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# distilbert-base-uncased-wandb-week-3-complaints-classifier-512

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: 1.0839
- Accuracy: 0.6745
- F1: 0.6356
- Recall: 0.6745
- Precision: 0.6122

## 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: 0.0007879237562376572
- 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: 512
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 1.2707        | 0.61  | 1500 | 1.3009          | 0.6381   | 0.5848 | 0.6381 | 0.5503    |
| 1.1348        | 1.22  | 3000 | 1.1510          | 0.6610   | 0.6178 | 0.6610 | 0.5909    |
| 1.0649        | 1.83  | 4500 | 1.0839          | 0.6745   | 0.6356 | 0.6745 | 0.6122    |


### 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": "distilbert-base-uncased-wandb-week-3-complaints-classifier-512", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "consumer-finance-complaints", "type": "consumer-finance-complaints", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6745323887671373, "name": "Accuracy"}, {"type": "f1", "value": 0.6355967633316707, "name": "F1"}, {"type": "recall", "value": 0.6745323887671373, "name": "Recall"}, {"type": "precision", "value": 0.6122130681567332, "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-base-uncased-wandb-week-3-complaints-classifier-512
==============================================================


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: 1.0839
* Accuracy: 0.6745
* F1: 0.6356
* Recall: 0.6745
* Precision: 0.6122


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: 0.0007879237562376572
* 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: 512
* 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: 0.0007879237562376572
* 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: 512
* 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 #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: 0.0007879237562376572
* 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: 512
* 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
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: 0.0007879237562376572
* 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: 512
* 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