metadata
license: apache-2.0
language: ca
tags:
- catalan
datasets:
- catalonia_independence
metrics:
- accuracy
model-index:
- name: roberta-base-ca-finetuned-mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: catalonia_independence
type: catalonia_independence
args: catalan
metrics:
- name: Accuracy
type: accuracy
value: 0.7611940298507462
- task:
type: text-classification
name: Text Classification
dataset:
name: catalonia_independence
type: catalonia_independence
config: catalan
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.7208955223880597
verified: true
- name: Precision Macro
type: precision
value: 0.7532458247651523
verified: true
- name: Precision Micro
type: precision
value: 0.7208955223880597
verified: true
- name: Precision Weighted
type: precision
value: 0.7367396361532118
verified: true
- name: Recall Macro
type: recall
value: 0.6880645531209203
verified: true
- name: Recall Micro
type: recall
value: 0.7208955223880597
verified: true
- name: Recall Weighted
type: recall
value: 0.7208955223880597
verified: true
- name: F1 Macro
type: f1
value: 0.7013044744309381
verified: true
- name: F1 Micro
type: f1
value: 0.7208955223880597
verified: true
- name: F1 Weighted
type: f1
value: 0.713640086434487
verified: true
- name: loss
type: loss
value: 0.6895929574966431
verified: true
widget:
- text: >-
Puigdemont, a l'estat espanyol: Quatre anys després, ens hem guanyat el
dret a dir prou
- text: >-
Llarena demana la detenció de Comín i Ponsatí aprofitant que són a Itàlia
amb Puigdemont
- text: >-
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.
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 |
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