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# distilbert-base-uncased-finetuned-amazon-review

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3494
- Accuracy: 0.693
- F1: 0.7003
- Precision: 0.7095
- Recall: 0.693

## 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: 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 0.5   | 500  | 0.8287          | 0.7104   | 0.7120 | 0.7152    | 0.7104 |
| 0.4238        | 1.0   | 1000 | 0.8917          | 0.7094   | 0.6989 | 0.6917    | 0.7094 |
| 0.4238        | 1.5   | 1500 | 0.9367          | 0.6884   | 0.6983 | 0.7151    | 0.6884 |
| 0.3152        | 2.0   | 2000 | 0.9845          | 0.7116   | 0.7144 | 0.7176    | 0.7116 |
| 0.3152        | 2.5   | 2500 | 1.0752          | 0.6814   | 0.6968 | 0.7232    | 0.6814 |
| 0.2454        | 3.0   | 3000 | 1.1215          | 0.6918   | 0.6954 | 0.7068    | 0.6918 |
| 0.2454        | 3.5   | 3500 | 1.2905          | 0.6976   | 0.7048 | 0.7138    | 0.6976 |
| 0.1989        | 4.0   | 4000 | 1.2938          | 0.694    | 0.7016 | 0.7113    | 0.694  |
| 0.1989        | 4.5   | 4500 | 1.3623          | 0.6972   | 0.7014 | 0.7062    | 0.6972 |
| 0.1746        | 5.0   | 5000 | 1.3494          | 0.693    | 0.7003 | 0.7095    | 0.693  |


### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "distilbert-base-uncased-finetuned-amazon-review", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "amazon_reviews_multi", "type": "amazon_reviews_multi", "args": "es"}, "metrics": [{"type": "accuracy", "value": 0.693, "name": "Accuracy"}, {"type": "f1", "value": 0.7002653469272611, "name": "F1"}, {"type": "precision", "value": 0.709541681233075, "name": "Precision"}, {"type": "recall", "value": 0.693, "name": "Recall"}]}]}]}
transformers pytorch tensorboard distilbert text-classification generated_from_trainer dataset:amazon_reviews_multi license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased-finetuned-amazon-review
===============================================


This model is a fine-tuned version of distilbert-base-uncased on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:


* Loss: 1.3494
* Accuracy: 0.693
* F1: 0.7003
* Precision: 0.7095
* Recall: 0.693


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: 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: 5


### Training results



### Framework versions


* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 5 ### Training results ### Framework versions


* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 5 ### Training results ### Framework versions


* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 5### Training results### Framework versions


* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3