Instructions to use TheJournal/good_sentiment_model3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheJournal/good_sentiment_model3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TheJournal/good_sentiment_model3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TheJournal/good_sentiment_model3") model = AutoModelForSequenceClassification.from_pretrained("TheJournal/good_sentiment_model3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from TheJournal/good_sentiment_model3: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/TheJournal/good_sentiment_model3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://TheJournal/good_sentiment_model3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/TheJournal/good_sentiment_model3/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 8302916b29785afee38c86c8a47ba7db12f9c39620669f0d2593322f6f71c3e5
- Size of remote file:
- 268 MB
- SHA256:
- 97fa6866fb8c43a233ab547dcd58025f07d6c3a0455a83af0c0f08840da5bcbd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.