Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use kitsunea/m2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kitsunea/m2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kitsunea/m2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kitsunea/m2") model = AutoModelForSequenceClassification.from_pretrained("kitsunea/m2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6d94205fe4bb1ef753767c4ceb71b33efbda3b331fbd0f2a07a8274000bd6e6
- Size of remote file:
- 5.78 kB
- SHA256:
- a2f67fcf9bd41c548d4fd7430438123b81519afe04f32784a860af17458fc2ff
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.