Text Classification
Transformers
PyTorch
TensorFlow
JAX
Rust
English
bert
financial-sentiment-analysis
sentiment-analysis
Instructions to use mdizak/finbert-rust with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mdizak/finbert-rust with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mdizak/finbert-rust")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mdizak/finbert-rust") model = AutoModelForSequenceClassification.from_pretrained("mdizak/finbert-rust", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4a40198086e5572d0964d834d87d3c854e1f73bab751ab56645e2469239459fb
- Size of remote file:
- 438 MB
- SHA256:
- e1a5427945b00821fdc0dc6c84f9c722768d85bfa0950de2635ef2da60597fb1
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