Instructions to use runaksh/ResumeClassification_distilBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use runaksh/ResumeClassification_distilBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="runaksh/ResumeClassification_distilBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("runaksh/ResumeClassification_distilBERT") model = AutoModelForSequenceClassification.from_pretrained("runaksh/ResumeClassification_distilBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec2c351ba38af48e3ab1daaf4d23a5dae0c5ac92ae077c6f10b59d5a73ec6f92
- Size of remote file:
- 268 MB
- SHA256:
- 27ac98bc6e95d1924b901787f5a31db7d7e9127d8b99877435dcf301d4f51939
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