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# distilroberta-base-wandb-week-3-complaints-classifier-1024

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the consumer-finance-complaints dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5351
- Accuracy: 0.8280
- F1: 0.8237
- Recall: 0.8280
- Precision: 0.8236

## 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: 9.027176214786854e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1024
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.7756        | 0.61  | 1500 | 0.7411          | 0.7647   | 0.7375 | 0.7647 | 0.7606    |
| 0.5804        | 1.22  | 3000 | 0.6140          | 0.8088   | 0.8052 | 0.8088 | 0.8077    |
| 0.5008        | 1.83  | 4500 | 0.5351          | 0.8280   | 0.8237 | 0.8280 | 0.8236    |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["consumer-finance-complaints"], "metrics": ["accuracy", "f1", "recall", "precision"], "model-index": [{"name": "distilroberta-base-wandb-week-3-complaints-classifier-1024", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "consumer-finance-complaints", "type": "consumer-finance-complaints", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8279904184292339, "name": "Accuracy"}, {"type": "f1", "value": 0.8236604095677945, "name": "F1"}, {"type": "recall", "value": 0.8279904184292339, "name": "Recall"}, {"type": "precision", "value": 0.8235526237070518, "name": "Precision"}]}]}]}
transformers pytorch roberta text-classification generated_from_trainer dataset:consumer-finance-complaints license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-consumer-finance-complaints #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

distilroberta-base-wandb-week-3-complaints-classifier-1024
==========================================================


This model is a fine-tuned version of distilroberta-base on the consumer-finance-complaints dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5351
* Accuracy: 0.8280
* F1: 0.8237
* Recall: 0.8280
* Precision: 0.8236


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: 9.027176214786854e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1024
* num\_epochs: 2
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu102
* Datasets 2.3.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 9.027176214786854e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1024
* num\_epochs: 2
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu102
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-consumer-finance-complaints #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 9.027176214786854e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1024
* num\_epochs: 2
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu102
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-consumer-finance-complaints #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 9.027176214786854e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1024
* num\_epochs: 2
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu102
* Datasets 2.3.2
* Tokenizers 0.12.1