Text Classification
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
bert
financial-sentiment-analysis
sentiment-analysis
text-embeddings-inference
Instructions to use Narsil/finbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Narsil/finbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Narsil/finbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Narsil/finbert") model = AutoModelForSequenceClassification.from_pretrained("Narsil/finbert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0c5c40e428831327d1ddc61d38e32f25f52619a6bf88d424786cef20aa7d2124
- Size of remote file:
- 438 MB
- SHA256:
- 513445ffce23398f35936ce03dbd29783a2e6340e3a1617d882c16a74b21ac11
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