Text Classification
Transformers
PyTorch
TensorFlow
JAX
English
bert
financial-sentiment-analysis
sentiment-analysis
Instructions to use ProsusAI/finbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProsusAI/finbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ProsusAI/finbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ProsusAI/finbert") model = AutoModelForSequenceClassification.from_pretrained("ProsusAI/finbert") - Inference
- Notebooks
- Google Colab
- Kaggle
Add TF weights
#3
by joaogante - opened
Validated by the pt_to_tf CLI. Max crossload output difference=3.123e-05; Max converted output difference=3.123e-05.
Hi again π
My apologies -- our automatic conversion tool was missing the conversion of some model heads, and this was one of the incomplete conversions. We also added much stricter equivalence tests (https://github.com/huggingface/transformers/pull/17588), to ensure TF users enjoy the exact same model experience as PT users.