Text Classification
Transformers
PyTorch
English
bert
financial-text-analysis
forward-looking-statement
Instructions to use yiyanghkust/finbert-fls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yiyanghkust/finbert-fls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yiyanghkust/finbert-fls")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yiyanghkust/finbert-fls") model = AutoModelForSequenceClassification.from_pretrained("yiyanghkust/finbert-fls") - Inference
- Notebooks
- Google Colab
- Kaggle
Add TF weights
#2
by joaogante - opened
Model converted by the transformers' pt_to_tf CLI.
All converted model outputs and hidden layers were validated against its Pytorch counterpart. Maximum crossload output difference=9.537e-06; Maximum converted output difference=9.537e-06.