Text Classification
Transformers
TensorBoard
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use underscore2/modernbert_large_likes_predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use underscore2/modernbert_large_likes_predictor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="underscore2/modernbert_large_likes_predictor")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("underscore2/modernbert_large_likes_predictor") model = AutoModelForSequenceClassification.from_pretrained("underscore2/modernbert_large_likes_predictor") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bba280bc8a32db84ad115fd15d3c6fa347bc965b2f4b93cbd8ddb077d4cd9a65
- Size of remote file:
- 5.3 kB
- SHA256:
- 598e08e931ed271c4ff002faa9ab3a0335d6a115e2b8acda3d791c7bd3e3a364
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.