Instructions to use Lordemarco/sentiment_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lordemarco/sentiment_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Lordemarco/sentiment_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Lordemarco/sentiment_model") model = AutoModelForSequenceClassification.from_pretrained("Lordemarco/sentiment_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Lordemarco/sentiment_model: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/Lordemarco/sentiment_model/resolve/main/training_args.bin
- Command line
-
hf download hf://Lordemarco/sentiment_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Lordemarco/sentiment_model/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 4ecf7740b35bf97db846842338351de55b1794a74e6152a9f4a9ea0799544035
- Size of remote file:
- 5.11 kB
- SHA256:
- 5191415cea706dbe8d124499e5be6108b7a758a3904873360e3a6023ed99d6f2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.