Instructions to use coldnasser/mindscape with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use coldnasser/mindscape with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="coldnasser/mindscape")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("coldnasser/mindscape") model = AutoModelForSequenceClassification.from_pretrained("coldnasser/mindscape", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- baf06dff42938d6b106bd2cbcf811bd28f5f4d21d482af39c2ef429d33cda296
- Size of remote file:
- 499 MB
- SHA256:
- a40dfd6fc441fef2666ade2e72000c216fe7e1812bd9de81fd8211e546d4a4ce
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.