IT-EMOTION-ANALYZER
This is a model for emotion analysis of italian sentences trained on a translated dataset by Google Translator. It maps sentences & paragraphs with 6 emotions which are:
- 0: sadness
- 1: joy
- 2: love
- 3: anger
- 4: fear
- 5: surprise
Model in action
Using this model becomes easy when you have transformers installed:
pip install -U transformers
Then you can use the model like this:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline
sentences = ["Questa è una frase triste", "Questa è una frase felice", "Questa è una frase di stupore"]
tokenizer = AutoTokenizer.from_pretrained("aiknowyou/it-emotion-analyzer")
model = AutoModelForSequenceClassification.from_pretrained("aiknowyou/it-emotion-analyzer")
emotion_analysis = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
emotion_analysis(sentences)
Obtaining the following result:
[{'label': '0', 'score': 0.9481984972953796},
{'label': '1', 'score': 0.9299975037574768},
{'label': '5', 'score': 0.9543816447257996}]
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 43095109829
- CO2 Emissions (in grams): 0.4489
Validation Metrics
- Loss: 0.566
- Accuracy: 0.828
- Macro F1: 0.828
- Micro F1: 0.828
- Weighted F1: 0.828
- Macro Precision: 0.828
- Micro Precision: 0.828
- Weighted Precision: 0.828
- Macro Recall: 0.828
- Micro Recall: 0.828
- Weighted Recall: 0.828