
<!-- 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_cut_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: 0.0722
- Precision: 0.7331
- Recall: 0.6101
- F1: 0.6660
- Accuracy: 0.9851

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2942        | 0.66  | 50   | 0.0969          | 0.4120    | 0.1031 | 0.1649 | 0.9742   |
| 0.0781        | 1.32  | 100  | 0.0720          | 0.4497    | 0.2933 | 0.3551 | 0.9779   |
| 0.0673        | 1.97  | 150  | 0.0632          | 0.5067    | 0.4227 | 0.4609 | 0.9794   |
| 0.046         | 2.63  | 200  | 0.0617          | 0.5822    | 0.4780 | 0.5250 | 0.9814   |
| 0.0426        | 3.29  | 250  | 0.0607          | 0.6815    | 0.5033 | 0.5790 | 0.9833   |
| 0.0365        | 3.95  | 300  | 0.0596          | 0.6222    | 0.5558 | 0.5871 | 0.9823   |
| 0.026         | 4.61  | 350  | 0.0632          | 0.6341    | 0.6026 | 0.6180 | 0.9829   |
| 0.0224        | 5.26  | 400  | 0.0700          | 0.6951    | 0.5576 | 0.6188 | 0.9838   |
| 0.0197        | 5.92  | 450  | 0.0621          | 0.6834    | 0.5989 | 0.6384 | 0.9840   |
| 0.0147        | 6.58  | 500  | 0.0676          | 0.7181    | 0.6064 | 0.6575 | 0.9845   |
| 0.0119        | 7.24  | 550  | 0.0689          | 0.6965    | 0.6560 | 0.6757 | 0.9846   |
| 0.0106        | 7.89  | 600  | 0.0722          | 0.7331    | 0.6101 | 0.6660 | 0.9851   |


### 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_cut_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\_cut\_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: 0.0722
* Precision: 0.7331
* Recall: 0.6101
* F1: 0.6660
* Accuracy: 0.9851


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