Model Trained
- Problem type: Classificação de sentimentos em dataset interno do Sebrae RS
- Model ID: 96390146647
- CO2 Emissions (in grams): 0.6308
- "id2label": {"0": "Negativo", "1": "Neutro", "2": "Positivo"}
Validation Metrics
- Loss: 0.143
- Accuracy: 0.965
- Macro F1: 0.935
- Micro F1: 0.965
- Weighted F1: 0.964
- Macro Precision: 0.938
- Micro Precision: 0.965
- Weighted Precision: 0.964
- Macro Recall: 0.933
- Micro Recall: 0.965
- Weighted Recall: 0.965
Usage
Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ggrazzioli/cls_sentimento_sebrae")
tokenizer = AutoTokenizer.from_pretrained("ggrazzioli/cls_sentimento_sebrae")
inputs = tokenizer("Gostei muito dos serviços gerados, recomendo a todos!", return_tensors="pt")
outputs = model(**inputs)