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# multiCorp_2e-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: 0.0307
- Precision: 0.7335
- Recall: 0.5525
- F1: 0.6303
- Accuracy: 0.9910

## 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
- training_steps: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4089        | 0.08  | 25   | 0.1001          | 0.0       | 0.0    | 0.0    | 0.9838   |
| 0.0776        | 0.15  | 50   | 0.0771          | 0.0       | 0.0    | 0.0    | 0.9838   |
| 0.0576        | 0.23  | 75   | 0.0631          | 0.5929    | 0.0521 | 0.0959 | 0.9843   |
| 0.0621        | 0.31  | 100  | 0.0582          | 0.2212    | 0.0179 | 0.0331 | 0.9839   |
| 0.0438        | 0.39  | 125  | 0.0505          | 0.4326    | 0.3346 | 0.3774 | 0.9859   |
| 0.047         | 0.46  | 150  | 0.0479          | 0.5205    | 0.3549 | 0.4220 | 0.9868   |
| 0.043         | 0.54  | 175  | 0.0461          | 0.5706    | 0.3144 | 0.4054 | 0.9871   |
| 0.0292        | 0.62  | 200  | 0.0437          | 0.4402    | 0.3899 | 0.4135 | 0.9865   |
| 0.0395        | 0.7   | 225  | 0.0411          | 0.5338    | 0.4669 | 0.4981 | 0.9882   |
| 0.0345        | 0.77  | 250  | 0.0414          | 0.5533    | 0.3471 | 0.4266 | 0.9869   |
| 0.0491        | 0.85  | 275  | 0.0379          | 0.6573    | 0.3447 | 0.4523 | 0.9883   |
| 0.0388        | 0.93  | 300  | 0.0370          | 0.6529    | 0.3704 | 0.4727 | 0.9884   |
| 0.0348        | 1.01  | 325  | 0.0371          | 0.5327    | 0.5191 | 0.5258 | 0.9883   |
| 0.0316        | 1.08  | 350  | 0.0363          | 0.5613    | 0.4988 | 0.5282 | 0.9884   |
| 0.0252        | 1.16  | 375  | 0.0340          | 0.6533    | 0.4957 | 0.5637 | 0.9898   |
| 0.0386        | 1.24  | 400  | 0.0367          | 0.5861    | 0.5829 | 0.5845 | 0.9889   |
| 0.0251        | 1.32  | 425  | 0.0362          | 0.6452    | 0.4444 | 0.5263 | 0.9890   |
| 0.0337        | 1.39  | 450  | 0.0348          | 0.6794    | 0.4981 | 0.5748 | 0.9896   |
| 0.0306        | 1.47  | 475  | 0.0371          | 0.7112    | 0.4350 | 0.5398 | 0.9895   |
| 0.022         | 1.55  | 500  | 0.0340          | 0.7126    | 0.5556 | 0.6244 | 0.9907   |
| 0.0292        | 1.63  | 525  | 0.0306          | 0.6797    | 0.5533 | 0.6100 | 0.9907   |
| 0.0277        | 1.7   | 550  | 0.0321          | 0.6529    | 0.5782 | 0.6133 | 0.9903   |
| 0.0295        | 1.78  | 575  | 0.0313          | 0.6564    | 0.5992 | 0.6265 | 0.9906   |
| 0.0253        | 1.86  | 600  | 0.0351          | 0.7402    | 0.4545 | 0.5632 | 0.9902   |
| 0.0228        | 1.93  | 625  | 0.0304          | 0.668     | 0.6498 | 0.6588 | 0.9910   |
| 0.0276        | 2.01  | 650  | 0.0313          | 0.6880    | 0.5183 | 0.5912 | 0.9904   |
| 0.0185        | 2.09  | 675  | 0.0325          | 0.6661    | 0.6257 | 0.6453 | 0.9907   |
| 0.0199        | 2.17  | 700  | 0.0303          | 0.6809    | 0.6459 | 0.6629 | 0.9911   |
| 0.0191        | 2.24  | 725  | 0.0307          | 0.6933    | 0.6156 | 0.6521 | 0.9910   |
| 0.0167        | 2.32  | 750  | 0.0334          | 0.6620    | 0.5930 | 0.6256 | 0.9906   |
| 0.0247        | 2.4   | 775  | 0.0317          | 0.6591    | 0.6062 | 0.6315 | 0.9902   |
| 0.0236        | 2.48  | 800  | 0.0315          | 0.7354    | 0.5798 | 0.6484 | 0.9914   |
| 0.0191        | 2.55  | 825  | 0.0367          | 0.7523    | 0.4420 | 0.5569 | 0.9900   |
| 0.0252        | 2.63  | 850  | 0.0307          | 0.7335    | 0.5525 | 0.6303 | 0.9910   |


### 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_2e-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\_2e-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: 0.0307
* Precision: 0.7335
* Recall: 0.5525
* F1: 0.6303
* Accuracy: 0.9910


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
* 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: 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
* 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: 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
* 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: 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
* training\_steps: 2000### Training results### Framework versions


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