Text Classification
Transformers
PyTorch
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use gaodrew/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gaodrew/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gaodrew/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gaodrew/results") model = AutoModelForSequenceClassification.from_pretrained("gaodrew/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from gaodrew/results: direct link, hf CLI and curl.
- Browser
- Download file 500 kB
-
https://huggingface.co/gaodrew/results/resolve/main/tokenizer.model
- Command line
-
hf download hf://gaodrew/results/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/gaodrew/results/resolve/main/tokenizer.model
500 kB
- Xet hash:
- 91bf184ab12793d0754344f9095332759432e666320cc6c07f637af50e36db6f
- Size of remote file:
- 500 kB
- SHA256:
- 9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.