Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Sigma/financial-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sigma/financial-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sigma/financial-sentiment-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sigma/financial-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("Sigma/financial-sentiment-analysis") - Notebooks
- Google Colab
- Kaggle
labels
#3
by mertolcaman - opened
The model gives the result as LABEL_0, LABEL_1, and LABEL_2
Are they like presented below?
0: negative
1:neutral
2:positive
Just tried it and it does appear so. Would be good to add to docs