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# AgitationTextV3

This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.1](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5199
- Accuracy: 0.8
- Precision: 0.9636
- Recall: 0.7465
- F1: 0.8413

## 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: 1e-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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6797        | 1.0   | 50   | 0.6650          | 0.57     | 0.85      | 0.4789 | 0.6126 |
| 0.599         | 2.0   | 100  | 0.6180          | 0.65     | 0.86      | 0.6056 | 0.7107 |
| 0.5121        | 3.0   | 150  | 0.5714          | 0.75     | 0.8833    | 0.7465 | 0.8092 |
| 0.4049        | 4.0   | 200  | 0.5187          | 0.81     | 0.9194    | 0.8028 | 0.8571 |
| 0.3091        | 5.0   | 250  | 0.5034          | 0.77     | 0.9444    | 0.7183 | 0.816  |
| 0.2303        | 6.0   | 300  | 0.4673          | 0.78     | 0.9298    | 0.7465 | 0.8281 |
| 0.1773        | 7.0   | 350  | 0.4802          | 0.8      | 0.9322    | 0.7746 | 0.8462 |
| 0.1396        | 8.0   | 400  | 0.5260          | 0.8      | 0.9636    | 0.7465 | 0.8413 |
| 0.1204        | 9.0   | 450  | 0.5317          | 0.8      | 0.9636    | 0.7465 | 0.8413 |
| 0.0982        | 10.0  | 500  | 0.5199          | 0.8      | 0.9636    | 0.7465 | 0.8413 |


### Framework versions

- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "AgitationTextV3", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

AgitationTextV3
===============


This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.1 on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5199
* Accuracy: 0.8
* Precision: 0.9636
* Recall: 0.7465
* F1: 0.8413


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: 1e-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: 10
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: 10
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.21.2
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1