715        | 0.1505     | 0.1603 | 0.0               | 0.0            | 0.0        | 0.98           | 0.5939      | 0.7396  | 0.0               | 0.0            | 0.0        | 0.0               | 0.0            | 0.0        | 0.5933          | 0.2380       | 0.3397   | 0.0                 | 0.0              | 0.0          | 1.0              | 0.5939        | 0.7452    | 0.0                 | 0.0              | 0.0          | 0.0                 | 0.0              | 0.0          | 0.1456          | 0.1347       | 0.1399   | 0.0                 | 0.0              | 0.0          | 0.98             | 0.5939        | 0.7396    | 0.0                 | 0.0              | 0.0          | 0.0                 | 0.0              | 0.0          | 0.1965       | 0.3329          |
| No log        | 2.0   | 254  | 0.4494          | 0.3603    | 0.2946 | 0.3242 | 0.8676   | 0.2676        | 0.2440     | 0.2553 | 0.0               | 0.0            | 0.0        | 0.6519         | 0.6242      | 0.6378  | 0.0               | 0.0            | 0.0        | 0.0               | 0.0            | 0.0        | 0.5703          | 0.4280       | 0.4890   | 0.0                 | 0.0              | 0.0          | 1.0              | 0.6182        | 0.7640    | 0.0                 | 0.0              | 0.0          | 0.0                 | 0.0              | 0.0          | 0.2759          | 0.2296       | 0.2506   | 0.0                 | 0.0              | 0.0          | 0.7342           | 0.7030        | 0.7183    | 0.0                 | 0.0              | 0.0          | 0.0                 | 0.0              | 0.0          | 0.2222       | 0.4204          |
| No log        | 3.0   | 381  | 0.4357          | 0.3508    | 0.3758 | 0.3629 | 0.8628   | 0.2656        | 0.3431     | 0.2994 | 0.0               | 0.0            | 0.0        | 0.7451         | 0.6909      | 0.7170  | 0.0               | 0.0            | 0.0        | 0.0               | 0.0            | 0.0        | 0.5393          | 0.5662       | 0.5524   | 0.0                 | 0.0              | 0.0          | 0.9375           | 0.7273        | 0.8191    | 0.0                 | 0.0              | 0.0          | 0.0                 | 0.0              | 0.0          | 0.2402          | 0.3113       | 0.2712   | 0.0                 | 0.0              | 0.0          | 0.7550           | 0.6909        | 0.7215    | 1.0                 | 0.0312           | 0.0606       | 0.0                 | 0.0              | 0.0          | 0.2425       | 0.4573          |
| 0.5429        | 4.0   | 508  | 0.4086          | 0.4501    | 0.4170 | 0.4329 | 0.8819   | 0.3612        | 0.3890     | 0.3746 | 0.0               | 0.0            | 0.0        | 0.7922         | 0.7394      | 0.7649  | 0.0               | 0.0            | 0.0        | 0.0               | 0.0            | 0.0        | 0.5988          | 0.5816       | 0.5901   | 0.0                 | 0.0              | 0.0          | 0.9209           | 0.7758        | 0.8421    | 0.0                 | 0.0              | 0.0          | 0.0                 | 0.0              | 0.0          | 0.3793          | 0.3642       | 0.3716   | 0.0                 | 0.0              | 0.0          | 0.82             | 0.7455        | 0.7810    | 1.0                 | 0.0312           | 0.0606       | 0.0                 | 0.0              | 0.0          | 0.2645       | 0.5113          |
| 0.5429        | 5.0   | 635  | 0.3806          | 0.4225    | 0.4457 | 0.4338 | 0.8797   | 0.3398        | 0.4165     | 0.3743 | 0.0               | 0.0            | 0.0        | 0.7805         | 0.7758      | 0.7781  | 0.2               | 0.0222         | 0.0400     | 0.5               | 0.0370         | 0.0690     | 0.5844          | 0.6180       | 0.6007   | 0.0                 | 0.0              | 0.0          | 0.8535           | 0.8121        | 0.8323    | 0.5                 | 0.0222           | 0.0426       | 0.5                 | 0.04             | 0.0741       | 0.3346          | 0.4018       | 0.3651   | 0.1667              | 0.0769           | 0.1053       | 0.8153           | 0.7758        | 0.7950    | 0.2                 | 0.0312           | 0.0541       | 0.0                 | 0.0              | 0.0          | 0.2869       | 0.5170          |
| 0.5429        | 6.0   | 762  | 0.3902          | 0.3860    | 0.4419 | 0.4121 | 0.8738   | 0.3329        | 0.4239     | 0.3729 | 0.0               | 0.0            | 0.0        | 0.6349         | 0.7273      | 0.6780  | 0.0833            | 0.0222         | 0.0351     | 0.4               | 0.0741         | 0.125      | 0.5832          | 0.6526       | 0.6159   | 0.0                 | 0.0              | 0.0          | 0.7886           | 0.8364        | 0.8118    | 0.3333              | 0.0444           | 0.0784       | 0.4                 | 0.08             | 0.1333       | 0.3198          | 0.3996       | 0.3553   | 0.0588              | 0.0769           | 0.0667       | 0.6910           | 0.7455        | 0.7172    | 0.1                 | 0.0312           | 0.0476       | 0.0                 | 0.0              | 0.0          | 0.2826       | 0.5099          |
| 0.5429        | 7.0   | 889  | 0.3776          | 0.4149    | 0.4594 | 0.4360 | 0.8795   | 0.3595        | 0.4367     | 0.3944 | 0.0               | 0.0            | 0.0        | 0.6949         | 0.7455      | 0.7193  | 0.125             | 0.0667         | 0.0870     | 0.3636            | 0.1481         | 0.2105     | 0.6094          | 0.6468       | 0.6276   | 0.0                 | 0.0              | 0.0          | 0.8405           | 0.8303        | 0.8354    | 0.4167              | 0.1111           | 0.1754       | 0.4                 | 0.16             | 0.2286       | 0.3443          | 0.4150       | 0.3764   | 0.0                 | 0.0              | 0.0          | 0.7326           | 0.7636        | 0.7478    | 0.1905              | 0.125            | 0.1509       | 0.0                 | 0.0              | 0.0          | 0.3142       | 0.5330          |
| 0.3019        | 8.0   | 1016 | 0.3892          | 0.4108    | 0.4657 | 0.4365 | 0.8781   | 0.3488        | 0.4404     | 0.3893 | 0.0               | 0.0            | 0.0        | 0.75           | 0.7636      | 0.7568  | 0.16              | 0.0889         | 0.1143     | 0.2727            | 0.1111         | 0.1579     | 0.5928          | 0.6679       | 0.6282   | 0.0                 | 0.0              | 0.0          | 0.8625           | 0.8364        | 0.8492    | 0.4375              | 0.1556           | 0.2295       | 0.3                 | 0.12             | 0.1714       | 0.3357          | 0.4172       | 0.3720   | 0.0                 | 0.0              | 0.0          | 0.7875           | 0.7636        | 0.7754    | 0.1667              | 0.0938           | 0.1200       | 0.0                 | 0.0              | 0.0          | 0.3146       | 0.5366          |
| 0.3019        | 9.0   | 1143 | 0.3872          | 0.4463    | 0.4719 | 0.4587 | 0.8845   | 0.3939        | 0.4495     | 0.4199 | 0.0               | 0.0            | 0.0        | 0.7530         | 0.7576      | 0.7553  | 0.1333            | 0.0889         | 0.1067     | 0.2667            | 0.1481         | 0.1905     | 0.6309          | 0.6430       | 0.6369   | 0.0                 | 0.0              | 0.0          | 0.8571           | 0.8364        | 0.8466    | 0.4375              | 0.1556           | 0.2295       | 0.3077              | 0.16             | 0.2105       | 0.3893          | 0.4349       | 0.4108   | 0.0                 | 0.0              | 0.0          | 0.7764           | 0.7576        | 0.7669    | 0.16                | 0.125            | 0.1404       | 0.6667              | 0.1              | 0.1739       | 0.3416       | 0.5540          |
| 0.3019        | 10.0  | 1270 | 0.3874          | 0.4370    | 0.4719 | 0.4538 | 0.8849   | 0.3917        | 0.4477     | 0.4178 | 0.0               | 0.0            | 0.0        | 0.7184         | 0.7576      | 0.7375  | 0.1389            | 0.1111         | 0.1235     | 0.2353            | 0.1481         | 0.1818     | 0.6259          | 0.6488       | 0.6371   | 0.0                 | 0.0              | 0.0          | 0.8589           | 0.8485        | 0.8537    | 0.4                 | 0.1778           | 0.2462       | 0.2667              | 0.16             | 0.2          | 0.3877          | 0.4305       | 0.4079   | 0.0                 | 0.0              | 0.0          | 0.7456           | 0.7636        | 0.7545    | 0.1429              | 0.125            | 0.1333       | 0.5                 | 0.1              | 0.1667       | 0.3399       | 0.5527          |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "roberta-base", "model-index": [{"name": "roberta-basefinetuned-ner-cadec", "results": []}]}
transformers tensorboard safetensors roberta token-classification generated_from_trainer base_model:roberta-base license:mit autotrain_compatible endpoints_compatible region:us