Text Classification
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
bert
financial-sentiment-analysis
sentiment-analysis
text-embeddings-inference
Instructions to use Narsil/finbert-slow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Narsil/finbert-slow with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Narsil/finbert-slow")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Narsil/finbert-slow") model = AutoModelForSequenceClassification.from_pretrained("Narsil/finbert-slow") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9d1a014c44a024a62b1970f0c10f6ee787a4cf6d11c35f5cecdebf0669c06ec6
- Size of remote file:
- 438 MB
- SHA256:
- 04c231ff252c4b5ed3e277120b1cc961b97be14d81825c51c44ba66b4ee8033e
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