Instructions to use BryanSwk/finetuning-sentiment-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BryanSwk/finetuning-sentiment-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BryanSwk/finetuning-sentiment-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BryanSwk/finetuning-sentiment-model") model = AutoModelForSequenceClassification.from_pretrained("BryanSwk/finetuning-sentiment-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 624264bdb93ba0103118fb7c057549b31b5bc2416de777657f788874a3664fc9
- Size of remote file:
- 268 MB
- SHA256:
- 68f2034543f0ff56897bbb25ddba046f7f182443090de8b582992b0f536d8da3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.