Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use gasperpre/custom_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gasperpre/custom_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gasperpre/custom_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gasperpre/custom_model") model = AutoModelForSequenceClassification.from_pretrained("gasperpre/custom_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from gasperpre/custom_model: direct link, hf CLI and curl.
- Browser
- Download file 16.4 MB
-
https://huggingface.co/gasperpre/custom_model/resolve/main/tokenizer.json
- Command line
-
hf download hf://gasperpre/custom_model/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/gasperpre/custom_model/resolve/main/tokenizer.json
16.4 MB
- Xet hash:
- c701f1bf6c010806fe3d160c6b49d0725ba57dadb9edf9f134925470eeff4326
- Size of remote file:
- 16.4 MB
- SHA256:
- 2e12ea7ef3c1bfd4b1fb977636226680b9fcf10ede02f869dec0a324a5b86dfb
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