Instructions to use rose-e-wang/tools_a6000_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rose-e-wang/tools_a6000_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rose-e-wang/tools_a6000_3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rose-e-wang/tools_a6000_3") model = AutoModelForSequenceClassification.from_pretrained("rose-e-wang/tools_a6000_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e44c33d66ccac2eee60b1e9ce9b6e0a2a9d0db100284d3abcf72d9fdf7780a5e
- Size of remote file:
- 4.09 kB
- SHA256:
- 13f1c167cb3aadb949b3a4a51fb70f4cc02ee5477896983746f639fef902bfdf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.