Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use BucketOfFish/huggingface_push_to_hub_tutorial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BucketOfFish/huggingface_push_to_hub_tutorial with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BucketOfFish/huggingface_push_to_hub_tutorial")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BucketOfFish/huggingface_push_to_hub_tutorial") model = AutoModelForSequenceClassification.from_pretrained("BucketOfFish/huggingface_push_to_hub_tutorial") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b7d45719b58dd343f1839e5346e554b28e5a076b63ffb02bdd4ff41f68e4fd07
- Size of remote file:
- 4.6 kB
- SHA256:
- 76724b95b9f2d4051706193089452be917a975e84be158254f353d0a5e0001e1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.