Instructions to use Jtisch7/bertFinancialSent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jtisch7/bertFinancialSent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jtisch7/bertFinancialSent")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, TF_AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jtisch7/bertFinancialSent") model = TF_AutoModelForSequenceClassification.from_pretrained("Jtisch7/bertFinancialSent", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from Jtisch7/bertFinancialSent: direct link, hf CLI and curl.
- Browser
- Download file 434 MB
-
https://huggingface.co/Jtisch7/bertFinancialSent/resolve/main/tf_model.h5
- Command line
-
hf download hf://Jtisch7/bertFinancialSent/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/Jtisch7/bertFinancialSent/resolve/main/tf_model.h5
434 MB
- Xet hash:
- 6d7b81dd4b34c36795c9cab28e5645e7bf4b432c0ee3c454b0b9f54f7b79b4f2
- Size of remote file:
- 434 MB
- SHA256:
- 21f1ef0ffe14ecb54e0682506ba62d269f1ec97798687f3897371e556916ffdb
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