Text Classification
Transformers
PyTorch
TensorFlow
English
bert
financial-sentiment-analysis
sentiment-analysis
Instructions to use ldeb/solved-finbert-tone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ldeb/solved-finbert-tone with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ldeb/solved-finbert-tone")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ldeb/solved-finbert-tone") model = AutoModelForSequenceClassification.from_pretrained("ldeb/solved-finbert-tone", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from ldeb/solved-finbert-tone: direct link, hf CLI and curl.
- Browser
- Download file 439 MB
-
https://huggingface.co/ldeb/solved-finbert-tone/resolve/main/tf_model.h5
- Command line
-
hf download hf://ldeb/solved-finbert-tone/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/ldeb/solved-finbert-tone/resolve/main/tf_model.h5
439 MB
- Xet hash:
- 00934c1a7348fc1f3732381939124571ada7d7ec6b9a21774285ca808e26029a
- Size of remote file:
- 439 MB
- SHA256:
- 37d45bf7c11607c5df782ef613f711ccf8b5a9af5ae6ceffc2e5da8aae191096
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