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# biomedical-ner-all-finetuned-ner

This model is a fine-tuned version of [d4data/biomedical-ner-all](https://huggingface.co/d4data/biomedical-ner-all) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0020
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 4    | 0.1905          | 0.7660    | 0.8480 | 0.8049 | 0.9447   |
| No log        | 2.0   | 8    | 0.1689          | 0.7619    | 0.8565 | 0.8065 | 0.9482   |
| No log        | 3.0   | 12   | 0.1535          | 0.7950    | 0.8801 | 0.8354 | 0.9552   |
| No log        | 4.0   | 16   | 0.1337          | 0.816     | 0.8737 | 0.8438 | 0.9598   |
| No log        | 5.0   | 20   | 0.1206          | 0.8191    | 0.9015 | 0.8583 | 0.9636   |
| No log        | 6.0   | 24   | 0.1047          | 0.8285    | 0.9101 | 0.8673 | 0.9689   |
| No log        | 7.0   | 28   | 0.0943          | 0.8509    | 0.9165 | 0.8825 | 0.9703   |
| No log        | 8.0   | 32   | 0.0848          | 0.8495    | 0.9186 | 0.8827 | 0.9734   |
| No log        | 9.0   | 36   | 0.0756          | 0.8732    | 0.9293 | 0.9004 | 0.9780   |
| No log        | 10.0  | 40   | 0.0671          | 0.8884    | 0.9379 | 0.9125 | 0.9825   |
| No log        | 11.0  | 44   | 0.0625          | 0.8912    | 0.9293 | 0.9099 | 0.9836   |
| No log        | 12.0  | 48   | 0.0548          | 0.9048    | 0.9358 | 0.92   | 0.9857   |
| No log        | 13.0  | 52   | 0.0492          | 0.9129    | 0.9422 | 0.9273 | 0.9881   |
| No log        | 14.0  | 56   | 0.0466          | 0.9187    | 0.9443 | 0.9314 | 0.9885   |
| No log        | 15.0  | 60   | 0.0400          | 0.9308    | 0.9507 | 0.9407 | 0.9902   |
| No log        | 16.0  | 64   | 0.0369          | 0.9414    | 0.9636 | 0.9524 | 0.9920   |
| No log        | 17.0  | 68   | 0.0342          | 0.9356    | 0.9636 | 0.9494 | 0.9920   |
| No log        | 18.0  | 72   | 0.0300          | 0.9514    | 0.9636 | 0.9574 | 0.9937   |
| No log        | 19.0  | 76   | 0.0284          | 0.9558    | 0.9722 | 0.9639 | 0.9941   |
| No log        | 20.0  | 80   | 0.0270          | 0.9580    | 0.9764 | 0.9671 | 0.9944   |
| No log        | 21.0  | 84   | 0.0234          | 0.9621    | 0.9786 | 0.9703 | 0.9948   |
| No log        | 22.0  | 88   | 0.0215          | 0.9662    | 0.9807 | 0.9734 | 0.9955   |
| No log        | 23.0  | 92   | 0.0199          | 0.9662    | 0.9807 | 0.9734 | 0.9955   |
| No log        | 24.0  | 96   | 0.0179          | 0.9725    | 0.9829 | 0.9776 | 0.9955   |
| No log        | 25.0  | 100  | 0.0164          | 0.9851    | 0.9893 | 0.9872 | 0.9965   |
| No log        | 26.0  | 104  | 0.0158          | 0.9851    | 0.9914 | 0.9883 | 0.9969   |
| No log        | 27.0  | 108  | 0.0145          | 0.9809    | 0.9914 | 0.9862 | 0.9979   |
| No log        | 28.0  | 112  | 0.0130          | 0.9809    | 0.9914 | 0.9862 | 0.9983   |
| No log        | 29.0  | 116  | 0.0122          | 0.9809    | 0.9914 | 0.9862 | 0.9983   |
| No log        | 30.0  | 120  | 0.0114          | 0.9872    | 0.9936 | 0.9904 | 0.9986   |
| No log        | 31.0  | 124  | 0.0104          | 0.9872    | 0.9936 | 0.9904 | 0.9990   |
| No log        | 32.0  | 128  | 0.0098          | 0.9830    | 0.9914 | 0.9872 | 0.9986   |
| No log        | 33.0  | 132  | 0.0094          | 0.9830    | 0.9914 | 0.9872 | 0.9986   |
| No log        | 34.0  | 136  | 0.0091          | 0.9915    | 0.9957 | 0.9936 | 0.9993   |
| No log        | 35.0  | 140  | 0.0085          | 0.9915    | 0.9957 | 0.9936 | 0.9990   |
| No log        | 36.0  | 144  | 0.0080          | 0.9915    | 0.9957 | 0.9936 | 0.9990   |
| No log        | 37.0  | 148  | 0.0073          | 0.9915    | 0.9957 | 0.9936 | 0.9993   |
| No log        | 38.0  | 152  | 0.0069          | 0.9915    | 0.9957 | 0.9936 | 0.9993   |
| No log        | 39.0  | 156  | 0.0067          | 0.9915    | 0.9957 | 0.9936 | 0.9993   |
| No log        | 40.0  | 160  | 0.0063          | 0.9915    | 0.9957 | 0.9936 | 0.9993   |
| No log        | 41.0  | 164  | 0.0059          | 0.9915    | 0.9957 | 0.9936 | 0.9993   |
| No log        | 42.0  | 168  | 0.0056          | 0.9957    | 0.9979 | 0.9968 | 0.9997   |
| No log        | 43.0  | 172  | 0.0053          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 44.0  | 176  | 0.0050          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 45.0  | 180  | 0.0049          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 46.0  | 184  | 0.0049          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 47.0  | 188  | 0.0046          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 48.0  | 192  | 0.0043          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 49.0  | 196  | 0.0041          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 50.0  | 200  | 0.0041          | 0.9957    | 0.9979 | 0.9968 | 0.9997   |
| No log        | 51.0  | 204  | 0.0041          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 52.0  | 208  | 0.0038          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 53.0  | 212  | 0.0036          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 54.0  | 216  | 0.0034          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 55.0  | 220  | 0.0033          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 56.0  | 224  | 0.0033          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 57.0  | 228  | 0.0033          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 58.0  | 232  | 0.0032          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 59.0  | 236  | 0.0031          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 60.0  | 240  | 0.0030          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 61.0  | 244  | 0.0029          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 62.0  | 248  | 0.0029          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 63.0  | 252  | 0.0028          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 64.0  | 256  | 0.0028          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 65.0  | 260  | 0.0027          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 66.0  | 264  | 0.0027          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 67.0  | 268  | 0.0026          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 68.0  | 272  | 0.0025          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 69.0  | 276  | 0.0025          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 70.0  | 280  | 0.0024          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 71.0  | 284  | 0.0024          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 72.0  | 288  | 0.0024          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 73.0  | 292  | 0.0024          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 74.0  | 296  | 0.0023          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 75.0  | 300  | 0.0023          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 76.0  | 304  | 0.0023          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 77.0  | 308  | 0.0023          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 78.0  | 312  | 0.0022          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 79.0  | 316  | 0.0022          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 80.0  | 320  | 0.0022          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 81.0  | 324  | 0.0022          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 82.0  | 328  | 0.0022          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 83.0  | 332  | 0.0021          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 84.0  | 336  | 0.0021          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 85.0  | 340  | 0.0021          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 86.0  | 344  | 0.0021          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 87.0  | 348  | 0.0021          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 88.0  | 352  | 0.0021          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 89.0  | 356  | 0.0020          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 90.0  | 360  | 0.0020          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 91.0  | 364  | 0.0020          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 92.0  | 368  | 0.0020          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 93.0  | 372  | 0.0020          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 94.0  | 376  | 0.0020          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 95.0  | 380  | 0.0020          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 96.0  | 384  | 0.0020          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 97.0  | 388  | 0.0020          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 98.0  | 392  | 0.0020          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 99.0  | 396  | 0.0020          | 1.0       | 1.0    | 1.0    | 1.0      |
| No log        | 100.0 | 400  | 0.0020          | 1.0       | 1.0    | 1.0    | 1.0      |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "biomedical-ner-all-finetuned-ner", "results": []}]}
transformers pytorch tensorboard distilbert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

biomedical-ner-all-finetuned-ner
================================


This model is a fine-tuned version of d4data/biomedical-ner-all on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0020
* Precision: 1.0
* Recall: 1.0
* F1: 1.0
* Accuracy: 1.0


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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2