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
1
#3 opened almost 2 years ago
by
mertolcaman
Librarian Bot: Add base_model information to model
#2 opened over 2 years ago
by
librarian-bot
Adding `safetensors` variant of this model
#1 opened about 3 years ago
by
SFconvertbot