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# moderate_severe_depression_model

This model is a fine-tuned version of [allenai/longformer-scico](https://huggingface.co/allenai/longformer-scico) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4276
- Macro F1: 0.8927
- Accuracy: 0.8932

Results on Test set:

-Accuracy: 0.8817204301075269

-F1 score: 0.8819253137510324 

-Precision: 0.8855477220587717

-Recall : 0.8817204301075269

-Matthews Correlation Coefficient: 0.8242972089300715

-Precision of each class: [0.98420129 0.85636693 0.81607495]

-Recall of each class: [0.93548387 0.78921023 0.92046719]

-F1 score of each class: [0.95922441 0.82141823 0.8651333 ]

## 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: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.3476        | 1.0   | 1798 | 0.3343          | 0.8765   | 0.8782   |
| 0.2658        | 2.0   | 3596 | 0.3190          | 0.8856   | 0.8859   |
| 0.2157        | 3.0   | 5394 | 0.3607          | 0.8938   | 0.8939   |
| 0.1749        | 4.0   | 7192 | 0.4276          | 0.8927   | 0.8932   |


### Framework versions

- Transformers 4.27.1
- Pytorch 2.0.1+cu118
- Datasets 2.9.0
- Tokenizers 0.13.3
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "code"], "datasets": ["christinacdl/balanced_depression_dataset"], "metrics": ["accuracy"], "pipeline_tag": "text-classification", "model-index": [{"name": "moderate_severe_depression_model", "results": []}]}
transformers pytorch tensorboard longformer text-classification generated_from_trainer code en dataset:christinacdl/balanced_depression_dataset license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #longformer #text-classification #generated_from_trainer #code #en #dataset-christinacdl/balanced_depression_dataset #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

moderate\_severe\_depression\_model
===================================


This model is a fine-tuned version of allenai/longformer-scico on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4276
* Macro F1: 0.8927
* Accuracy: 0.8932


Results on Test set:


-Accuracy: 0.8817204301075269


-F1 score: 0.8819253137510324


-Precision: 0.8855477220587717


-Recall : 0.8817204301075269


-Matthews Correlation Coefficient: 0.8242972089300715


-Precision of each class: [0.98420129 0.85636693 0.81607495]


-Recall of each class: [0.93548387 0.78921023 0.92046719]


-F1 score of each class: [0.95922441 0.82141823 0.8651333 ]


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: 6
* eval\_batch\_size: 6
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 12
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.1
* Pytorch 2.0.1+cu118
* Datasets 2.9.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 6
* eval\_batch\_size: 6
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 12
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 2.0.1+cu118
* Datasets 2.9.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #longformer #text-classification #generated_from_trainer #code #en #dataset-christinacdl/balanced_depression_dataset #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 6
* eval\_batch\_size: 6
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 12
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 2.0.1+cu118
* Datasets 2.9.0
* Tokenizers 0.13.3