Instructions to use rnribeiro/FT-ProsusAI-finbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rnribeiro/FT-ProsusAI-finbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rnribeiro/FT-ProsusAI-finbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rnribeiro/FT-ProsusAI-finbert") model = AutoModelForSequenceClassification.from_pretrained("rnribeiro/FT-ProsusAI-finbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rnribeiro/FT-ProsusAI-finbert: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/rnribeiro/FT-ProsusAI-finbert/resolve/main/training_args.bin
- Command line
-
hf download hf://rnribeiro/FT-ProsusAI-finbert/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rnribeiro/FT-ProsusAI-finbert/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- 9ec2538a36c0b23d2779a307eea9bc3072c55fb28b8bf0a1bc933e5f019385c4
- Size of remote file:
- 4.98 kB
- SHA256:
- ad513649043c6c537d8fb174471f4fe57529db9ff281a8364bd6f7827ff3cd9a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.