
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# multiCorp_5e-05_250

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: 0.0691
- Precision: 0.6768
- Recall: 0.5971
- F1: 0.6344
- Accuracy: 0.9855

## 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: 1500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4757        | 0.34  | 50   | 0.1963          | 0.0       | 0.0    | 0.0    | 0.9740   |
| 0.1585        | 0.68  | 100  | 0.1299          | 0.3375    | 0.1049 | 0.1600 | 0.9758   |
| 0.1224        | 1.01  | 150  | 0.1121          | 0.3719    | 0.3094 | 0.3377 | 0.9750   |
| 0.1003        | 1.35  | 200  | 0.0954          | 0.4297    | 0.3167 | 0.3647 | 0.9791   |
| 0.0903        | 1.69  | 250  | 0.0920          | 0.4213    | 0.3063 | 0.3547 | 0.9786   |
| 0.0735        | 2.03  | 300  | 0.0795          | 0.4882    | 0.4575 | 0.4724 | 0.9814   |
| 0.0636        | 2.36  | 350  | 0.0769          | 0.5188    | 0.4718 | 0.4942 | 0.9820   |
| 0.0633        | 2.7   | 400  | 0.0737          | 0.5296    | 0.4926 | 0.5104 | 0.9823   |
| 0.0598        | 3.04  | 450  | 0.0735          | 0.5844    | 0.4320 | 0.4968 | 0.9827   |
| 0.0479        | 3.38  | 500  | 0.0730          | 0.5797    | 0.5264 | 0.5518 | 0.9831   |
| 0.0492        | 3.72  | 550  | 0.0680          | 0.6086    | 0.4978 | 0.5477 | 0.9838   |
| 0.041         | 4.05  | 600  | 0.0672          | 0.6190    | 0.5667 | 0.5917 | 0.9842   |
| 0.0371        | 4.39  | 650  | 0.0672          | 0.6616    | 0.5693 | 0.6120 | 0.9851   |
| 0.0362        | 4.73  | 700  | 0.0665          | 0.6670    | 0.5711 | 0.6153 | 0.9852   |
| 0.0334        | 5.07  | 750  | 0.0700          | 0.6532    | 0.5468 | 0.5953 | 0.9848   |
| 0.0288        | 5.41  | 800  | 0.0670          | 0.6482    | 0.5628 | 0.6025 | 0.9849   |
| 0.0288        | 5.74  | 850  | 0.0698          | 0.6643    | 0.5745 | 0.6162 | 0.9851   |
| 0.0263        | 6.08  | 900  | 0.0717          | 0.6827    | 0.5845 | 0.6298 | 0.9856   |
| 0.0231        | 6.42  | 950  | 0.0712          | 0.6826    | 0.5702 | 0.6213 | 0.9852   |
| 0.0238        | 6.76  | 1000 | 0.0691          | 0.6768    | 0.5971 | 0.6344 | 0.9855   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "multiCorp_5e-05_250", "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\_250
=====================


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: 0.0691
* Precision: 0.6768
* Recall: 0.5971
* F1: 0.6344
* Accuracy: 0.9855


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: 1500


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2



### 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: 1500 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2
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: 1500 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2
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: 1500### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2