
<!-- 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. -->

# disaster-tweet-5

This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on a [Kaggle competition](https://www.kaggle.com/competitions/nlp-getting-started/data) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4107

## Model description

More information needed

## Intended uses & limitations

This model was created for the [Natural Language Processing with Disaster Tweets](https://www.kaggle.com/competitions/nlp-getting-started) Kaggle competition.

## Training and evaluation data

Information about the data can be found [here](https://www.kaggle.com/competitions/nlp-getting-started/data)

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-5
- train_batch_size: 64
- eval_batch_size: 64
- seed: 7
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7065        | 0.12  | 12   | 0.7027          |
| 0.7021        | 0.25  | 24   | 0.6997          |
| 0.7048        | 0.38  | 36   | 0.6948          |
| 0.7025        | 0.5   | 48   | 0.6887          |
| 0.683         | 0.62  | 60   | 0.6808          |
| 0.6748        | 0.75  | 72   | 0.6715          |
| 0.6701        | 0.88  | 84   | 0.6610          |
| 0.6564        | 1.0   | 96   | 0.6487          |
| 0.6418        | 1.12  | 108  | 0.6341          |
| 0.6268        | 1.25  | 120  | 0.6165          |
| 0.6362        | 1.38  | 132  | 0.5985          |
| 0.5824        | 1.5   | 144  | 0.5776          |
| 0.5766        | 1.62  | 156  | 0.5541          |
| 0.5417        | 1.75  | 168  | 0.5281          |
| 0.5232        | 1.88  | 180  | 0.5064          |
| 0.4737        | 2.0   | 192  | 0.4909          |
| 0.4479        | 2.12  | 204  | 0.4826          |
| 0.456         | 2.25  | 216  | 0.4662          |
| 0.4718        | 2.38  | 228  | 0.4541          |
| 0.4198        | 2.5   | 240  | 0.4451          |
| 0.4333        | 2.62  | 252  | 0.4376          |
| 0.4086        | 2.75  | 264  | 0.4337          |
| 0.4419        | 2.88  | 276  | 0.4332          |
| 0.3857        | 3.0   | 288  | 0.4225          |
| 0.3878        | 3.12  | 300  | 0.4188          |
| 0.3578        | 3.25  | 312  | 0.4280          |
| 0.3562        | 3.38  | 324  | 0.4234          |
| 0.4125        | 3.5   | 336  | 0.4147          |
| 0.3882        | 3.62  | 348  | 0.4090          |
| 0.3751        | 3.75  | 360  | 0.4145          |
| 0.3892        | 3.88  | 372  | 0.4077          |
| 0.3946        | 4.0   | 384  | 0.4107          |


### Framework versions

- Transformers 4.28.0
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "model-index": [{"name": "disaster-tweet-5", "results": []}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

disaster-tweet-5
================


This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment on a Kaggle competition dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4107


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


More information needed


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


This model was created for the Natural Language Processing with Disaster Tweets Kaggle competition.


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


Information about the data can be found here


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


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-5
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 7
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-5
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 7
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-5
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 7
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-5
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 7
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4### Training results### Framework versions


* Transformers 4.28.0
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2