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# distilbert-base-uncased-finetuned-amazon-fine-food-lite

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an lite-version of the Amazon fine food Review dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8019
- Accuracy: 0.8097
- F1: 0.8286

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.41  | 100  | 0.6570          | 0.7479   | 0.7815 |
| No log        | 0.82  | 200  | 0.5237          | 0.7914   | 0.8213 |
| 0.7333        | 1.23  | 300  | 0.5004          | 0.8042   | 0.8343 |
| 0.7333        | 1.65  | 400  | 0.5946          | 0.7715   | 0.8107 |
| 0.4693        | 2.06  | 500  | 0.5146          | 0.8124   | 0.8355 |
| 0.4693        | 2.47  | 600  | 0.5877          | 0.7950   | 0.8231 |
| 0.4693        | 2.88  | 700  | 0.6603          | 0.7855   | 0.8177 |
| 0.3151        | 3.29  | 800  | 0.5712          | 0.8150   | 0.8365 |
| 0.3151        | 3.7   | 900  | 0.6085          | 0.8081   | 0.8335 |
| 0.208         | 4.12  | 1000 | 0.7475          | 0.7807   | 0.8151 |
| 0.208         | 4.53  | 1100 | 0.6621          | 0.8214   | 0.8435 |
| 0.208         | 4.94  | 1200 | 0.6542          | 0.8229   | 0.8425 |
| 0.1479        | 5.35  | 1300 | 0.7987          | 0.7948   | 0.8229 |
| 0.1479        | 5.76  | 1400 | 0.7361          | 0.8118   | 0.8346 |
| 0.1           | 6.17  | 1500 | 0.8019          | 0.8097   | 0.8286 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["kfuangsung/AmazonFineFoodReviews"], "metrics": ["accuracy", "f1"], "widget": [{"text": "Came a lot quicker than expected and is as good as new , happy with this purchase so far", "example_title": "5/5 stars"}, {"text": "Mattel are killing it with these Masters Of The Universe Origins figures. It's a simple enough premise; re-release the '80s toys, but give them modern articulation. Nice. Man-At-Arms here comes fully moustached (unlike the original toy) and is bright and vibrant. Plastics are good and the armour's removable like in the old days. I love these chunky and simple old-school designs; they're much more fun than the po-faced 'serious' Collector -aimed toylines Mattel have done. Duncan here (or is that Dun-Can?) can strike some good poses and you can pop him apart to build your own wacky figures - er. assuming you've bought others in the line. Good fun!", "example_title": "4/5 stars"}, {"text": "Bought this as it was quite reasonably priced for premium oil. However when the weather got cold i started to experience lifter tick on my mercedes om651 engine on cold starts. The tick goes away after the lifters have pumped but i didn't have this issue before. Will be going back to the factory mercedes oil thats much cheaper as well.", "example_title": "3/5 star"}, {"text": "I thought this book would be right up my street but I got very bored after 100 pages. The story never starts, it reads like the biography of a random and rather annoying woman. I didn't like the characters as they are not 3 dimensional and reading through various episodes of Zott's life wasn't a very interesting read. If it was meant to be funny, I missed it too and gave up half-way.", "example_title": "2/5 stars"}, {"text": "Nothing but trouble, had to phone Norton several times to get product set up allowing them to take control of my device (I don\u2019t like doing this) Difficulty using extra licences for family. Don\u2019t like the auto renewal they apply. Don\u2019t like the stupid security check they use for passwords i.e. grid asking you to identify traffic signals, stairs, bicycles, etc. The whole thing has become a pain. Having used Norton for some years will no longer use them in future.", "example_title": "1/5 stars"}], "model-index": [{"name": "distilbert-base-uncased-finetuned-amazon-fine-food-lite", "results": []}]}
transformers pytorch tensorboard distilbert text-classification generated_from_trainer en dataset:kfuangsung/AmazonFineFoodReviews license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #en #dataset-kfuangsung/AmazonFineFoodReviews #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased-finetuned-amazon-fine-food-lite
=======================================================


This model is a fine-tuned version of distilbert-base-uncased on an lite-version of the Amazon fine food Review dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8019
* Accuracy: 0.8097
* F1: 0.8286


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: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #en #dataset-kfuangsung/AmazonFineFoodReviews #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: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.1+cu118
* Tokenizers 0.13.3