Create README.md

README.md

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+---
+language:
+- en
+base_model:
+- distilbert/distilbert-base-uncased
+license: apache-2.0
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+library_name: adapter-transformers
+tags:
+- resume-classification
+- multi-label-classification
+- human-resources
+- transformers
+- distilbert
+- career-guidance
+- fine-tuned
+---
+# **Res-BERT**
+Fine-tuned DistilBERT model for multi-label resume classification.
+
+---
+
+## **Model Overview**
+**Res-BERT** is a fine-tuned version of the DistilBERT base model, trained on a multi-labeled dataset of resumes (`resume_corpus`) with occupation labels. This model can classify resumes into multiple occupation categories, making it a useful tool for HR teams, recruitment platforms, and AI-powered career assistants.
+
+### **Base Model**
+- **DistilBERT (uncased):** A smaller, faster, and cheaper version of BERT, pretrained on BookCorpus and English Wikipedia. It provides a balance of performance and efficiency for NLP tasks.
+
+---
+
+## **Dataset**
+The **resume_corpus** dataset was used for training. It consists of resumes labeled with occupations. The dataset includes:
+- **`resumes_corpus.zip`:** A collection of `.txt` files (resumes) with corresponding labels in `.lab` files.
+- **`resumes_sample.zip`:** A consolidated text file, where each line contains:
+  - Resume ID
+  - Occupation labels (separated by `;`)
+  - Resume text.
+- **`normalized_classes`:** Associations between raw and normalized occupation labels.
+
+### **Dataset Citation**
+Jiechieu, K.F.F., Tsopze, N. *Skills prediction based on multi-label resume classification using CNN with model predictions explanation.* Neural Comput & Applic (2020). [DOI:10.1007/s00521-020-05302-x](https://doi.org/10.1007/s00521-020-05302-x).
+
+---
+
+
+## **Training Procedure**
+The model was fine-tuned using:
+- **Input Format:** Lowercased, tokenized text using WordPiece with a vocabulary of 30,000 tokens.
+- **Hyperparameters:** Default settings of the Hugging Face `Trainer` API for DistilBERT-based sequence classification.
+- **Preprocessing:**
+  - Masking: 15% of tokens were masked during pretraining.
+  - Split: 80% training, 10% validation, 10% test.
+- **Hardware:** 8 16GB V100 GPUs, trained for 10 hours.
+
+---
+
+## **Intended Use**
+### **Applications**
+- Resume screening for recruitment platforms.
+- Career guidance and job-matching services.
+- Analyzing skills and experiences from resumes.
+
+### **How to Use**
+#### Using Transformers' pipeline:
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="Res-BERT", tokenizer="Res-BERT", multi_label=True)
+resumes = ["Software developer with 5 years of experience in Java and Python."]
+predictions = classifier(resumes)
+
+print(predictions)
+```
+
+#### Using Transformers' pipeline:
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("Res-BERT")
+model = AutoModelForSequenceClassification.from_pretrained("Res-BERT")
+
+text = "Experienced mechanical engineer with expertise in CAD and manufacturing."
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+
+print(outputs.logits)
+
+```
+## Citations
+@article{Sanh2019DistilBERTAD,
+  title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
+  author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
+  journal={ArXiv},
+  year={2019},
+  volume={abs/1910.01108}
+}
+
+@article{Jiechieu2020ResumeClassification,
+  title={Skills prediction based on multi-label resume classification using CNN with model predictions explanation},
+  author={K.F.F. Jiechieu and N. Tsopze},
+  journal={Neural Comput & Applic},
+  year={2020},
+  doi={10.1007/s00521-020-05302-x}
+}