Instructions to use FinScience/FS-distilroberta-fine-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FinScience/FS-distilroberta-fine-tuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FinScience/FS-distilroberta-fine-tuned")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("FinScience/FS-distilroberta-fine-tuned") model = AutoModelForSequenceClassification.from_pretrained("FinScience/FS-distilroberta-fine-tuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from FinScience/FS-distilroberta-fine-tuned: direct link, hf CLI and curl.
- Browser
- Download file 329 MB
-
https://huggingface.co/FinScience/FS-distilroberta-fine-tuned/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://FinScience/FS-distilroberta-fine-tuned/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/FinScience/FS-distilroberta-fine-tuned/resolve/main/pytorch_model.bin
329 MB
- Xet hash:
- 044b9e7a2f6a2b575be5431040b8af76ba1ccaba771db2ef846e5e4bf5bd44d7
- Size of remote file:
- 329 MB
- SHA256:
- 436d6806a757e5fc71d4009bba5989c856fcf9b72967b5cdfaff034d374eedc1
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