Instructions to use Neil96/mymodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Neil96/mymodel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Neil96/mymodel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Neil96/mymodel") model = AutoModelForSequenceClassification.from_pretrained("Neil96/mymodel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c1b563fc43b4c7b6a46a625a73cf5cf65d30ab9715bfeef02e64dbbe1ca33d8
- Size of remote file:
- 3.52 kB
- SHA256:
- d1e382683dba1264d8b89e032672c94fd2818e2a661473827da588e9b126eeba
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.