Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use yemen2016/nbbert_ED with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yemen2016/nbbert_ED with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yemen2016/nbbert_ED")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yemen2016/nbbert_ED") model = AutoModelForSequenceClassification.from_pretrained("yemen2016/nbbert_ED") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 23c4b3350a1cd1a0f363196759f94de58a2271ba67df9951562916980f91a16d
- Size of remote file:
- 5.3 kB
- SHA256:
- 560ca40fb366b6d3ee1eae93b3eb3e2b205f0ea8572f26bff1572a2e805e8d33
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.