
# roberta-base-bne-finetuned-catalonia-independence-detector

This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the catalonia_independence dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9415
- Accuracy: 0.7881

<details>

## Model description

The data was collected over 12 days during February and March of 2019 from tweets posted in Barcelona, and during September of 2018 from tweets posted in the town of Terrassa, Catalonia.
Each corpus is annotated with three classes: AGAINST, FAVOR and NEUTRAL, which express the stance towards the target - independence of Catalonia.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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   | 378  | 0.5534          | 0.7558   |
| 0.6089        | 2.0   | 756  | 0.5315          | 0.7643   |
| 0.2678        | 3.0   | 1134 | 0.7336          | 0.7816   |
| 0.0605        | 4.0   | 1512 | 0.8809          | 0.7866   |
| 0.0605        | 5.0   | 1890 | 0.9415          | 0.7881   |

</details>

### Model in action 🚀

Fast usage with **pipelines**:

```python

from transformers import pipeline

model_path = "JonatanGk/roberta-base-bne-finetuned-catalonia-independence-detector"
independence_analysis = pipeline("text-classification", model=model_path, tokenizer=model_path)

independence_analysis(
    "Junqueras, sobre la decisión judicial sobre Puigdemont: La justicia que falta en el Estado llega y llegará de Europa"
    )

# Output:
[{'label': 'FAVOR', 'score': 0.9936726093292236}]

independence_analysis(
    "El desafío independentista queda adormecido, y eso que el Gobierno ha sido muy claro en que su propuesta para Cataluña es una agenda de reencuentro, centrada en inversiones e infraestructuras")

# Output:
[{'label': 'AGAINST', 'score': 0.7508948445320129}]

independence_analysis(
    "Desconvocada la manifestación del domingo en Barcelona en apoyo a Puigdemont"
    )

# Output:
[{'label': 'NEUTRAL', 'score': 0.9966907501220703}]

```

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JonatanGk/Shared-Colab/blob/master/Catalonia_independence_Detector_(SPANISH).ipynb#scrollTo=uNMOXJz38W6U)

### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3

## Citation
Thx to HF.co & [@lewtun](https://github.com/lewtun) for Dataset ;)


> Special thx to [Manuel Romero/@mrm8488](https://huggingface.co/mrm8488) as my mentor & R.C.

> Created by [Jonatan Luna](https://JonatanGk.github.io) | [LinkedIn](https://www.linkedin.com/in/JonatanGk/)
{"language": "es", "license": "apache-2.0", "tags": ["spanish"], "datasets": ["catalonia_independence"], "metrics": ["accuracy"], "widget": [{"text": "Junqueras, sobre la decisi\u00f3n judicial sobre Puigdemont: La justicia que falta en el Estado llega y llegar\u00e1 de Europa"}, {"text": "Desconvocada la manifestaci\u00f3n del domingo en Barcelona en apoyo a Puigdemont"}], "model-index": [{"name": "roberta-base-bne-finetuned-mnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "catalonia_independence", "type": "catalonia_independence", "args": "spanish"}, "metrics": [{"type": "accuracy", "value": 0.7880893300248138, "name": "Accuracy"}]}, {"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "catalonia_independence", "type": "catalonia_independence", "config": "catalan", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.4592039800995025, "name": "Accuracy", "verified": true}, {"type": "precision", "value": 0.6104489964825159, "name": "Precision Macro", "verified": true}, {"type": "precision", "value": 0.4592039800995025, "name": "Precision Micro", "verified": true}, {"type": "precision", "value": 0.6167123723406555, "name": "Precision Weighted", "verified": true}, {"type": "recall", "value": 0.4146479268294389, "name": "Recall Macro", "verified": true}, {"type": "recall", "value": 0.4592039800995025, "name": "Recall Micro", "verified": true}, {"type": "recall", "value": 0.4592039800995025, "name": "Recall Weighted", "verified": true}, {"type": "f1", "value": 0.33416407167650636, "name": "F1 Macro", "verified": true}, {"type": "f1", "value": 0.4592039800995025, "name": "F1 Micro", "verified": true}, {"type": "f1", "value": 0.34549318538357193, "name": "F1 Weighted", "verified": true}, {"type": "loss", "value": 3.393402099609375, "name": "loss", "verified": true}]}]}]}
transformers pytorch tensorboard roberta text-classification spanish es dataset:catalonia_independence license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #spanish #es #dataset-catalonia_independence #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

roberta-base-bne-finetuned-catalonia-independence-detector
==========================================================


This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the catalonia\_independence dataset.
It achieves the following results on the evaluation set:


* Loss: 0.9415
* Accuracy: 0.7881



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


The data was collected over 12 days during February and March of 2019 from tweets posted in Barcelona, and during September of 2018 from tweets posted in the town of Terrassa, Catalonia.
Each corpus is annotated with three classes: AGAINST, FAVOR and NEUTRAL, which express the stance towards the target - independence of Catalonia.


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


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5


### Training results




### Model in action


Fast usage with pipelines:


![Open In Colab](URL


### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.0+cu111
* Datasets 1.12.1
* Tokenizers 0.10.3


Thx to URL & @lewtun for Dataset ;)



> 
> Special thx to Manuel Romero/@mrm8488 as my mentor & R.C.
> 
> 
> 



> 
> Created by Jonatan Luna | LinkedIn
> 
> 
> 



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Model in action


Fast usage with pipelines:


![Open In Colab](URL ### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.0+cu111
* Datasets 1.12.1
* Tokenizers 0.10.3


Thx to URL & @lewtun for Dataset ;)



> 
> Special thx to Manuel Romero/@mrm8488 as my mentor & R.C.
> 
> 
> 



> 
> Created by Jonatan Luna | LinkedIn
> 
> 
>
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #spanish #es #dataset-catalonia_independence #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5 ### Training results ### Model in action


Fast usage with pipelines:


![Open In Colab](URL ### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.0+cu111
* Datasets 1.12.1
* Tokenizers 0.10.3


Thx to URL & @lewtun for Dataset ;)



> 
> Special thx to Manuel Romero/@mrm8488 as my mentor & R.C.
> 
> 
> 



> 
> Created by Jonatan Luna | LinkedIn
> 
> 
>
passage: TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #spanish #es #dataset-catalonia_independence #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5### Training results### Model in action


Fast usage with pipelines:


![Open In Colab](URL### Framework versions


* Transformers 4.11.3
* Pytorch 1.9.0+cu111
* Datasets 1.12.1
* Tokenizers 0.10.3


Thx to URL & @lewtun for Dataset ;)



> 
> Special thx to Manuel Romero/@mrm8488 as my mentor & R.C.
> 
> 
> 



> 
> Created by Jonatan Luna | LinkedIn
> 
> 
>