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# distilbert-complaints-wandb-product

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the consumer-finance-complaints dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4431
- Accuracy: 0.8691
- F1: 0.8645
- Recall: 0.8691
- Precision: 0.8629

## 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: 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: 500
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.562         | 0.51  | 2000  | 0.5107          | 0.8452   | 0.8346 | 0.8452 | 0.8252    |
| 0.4548        | 1.01  | 4000  | 0.4628          | 0.8565   | 0.8481 | 0.8565 | 0.8466    |
| 0.3439        | 1.52  | 6000  | 0.4519          | 0.8605   | 0.8544 | 0.8605 | 0.8545    |
| 0.2626        | 2.03  | 8000  | 0.4412          | 0.8678   | 0.8618 | 0.8678 | 0.8626    |
| 0.2717        | 2.53  | 10000 | 0.4431          | 0.8691   | 0.8645 | 0.8691 | 0.8629    |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- 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": "distilbert-complaints-wandb-product", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "consumer-finance-complaints", "type": "consumer-finance-complaints", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8690996641956535, "name": "Accuracy"}, {"type": "f1", "value": 0.8645310918904254, "name": "F1"}, {"type": "recall", "value": 0.8690996641956535, "name": "Recall"}, {"type": "precision", "value": 0.8629318199420283, "name": "Precision"}]}]}]}
transformers pytorch distilbert text-classification generated_from_trainer dataset:consumer-finance-complaints license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-consumer-finance-complaints #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

distilbert-complaints-wandb-product
===================================


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


* Loss: 0.4431
* Accuracy: 0.8691
* F1: 0.8645
* Recall: 0.8691
* Precision: 0.8629


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: 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: 500
* num\_epochs: 3
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 500
* num\_epochs: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #distilbert #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: 5e-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: 500
* num\_epochs: 3
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #distilbert #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: 5e-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: 500
* num\_epochs: 3
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1