Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use gerbejon/digilog-eform-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gerbejon/digilog-eform-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gerbejon/digilog-eform-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gerbejon/digilog-eform-classifier") model = AutoModelForSequenceClassification.from_pretrained("gerbejon/digilog-eform-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-500/optimizer.pt from gerbejon/digilog-eform-classifier: direct link, hf CLI and curl.
- Browser
- Download file 1.08 GB
-
https://huggingface.co/gerbejon/digilog-eform-classifier/resolve/main/checkpoint-500/optimizer.pt
- Command line
-
hf download hf://gerbejon/digilog-eform-classifier/checkpoint-500/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/gerbejon/digilog-eform-classifier/resolve/main/checkpoint-500/optimizer.pt
1.08 GB
- Xet hash:
- 9851bdf417eed241f60f2111d2b39b7a66fc4164a024ae7c69b45f11856edb21
- Size of remote file:
- 1.08 GB
- SHA256:
- 9acce701aee38dba44fe43ebfd5cd674312e969dbd8592c1bcbfa2717aec7869
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.