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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d74397300ad06633326c587c1140461dfa9cb168914168f2ed79c447813ba3cf
- Size of remote file:
- 439 MB
- SHA256:
- 0eed747f529e85e5aedb2b3a16d4765f80857160e9702b191781bf1e5688cd68
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