

# bert-model-disaster-tweets-classification

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the Natural-Language-Processing-with-Disaster-Tweets dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.82
- F1 Score: 0.82

## Model description

Load BertForSequenceClassification, the pretrained BERT model with a single linear classification layer on top, using an optimizer : incorporates weight decay, which is a regularization technique that helps prevent overfitting during training.

## Intended uses & limitations

Use to classify if a tweet represents a disaster or not.

### 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 epsilon = 1e-8.
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Epoch | Average training loss | Training epoch  | Accuracy | F1   |
|:-----:|:---------------------:|:---------------:|:--------:|:----:|
| 1.0   | 0.47                  | 0:00:49         | 0.82     | 0.82 |
| 2.0   | 0.36                  | 0:00:36         | 0.82     | 0.82 |
| 3.0   | 0.29                  | 0:00:51         | 0.82     | 0.82 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "afl-3.0", "library_name": "sklearn", "tags": ["clim"], "metrics": ["accuracy", "code_eval"], "pipeline_tag": "text-classification"}
sklearn clim text-classification license:afl-3.0 region:us
TAGS
#sklearn #clim #text-classification #license-afl-3.0 #region-us 

bert-model-disaster-tweets-classification
=========================================


This model is a fine-tuned version of bert-base-uncased on the Natural-Language-Processing-with-Disaster-Tweets dataset.
It achieves the following results on the evaluation set:


* Accuracy: 0.82
* F1 Score: 0.82


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


Load BertForSequenceClassification, the pretrained BERT model with a single linear classification layer on top, using an optimizer : incorporates weight decay, which is a regularization technique that helps prevent overfitting during training.


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


Use to classify if a tweet represents a disaster or not.


### 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 epsilon = 1e-8.
* lr\_scheduler\_type: linear
* num\_epochs: 3


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* 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 epsilon = 1e-8.
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#sklearn #clim #text-classification #license-afl-3.0 #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 epsilon = 1e-8.
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3