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# multiCorp_5e-05_LabelNorm_0404

This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0310
- Precision: 0.6947
- Recall: 0.7305
- F1: 0.7121
- Accuracy: 0.9911

## 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: 5e-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
- training_steps: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2337        | 0.08  | 25   | 0.1020          | 0.0       | 0.0    | 0.0    | 0.9838   |
| 0.0855        | 0.15  | 50   | 0.1026          | 0.0       | 0.0    | 0.0    | 0.9838   |
| 0.0834        | 0.23  | 75   | 0.1037          | 0.0       | 0.0    | 0.0    | 0.9838   |
| 0.0868        | 0.31  | 100  | 0.0682          | 0.0       | 0.0    | 0.0    | 0.9838   |
| 0.0569        | 0.39  | 125  | 0.0630          | 0.0       | 0.0    | 0.0    | 0.9838   |
| 0.0612        | 0.46  | 150  | 0.0595          | 0.0       | 0.0    | 0.0    | 0.9838   |
| 0.0551        | 0.54  | 175  | 0.0507          | 0.3790    | 0.3046 | 0.3378 | 0.9841   |
| 0.0347        | 0.62  | 200  | 0.0504          | 0.5773    | 0.2403 | 0.3393 | 0.9859   |
| 0.0472        | 0.7   | 225  | 0.0474          | 0.5132    | 0.3795 | 0.4363 | 0.9862   |
| 0.0374        | 0.77  | 250  | 0.0444          | 0.5509    | 0.3039 | 0.3917 | 0.9857   |
| 0.0525        | 0.85  | 275  | 0.0383          | 0.6984    | 0.4281 | 0.5309 | 0.9882   |
| 0.0406        | 0.93  | 300  | 0.0389          | 0.6280    | 0.5105 | 0.5632 | 0.9885   |
| 0.0347        | 1.01  | 325  | 0.0404          | 0.5134    | 0.6003 | 0.5535 | 0.9873   |
| 0.0333        | 1.08  | 350  | 0.0415          | 0.6409    | 0.4289 | 0.5139 | 0.9880   |
| 0.0267        | 1.16  | 375  | 0.0377          | 0.6010    | 0.4543 | 0.5175 | 0.9885   |
| 0.0406        | 1.24  | 400  | 0.0397          | 0.6406    | 0.6310 | 0.6357 | 0.9889   |
| 0.0252        | 1.32  | 425  | 0.0364          | 0.5946    | 0.5599 | 0.5767 | 0.9882   |
| 0.0351        | 1.39  | 450  | 0.0343          | 0.6632    | 0.6130 | 0.6371 | 0.9895   |
| 0.0304        | 1.47  | 475  | 0.0378          | 0.6756    | 0.5846 | 0.6268 | 0.9896   |
| 0.0242        | 1.55  | 500  | 0.0342          | 0.7061    | 0.5988 | 0.6480 | 0.9904   |
| 0.0291        | 1.63  | 525  | 0.0327          | 0.5841    | 0.6497 | 0.6152 | 0.9894   |
| 0.0307        | 1.7   | 550  | 0.0333          | 0.5815    | 0.5823 | 0.5819 | 0.9890   |
| 0.0278        | 1.78  | 575  | 0.0316          | 0.6654    | 0.6579 | 0.6616 | 0.9904   |
| 0.0293        | 1.86  | 600  | 0.0335          | 0.7969    | 0.4933 | 0.6093 | 0.9898   |
| 0.0241        | 1.93  | 625  | 0.0322          | 0.6473    | 0.6991 | 0.6722 | 0.9901   |
| 0.0288        | 2.01  | 650  | 0.0300          | 0.7431    | 0.5846 | 0.6544 | 0.9906   |
| 0.0192        | 2.09  | 675  | 0.0329          | 0.6908    | 0.7006 | 0.6957 | 0.9910   |
| 0.0184        | 2.17  | 700  | 0.0326          | 0.6788    | 0.6407 | 0.6592 | 0.9897   |
| 0.0171        | 2.24  | 725  | 0.0325          | 0.7131    | 0.6864 | 0.6995 | 0.9911   |
| 0.0161        | 2.32  | 750  | 0.0336          | 0.7138    | 0.6647 | 0.6884 | 0.9910   |
| 0.0218        | 2.4   | 775  | 0.0306          | 0.6892    | 0.6954 | 0.6923 | 0.9904   |
| 0.0209        | 2.48  | 800  | 0.0310          | 0.6947    | 0.7305 | 0.7121 | 0.9911   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "multiCorp_5e-05_LabelNorm_0404", "results": []}]}
transformers pytorch bert token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

multiCorp\_5e-05\_LabelNorm\_0404
=================================


This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0310
* Precision: 0.6947
* Recall: 0.7305
* F1: 0.7121
* Accuracy: 0.9911


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: 5e-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
* training\_steps: 2000


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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
* training\_steps: 2000 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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
* training\_steps: 2000 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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
* training\_steps: 2000### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3