Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use chengyineng/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chengyineng/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chengyineng/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chengyineng/results") model = AutoModelForSequenceClassification.from_pretrained("chengyineng/results") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1360a666e9bddbda7f6ab8f3811363a6ca8617931ef061910bb9b9db0d3afb5f
- Size of remote file:
- 5.18 kB
- SHA256:
- 5d112ed0929b375b789a1fcc971e16f13c0371abc4381c3422277f9c455d9598
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.