
# distilbert_rotten_tomatoes_sentiment_classifier

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the rotten_tomatoes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7927
- Accuracy: 0.8386

## Model description

The goal was to fine-tune a model on the rotten_tomatoes dataset to showcase an end-to-end workflow using the Hugging face library. As such, only the bare minimum  of pre-processing was used.

## Intended uses & limitations

The model will be used as part of a blog post to help others engineers better understand what natural language processing is and how to perform a text classification.

## Training and evaluation data

The model was evaluated using the accuracy metric that form part of the Hugging Face library.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 134  | 0.5940          | 0.8340   |
| No log        | 2.0   | 268  | 0.7095          | 0.8227   |
| No log        | 3.0   | 402  | 0.7276          | 0.8321   |
| 0.065         | 4.0   | 536  | 0.7693          | 0.8415   |
| 0.065         | 5.0   | 670  | 0.7927          | 0.8386   |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["rotten_tomatoes"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "outputs", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "rotten_tomatoes", "type": "rotten_tomatoes", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8386491557223265, "name": "Accuracy"}]}]}]}
transformers pytorch safetensors distilbert text-classification generated_from_trainer dataset:rotten_tomatoes base_model:distilbert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #generated_from_trainer #dataset-rotten_tomatoes #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

distilbert\_rotten\_tomatoes\_sentiment\_classifier
===================================================


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


* Loss: 0.7927
* Accuracy: 0.8386


Model description
-----------------


The goal was to fine-tune a model on the rotten\_tomatoes dataset to showcase an end-to-end workflow using the Hugging face library. As such, only the bare minimum of pre-processing was used.


Intended uses & limitations
---------------------------


The model will be used as part of a blog post to help others engineers better understand what natural language processing is and how to perform a text classification.


Training and evaluation data
----------------------------


The model was evaluated using the accuracy metric that form part of the Hugging Face library.


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* 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 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* 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 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #generated_from_trainer #dataset-rotten_tomatoes #base_model-distilbert-base-uncased #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: 64
* eval\_batch\_size: 64
* 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 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #generated_from_trainer #dataset-rotten_tomatoes #base_model-distilbert-base-uncased #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: 64
* eval\_batch\_size: 64
* 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 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1