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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Narsil/finbert") model = AutoModelForSequenceClassification.from_pretrained("Narsil/finbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from Narsil/finbert: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Narsil/finbert/resolve/main/tf_model.h5
- Command line
-
hf download hf://Narsil/finbert/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/Narsil/finbert/resolve/main/tf_model.h5
438 MB
- Xet hash:
- 9b7647c1f674a6e1a13bab9afc3db9b0ce0eb85bc57239d7202dc8b01c8a4848
- Size of remote file:
- 438 MB
- SHA256:
- 195e23248e2e9a4ffed51e671408e390d8b902f070d93dea3b06d8d5e3bfc1da
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