Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use kitsunea/m1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kitsunea/m1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kitsunea/m1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kitsunea/m1") model = AutoModelForSequenceClassification.from_pretrained("kitsunea/m1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4df8d1b327651f658378a48c7d7a560990b05cb3cdbc116bd7d664ef999c3045
- Size of remote file:
- 5.71 kB
- SHA256:
- 0445549f9b63a5c7aa54fd29d2ea2d4a708d1c671161159da01209ac8ba30cd9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.