
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# code-vs-nl

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 
on [bookcorpus](https://huggingface.co/datasets/bookcorpus) for text and [codeparrot/github-code](https://huggingface.co/datasets/codeparrot/github-code) for code datasets.
It achieves the following results on the evaluation set:
- Loss: 0.5180
- Accuracy: 0.9951
- F1 Score: 0.9950

## Model description

As it's a finetuned model, it's architecture is same as distilbert-base-uncased for Sequence Classification

## Intended uses & limitations

Can be used to classify documents into text and code

## Training and evaluation data

It is a mix of above two datasets, equally random sampled

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-07
- train_batch_size: 256
- eval_batch_size: 1024
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.5732        | 0.07  | 500  | 0.5658          | 0.9934   | 0.9934   |
| 0.5254        | 0.14  | 1000 | 0.5180          | 0.9951   | 0.9950   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["bookcorpus", "codeparrot/github-code"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "code-vs-nl", "results": []}]}
transformers pytorch tensorboard safetensors distilbert text-classification generated_from_trainer en dataset:bookcorpus dataset:codeparrot/github-code base_model:distilbert-base-uncased license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #en #dataset-bookcorpus #dataset-codeparrot/github-code #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

code-vs-nl
==========


This model is a fine-tuned version of distilbert-base-uncased
on bookcorpus for text and codeparrot/github-code for code datasets.
It achieves the following results on the evaluation set:


* Loss: 0.5180
* Accuracy: 0.9951
* F1 Score: 0.9950


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


As it's a finetuned model, it's architecture is same as distilbert-base-uncased for Sequence Classification


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


Can be used to classify documents into text and code


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


It is a mix of above two datasets, equally random sampled


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


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-07
* train\_batch\_size: 256
* eval\_batch\_size: 1024
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 1000


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-07
* train\_batch\_size: 256
* eval\_batch\_size: 1024
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 1000 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #en #dataset-bookcorpus #dataset-codeparrot/github-code #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-07
* train\_batch\_size: 256
* eval\_batch\_size: 1024
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 1000 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #en #dataset-bookcorpus #dataset-codeparrot/github-code #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-07
* train\_batch\_size: 256
* eval\_batch\_size: 1024
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 1000### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2