Instructions to use Shrina/FineTuneBert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shrina/FineTuneBert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Shrina/FineTuneBert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Shrina/FineTuneBert") model = AutoModelForSequenceClassification.from_pretrained("Shrina/FineTuneBert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Shrina/FineTuneBert: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Shrina/FineTuneBert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Shrina/FineTuneBert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Shrina/FineTuneBert/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- a4aa42ee15e76aed98285877fc119f0e9aa2c5e54b13144df7a7c97912c44135
- Size of remote file:
- 438 MB
- SHA256:
- ff909075cd5f73b655ec06b8c8e11809cb8fc310ac9d6a88e0222af0cf48472a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.