--- pretty_name: "JBCS2025: AES Experimental Logs and Predictions" license: "cc-by-nc-4.0" configs: - config_name: evaluation_results data_files: - split: evaluation_results path: evaluation_results-*.parquet - config_name: bootstrap_confidence_intervals data_files: - split: bootstrap_confidence_intervals path: bootstrap_confidence_intervals-*.parquet tags: - automatic-essay-scoring - portuguese - text-classification --- # JBCS 2025: Experimental Artefacts for AES in Brazilian Portuguese This repository contains all experimental artefacts (logs, configurations, predictions, and evaluation results) described in the paper: > **Exploring the Usage of LLMs for Automatic Essay Scoring in Brazilian Portuguese Essays** > AndrΓ© Barbosa, Igor Cataneo Silveira, Denis Deratani MauΓ‘ > TODO --- ## πŸ“¦ What's in this dataset repo? This dataset is **not a training dataset**. Instead, it provides comprehensive logs and outputs from experiments evaluating different language models for Automatic Essay Scoring (AES) tasks in Brazilian Portuguese. Specifically, it contains: - πŸ” **JSONL files**: raw predictions from each evaluated model. - πŸ“Š **CSV files**: detailed performance metrics (Quadratic Weighted Kappa, F1-score, etc.). - βš™οΈ **YAML files**: complete Hydra configurations for reproducibility. - πŸ“‹ **Log files**: logs detailing each evaluation run. --- ## πŸ“š Related Collection All models and datasets related to this work are available in the Hugging Face collection: πŸ”— [**AES JBCS2025 Collection**](https://huggingface.co/collections/kamel-usp/jbcs2025-67d5e73a4b89c1f0c878159c) --- ## πŸ“Š Evaluated Models The table below lists all models trained and evaluated for each essay competence (C1 to C5), along with direct links to their Hugging Face repository pages: | Model | Architecture | Training Type | Link | |-------|--------------|---------------|------| | mbert_base-C1 | Encoder-only | Fine-tuned | [mbert_base-C1](https://huggingface.co/kamel-usp/jbcs2025_mbert_base-C1) | | mbert_base-C2 | Encoder-only | Fine-tuned | [mbert_base-C2](https://huggingface.co/kamel-usp/jbcs2025_mbert_base-C2) | | mbert_base-C3 | Encoder-only | Fine-tuned | [mbert_base-C3](https://huggingface.co/kamel-usp/jbcs2025_mbert_base-C3) | | mbert_base-C4 | Encoder-only | Fine-tuned | [mbert_base-C4](https://huggingface.co/kamel-usp/jbcs2025_mbert_base-C4) | | mbert_base-C5 | Encoder-only | Fine-tuned | [mbert_base-C5](https://huggingface.co/kamel-usp/jbcs2025_mbert_base-C5) | | bertimbau_base-C1 | Encoder-only | Fine-tuned | [bertimbau_base-C1](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_base-C1) | | bertimbau_base-C2 | Encoder-only | Fine-tuned | [bertimbau_base-C2](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_base-C2) | | bertimbau_base-C3 | Encoder-only | Fine-tuned | [bertimbau_base-C3](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_base-C3) | | bertimbau_base-C4 | Encoder-only | Fine-tuned | [bertimbau_base-C4](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_base-C4) | | bertimbau_base-C5 | Encoder-only | Fine-tuned | [bertimbau_base-C5](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_base-C5) | | bertimbau_large-C1 | Encoder-only | Fine-tuned | [bertimbau_large-C1](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_large-C1) | | bertimbau_large-C2 | Encoder-only | Fine-tuned | [bertimbau_large-C2](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_large-C2) | | bertimbau_large-C3 | Encoder-only | Fine-tuned | [bertimbau_large-C3](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_large-C3) | | bertimbau_large-C4 | Encoder-only | Fine-tuned | [bertimbau_large-C4](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_large-C4) | | bertimbau_large-C5 | Encoder-only | Fine-tuned | [bertimbau_large-C5](https://huggingface.co/kamel-usp/jbcs2025_bertimbau_large-C5) | | llama3-8b-C1 | Decoder-only | LoRA | [llama3-8b-C1](https://huggingface.co/kamel-usp/jbcs2025_llama3-8b-C1) | | llama3-8b-C2 | Decoder-only | LoRA | [llama3-8b-C2](https://huggingface.co/kamel-usp/jbcs2025_llama3-8b-C2) | | llama3-8b-C3 | Decoder-only | LoRA | [llama3-8b-C3](https://huggingface.co/kamel-usp/jbcs2025_llama3-8b-C3) | | llama3-8b-C4 | Decoder-only | LoRA | [llama3-8b-C4](https://huggingface.co/kamel-usp/jbcs2025_llama3-8b-C4) | | llama3-8b-C5 | Decoder-only | LoRA | [llama3-8b-C5](https://huggingface.co/kamel-usp/jbcs2025_llama3-8b-C5) | | phi3.5-C1 | Decoder-only | LoRA | [phi3.5-C1](https://huggingface.co/kamel-usp/jbcs2025_phi3.5-C1) | | phi3.5-C2 | Decoder-only | LoRA | [phi3.5-C2](https://huggingface.co/kamel-usp/jbcs2025_phi3.5-C2) | | phi3.5-C3 | Decoder-only | LoRA | [phi3.5-C3](https://huggingface.co/kamel-usp/jbcs2025_phi3.5-C3) | | phi3.5-C4 | Decoder-only | LoRA | [phi3.5-C4](https://huggingface.co/kamel-usp/jbcs2025_phi3.5-C4) | | phi3.5-C5 | Decoder-only | LoRA | [phi3.5-C5](https://huggingface.co/kamel-usp/jbcs2025_phi3.5-C5) | | phi4-C1 | Decoder-only | LoRA | [phi4-C1](https://huggingface.co/kamel-usp/jbcs2025_phi4-C1) | | phi4-C2 | Decoder-only | LoRA | [phi4-C2](https://huggingface.co/kamel-usp/jbcs2025_phi4-C2) | | phi4-C3 | Decoder-only | LoRA | [phi4-C3](https://huggingface.co/kamel-usp/jbcs2025_phi4-C3) | | phi4-C4 | Decoder-only | LoRA | [phi4-C4](https://huggingface.co/kamel-usp/jbcs2025_phi4-C4) | | phi4-C5 | Decoder-only | LoRA | [phi4-C5](https://huggingface.co/kamel-usp/jbcs2025_phi4-C5) | 🧠 Additionally, **API-only models** (e.g., DeepSeek-R1, ChatGPT-4o, SabiΓ‘-3) were evaluated but are not hosted on the Hub. Their predictions and logs are still included in this dataset. --- ## πŸ§ͺ How to Use this Dataset You can easily load the data using Hugging Face datasets library: ```python from datasets import load_dataset ds = load_dataset("kamel-usp/jbcs2025_experiments", split="runs") ``` --- ## πŸ“„ License and Citation This work is licensed under the [Creative Commons Attribution 4.0 International License (CC-BY-4.0)](https://creativecommons.org/licenses/by/4.0/). If you use these artefacts, please cite our paper: ```bibtex TODO ```