Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/tiny_bert_km_10_v1_qnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/tiny_bert_km_10_v1_qnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_km_10_v1_qnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_km_10_v1_qnli") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_km_10_v1_qnli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0280a3a8d8bff8da620c23df63e936cac9fa3afd19c4131ed1003d3c5819352
- Size of remote file:
- 5.37 kB
- SHA256:
- 7d29b321724ffea51dd34409f56e9331906ee3401e342c360c0f73a478fa7e56
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.