Instructions to use AZIIIIIIIIZ/distilbert-base-uncased_finetuned_code_text_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AZIIIIIIIIZ/distilbert-base-uncased_finetuned_code_text_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AZIIIIIIIIZ/distilbert-base-uncased_finetuned_code_text_classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AZIIIIIIIIZ/distilbert-base-uncased_finetuned_code_text_classifier") model = AutoModelForSequenceClassification.from_pretrained("AZIIIIIIIIZ/distilbert-base-uncased_finetuned_code_text_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from AZIIIIIIIIZ/distilbert-base-uncased_finetuned_code_text_classifier: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/AZIIIIIIIIZ/distilbert-base-uncased_finetuned_code_text_classifier/resolve/main/training_args.bin
- Command line
-
hf download hf://AZIIIIIIIIZ/distilbert-base-uncased_finetuned_code_text_classifier/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/AZIIIIIIIIZ/distilbert-base-uncased_finetuned_code_text_classifier/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- b9fb833837ee0eedad7df74ac77a4ba63941eac60a24123466a8a3c1011e1368
- Size of remote file:
- 5.24 kB
- SHA256:
- 671cfc799a6bfa76be12fc3b984ae1e3dfb8d9204e29c302341ae8385f34e31e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.