Instructions to use dima806/tweets-financial-classifier-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/tweets-financial-classifier-distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dima806/tweets-financial-classifier-distilbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dima806/tweets-financial-classifier-distilbert") model = AutoModelForSequenceClassification.from_pretrained("dima806/tweets-financial-classifier-distilbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dima806/tweets-financial-classifier-distilbert: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/dima806/tweets-financial-classifier-distilbert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dima806/tweets-financial-classifier-distilbert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dima806/tweets-financial-classifier-distilbert/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 912979128f1b4dd8feadca6d4ce4665b7d65ec830c7a2a26304e35fe05afb02d
- Size of remote file:
- 438 MB
- SHA256:
- 0f16f57e05e26cd342e335725560c29d7bf18dd6096eadd32bf0fe47cbc396f7
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