Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use djsull/intro_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use djsull/intro_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="djsull/intro_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("djsull/intro_classification") model = AutoModelForSequenceClassification.from_pretrained("djsull/intro_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d0e253adc8af1fe6014d5cb4e9935d830f2af7a8aa60dd51ff24fee4ff61642
- Size of remote file:
- 597 MB
- SHA256:
- 1e9bdb98afe3cd46817bff0a44b9311a1d21ba5efb931e2a26661b541099acb3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.