roberta-base-ca-finetuned-catalonia-independence-detector

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

Loss: 0.6065
Accuracy: 0.7612

Training and evaluation data

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	377	0.6311	0.7453
0.7393	2.0	754	0.6065	0.7612
0.5019	3.0	1131	0.6340	0.7547
0.3837	4.0	1508	0.6777	0.7597
0.3837	5.0	1885	0.7232	0.7582

</details>

Model in action 🚀

Fast usage with pipelines:


from transformers import pipeline

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

independence_analysis(
    "Assegura l'expert que en un 46% els catalans s'inclouen dins del que es denomina com el doble sentiment identitari. És a dir, se senten tant catalans com espanyols. 1 de cada cinc, en canvi, té un sentiment excloent, només se senten catalans, i un 4% sol espanyol."
    )
# Output:
[{'label': 'AGAINST', 'score': 0.7457581758499146}]

independence_analysis(
    "Llarena demana la detenció de Comín i Ponsatí aprofitant que són a Itàlia amb Puigdemont"
    )
# Output:
[{'label': 'NEUTRAL', 'score': 0.7436802983283997}] 
    
independence_analysis(
    "Puigdemont, a l'estat espanyol: Quatre anys després, ens hem guanyat el dret a dir prou"
    )
# Output:
[{'label': 'FAVOR', 'score': 0.9040119647979736}]

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 for Dataset ;)

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

Created by Jonatan Luna | LinkedIn