alfiannajih
/

g-retriever-resume-reviewer

@@ -10,7 +10,9 @@ base_model: NousResearch/Hermes-3-Llama-3.1-8B
 # Model Card for Model ID
 <!-- Provide a quick summary of what the model is/does. -->
-G-Retriever integrates Graph Neural Networks (GNN), Large Language Model (LLM), and Retrieval-Augmented Generation(RAG) by using Knowledge Graph. This model was developed by Xiaoxin He.
 ## Model Details
@@ -19,175 +21,15 @@ G-Retriever integrates Graph Neural Networks (GNN), Large Language Model (LLM),
 <!-- Provide a longer summary of what this model is. -->
 While the original method utilized Llama 2 family model as the LLM, this repository has experimented it with Llama 3.1 8B.
-### Model Sources [optional]
 <!-- Provide the basic links for the model. -->
-- **Repository:** [Original repository](https://github.com/XiaoxinHe/G-Retriever)
-- **Paper [optional]:** [G-Retriever Paper](https://arxiv.org/abs/2402.07630)
 ## Uses
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-This model is designed to be used as a resume reviewer. The approach involves retrieving a subgraph from a knowledge graph built from LinkedIn job postings and feeding it into a GNN. The features extracted from the subgraph are further processed and concatenated with the input embeddings from the query text. These concatenated features are then passed through the self-attention layer of Llama 3.1 8B.
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
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-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
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-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**APA:**
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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 # Model Card for Model ID
 <!-- Provide a quick summary of what the model is/does. -->
+This repository is created for submission to Compfest: Artificial Intelligence Competition (AIC) 16.
+G-Retriever integrates Graph Neural Networks (GNN), Large Language Model (LLM), and Retrieval-Augmented Generation(RAG) by using Knowledge Graph. This model was originaly developed by Xiaoxin He.
 ## Model Details
 <!-- Provide a longer summary of what this model is. -->
 While the original method utilized Llama 2 family model as the LLM, this repository has experimented it with Llama 3.1 8B.
+### Model Sources
 <!-- Provide the basic links for the model. -->
+- **Repository:** [Repository](https://github.com/alfiannajih/job-recommender)
+- **Training Script:** [G-Retriever Repository](https://github.com/XiaoxinHe/G-Retriever)
+- **Paper:** [G-Retriever Paper](https://arxiv.org/abs/2402.07630)
 ## Uses
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+This model is designed to be used as a resume reviewer. The approach involves retrieving a subgraph from a knowledge graph built from LinkedIn job postings and feeding it into a GNN. The features extracted from the subgraph are further processed and concatenated with the input embeddings from the query text. These concatenated features are then passed through the self-attention layer of Llama 3.1 8B to generate a resume review.