Text Classification
Transformers
PyTorch
distilbert
fine-tuning
resume classification
text-embeddings-inference
Instructions to use oussama120/Resume_Sentence_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oussama120/Resume_Sentence_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oussama120/Resume_Sentence_Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("oussama120/Resume_Sentence_Classification") model = AutoModelForSequenceClassification.from_pretrained("oussama120/Resume_Sentence_Classification") - Notebooks
- Google Colab
- Kaggle
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README.md
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## Model Details
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- **Model:** DistilBERT (base-uncased)
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- **Fine-tuned on:** Custom resume dataset
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- **Number of classes:** 7
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## Categories
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## Model Details
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- **Model:** DistilBERT (base-uncased)
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- **Fine-tuned on:** Custom resume dataset (ganchengguang/resume_seven_class)
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- **Number of classes:** 7
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## Categories
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