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--- |
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license: apache-2.0 |
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tags: |
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- text-classification |
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- fine-tuning |
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- resume classification |
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library_name: transformers |
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--- |
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# DistilBERT Resume Classification Model |
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This repository contains a fine-tuned DistilBERT model for classifying resume sentences into predefined categories. The model is trained on a dataset of resumes and can classify sentences into categories such as Personal Information, Experience, Summary, Education, Qualifications & Certificates, Skills, and Objectives. |
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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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The model can classify sentences into the following categories: |
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- Personal Information |
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- Experience |
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- Summary |
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- Education |
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- Qualifications & Certificates |
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- Skills |
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- Objectives |
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## Usage |
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### Load the Model and Tokenizer |
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To use the model and tokenizer, you can load them from the Hugging Face Hub as follows: |
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```python |
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from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification |
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# Load the model and tokenizer |
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model_name = "oussama120/Resume_Sentence_Classification" |
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tokenizer = DistilBertTokenizerFast.from_pretrained(model_name) |
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model = DistilBertForSequenceClassification.from_pretrained(model_name) |
