Text Classification
Transformers
PyTorch
English
bert
finance
sentiment analysis
regression
finbert
text-embeddings-inference
Instructions to use LHF/finbert-regressor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LHF/finbert-regressor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LHF/finbert-regressor")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LHF/finbert-regressor") model = AutoModelForSequenceClassification.from_pretrained("LHF/finbert-regressor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from LHF/finbert-regressor: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/LHF/finbert-regressor/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://LHF/finbert-regressor/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/LHF/finbert-regressor/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 3a0c07f4e13eaad06376c8f091dd66bd042c22514d9a45458838506e224b1304
- Size of remote file:
- 438 MB
- SHA256:
- 5ccc44b5da969a5ea6088c84c803d6b411ac7873cb236936289ce84c0207245a
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