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:
- 9ee4e576fbaf440d6c8853d09e370d051be0f0989aa448290e7586368dcdebf6
- Size of remote file:
- 2.16 kB
- SHA256:
- a448ecd6685ca0024ee8fe5a7b8bf2ee038a0f564a5fea75d3b152c96d1d5839
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