
# ernie-2.0-base-en-Tweet_About_Disaster_Or_Not

This model is a fine-tuned version of [nghuyong/ernie-2.0-base-en](https://huggingface.co/nghuyong/ernie-2.0-base-en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2292
- Accuracy: 0.9156
- F1: 0.7876
- Recall: 0.8436
- Precision: 0.7386

## Model description

This is a binary classification model to determine if tweet input samples are about a disaster or not.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Binary%20Classification/Transformer%20Comparison/Is%20This%20Tweet%20Referring%20to%20a%20Disaster%20or%20Not%3F%20-%20ERNIE.ipynb

### Associated Projects
This project is part of a comparison of multiple transformers. The others can be found at the following links:

- https://huggingface.co/DunnBC22/roberta-base-Tweet_About_Disaster_Or_Not
- https://huggingface.co/DunnBC22/deberta-v3-small-Tweet_About_Disaster_Or_Not
- https://huggingface.co/DunnBC22/albert-base-v2-Tweet_About_Disaster_Or_Not
- https://huggingface.co/DunnBC22/electra-base-emotion-Tweet_About_Disaster_Or_Not
- https://huggingface.co/DunnBC22/distilbert-base-uncased-Tweet_About_Disaster_Or_Not

## Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

The main limitation is the quality of the data source.

## Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/vstepanenko/disaster-tweets

_Input Word Length By Class:_

![Length of Input Text (in Words) By Class](https://github.com/DunnBC22/NLP_Projects/raw/main/Binary%20Classification/Transformer%20Comparison/Images/Tweet%20Word%20Lengths%20By%20Class.png)

## 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 | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.347         | 1.0   | 143  | 0.2663          | 0.8777   | 0.7342 | 0.9100 | 0.6154    |
| 0.2192        | 2.0   | 286  | 0.2292          | 0.9156   | 0.7876 | 0.8436 | 0.7386    |
| 0.132         | 3.0   | 429  | 0.2629          | 0.9129   | 0.7843 | 0.8531 | 0.7258    |
| 0.0767        | 4.0   | 572  | 0.3266          | 0.9120   | 0.7807 | 0.8436 | 0.7265    |
| 0.0532        | 5.0   | 715  | 0.3622          | 0.9120   | 0.7788 | 0.8341 | 0.7303    |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.12.1
{"language": ["en"], "license": "cc", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "recall", "precision"], "model-index": [{"name": "ernie-2.0-base-en-Tweet_About_Disaster_Or_Not", "results": []}]}
transformers pytorch tensorboard ernie text-classification generated_from_trainer en license:cc autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #ernie #text-classification #generated_from_trainer #en #license-cc #autotrain_compatible #endpoints_compatible #region-us 

ernie-2.0-base-en-Tweet\_About\_Disaster\_Or\_Not
=================================================


This model is a fine-tuned version of nghuyong/ernie-2.0-base-en on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2292
* Accuracy: 0.9156
* F1: 0.7876
* Recall: 0.8436
* Precision: 0.7386


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


This is a binary classification model to determine if tweet input samples are about a disaster or not.


For more information on how it was created, check out the following link: URL


### Associated Projects


This project is part of a comparison of multiple transformers. The others can be found at the following links:


* URL
* URL
* URL
* URL
* URL


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


This model is intended to demonstrate my ability to solve a complex problem using technology.


The main limitation is the quality of the data source.


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


Dataset Source: URL


*Input Word Length By Class:*


!Length of Input Text (in Words) By Class


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 4.26.1
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.12.1



### Associated Projects


This project is part of a comparison of multiple transformers. The others can be found at the following links:


* URL
* URL
* URL
* URL
* URL


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


This model is intended to demonstrate my ability to solve a complex problem using technology.


The main limitation is the quality of the data source.


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


Dataset Source: URL


*Input Word Length By Class:*


!Length of Input Text (in Words) By Class


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 4.26.1
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #ernie #text-classification #generated_from_trainer #en #license-cc #autotrain_compatible #endpoints_compatible #region-us 
 ### Associated Projects


This project is part of a comparison of multiple transformers. The others can be found at the following links:


* URL
* URL
* URL
* URL
* URL


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


This model is intended to demonstrate my ability to solve a complex problem using technology.


The main limitation is the quality of the data source.


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


Dataset Source: URL


*Input Word Length By Class:*


!Length of Input Text (in Words) By Class


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 4.26.1
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.12.1