google/jigsaw_toxicity_pred
Updated β’ 1.85k β’ 34
How to use citizenlab/twitter-xlm-roberta-base-sentiment-finetunned with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="citizenlab/twitter-xlm-roberta-base-sentiment-finetunned") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("citizenlab/twitter-xlm-roberta-base-sentiment-finetunned")
model = AutoModelForSequenceClassification.from_pretrained("citizenlab/twitter-xlm-roberta-base-sentiment-finetunned", device_map="auto")This is multilingual XLM-Roberta model sequence classifier fine tunned and based on Cardiff NLP Group sentiment classification model.
from transformers import pipeline
model_path = "citizenlab/twitter-xlm-roberta-base-sentiment-finetunned"
sentiment_classifier = pipeline("text-classification", model=model_path, tokenizer=model_path)
sentiment_classifier("this is a lovely message")
> [{'label': 'Positive', 'score': 0.9918450713157654}]
sentiment_classifier("you are an idiot and you and your family should go back to your country")
> [{'label': 'Negative', 'score': 0.9849833846092224}]
precision recall f1-score support
Negative 0.57 0.14 0.23 28
Neutral 0.78 0.94 0.86 132
Positive 0.89 0.80 0.85 51
accuracy 0.80 211
macro avg 0.75 0.63 0.64 211
weighted avg 0.78 0.80 0.77 211