Instructions to use nikunjbjj/jd-resume-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nikunjbjj/jd-resume-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nikunjbjj/jd-resume-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nikunjbjj/jd-resume-model") model = AutoModelForSequenceClassification.from_pretrained("nikunjbjj/jd-resume-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b022b49540e85fda276feb3ef53abf7e8807f1947655baa0afbfb4a6c97b0aa4
- Size of remote file:
- 439 MB
- SHA256:
- 3183ca072aa24ec7f6884cc0f9f9a5112ad3b1e4491c9b0c5fc929e95a599ebc
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