Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use kitsunea/m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kitsunea/m3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kitsunea/m3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kitsunea/m3") model = AutoModelForSequenceClassification.from_pretrained("kitsunea/m3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ffd40b28ede6c0fafa61b0c487d22616cac573e1afe90aefd1b0560b5ff1e435
- Size of remote file:
- 5.78 kB
- SHA256:
- 46dd57547463bbc2ac353f35c713b4fb9009b4a12314ef7c3e588d7bbcd3f8dc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.