| | --- |
| | model-index: |
| | - name: augustulus-latin-sentiment-lora |
| | results: |
| | - task: |
| | type: text-classification |
| | name: Sentiment Analysis |
| | dataset: |
| | name: Ancient Latin Sentiment (Custom) |
| | type: custom |
| | metrics: |
| | - type: accuracy |
| | value: 75 |
| | name: Accuracy (with linguistic post-processing) |
| | - type: accuracy |
| | value: 37.5 |
| | name: Raw Model Accuracy |
| | license: llama3.1 |
| | language: |
| | - la |
| | base_model: |
| | - meta-llama/Llama-3.1-8B-Instruct |
| | tags: |
| | - gguf |
| | - quantized |
| | - llama-cpp |
| | --- |
| | |
| | # Augustulus Latin Sentiment Analysis LoRA |
| |
|
| | **Developed by Team Trojan Parse** *University of Florida Senior Design Project* |
| |
|
| | A LoRA (Low-Rank Adaptation) adapter fine-tuned on **Llama-3.1-8B-Instruct** for fine-grained sentiment classification of Ancient Latin texts across seven emotional intensity levels. |
| |
|
| | ## Project Information |
| |
|
| | - **Team Name:** Trojan Parse |
| | - **Team Members:** |
| | - Alex John |
| | - Ryan Willson |
| | - Byron Boatright |
| | - Jake Marotta |
| | - Duncan Fuller |
| | - **Project Repository:** [GitHub: Trojan-Parse-Project](https://github.com/alxxjohn/Trojan-Parse-Project) |
| | - **Advisor:** Eleni Bozia, Ph.D., Dr. phil. (Associate Professor of Classics and Digital Humanities) |
| | - **Advisor Department:** Department of Classics, University of Florida |
| |
|
| | ## Model Description |
| |
|
| | - **Model Name:** `augustulus-latin-sentiment-lora` |
| | - **Model type:** LoRA Adapter for Ancient Language Sentiment Classification |
| | - **Language:** Classical/Ancient Latin |
| | - **Base model:** [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) |
| | - **License:** Llama 3.1 Community License |
| | - **Purpose:** Academic research and historical text analysis |
| |
|
| | ## Sentiment Categories |
| |
|
| | Our model classifies Ancient Latin texts into six emotional intensity levels: |
| |
|
| | ### Positive Sentiments |
| | - **EXTREMELY POSITIVE (+3)**: *exsultatio, jubilum, beatitudo, summa felicitas* |
| | - Examples: Triumphal declarations, ultimate joy, divine blessing |
| | |
| | - **VERY POSITIVE (+2)**: *gaudium, laetitia, amor, gloria, victoria, laudare* |
| | - Examples: Military victories, celebrations, expressions of love/honor |
| | |
| | - **MODERATELY POSITIVE (+1)**: *felix, laetus, bonus, pulcher, spes* |
| | - Examples: General contentment, hope, pleasant situations |
| |
|
| | ### Neutral (0) |
| | - Factual statements, descriptions without emotional valence |
| |
|
| | ### Negative Sentiments |
| | - **MODERATELY NEGATIVE (-1)**: *malus, tristis, anxius, timor* |
| | - Examples: Minor concerns, sadness, mild fear |
| | |
| | - **VERY NEGATIVE (-2)**: *dolor magnus, timor vehemens, ira, furor* |
| | - Examples: Great pain, intense anger, serious threats |
| | |
| | - **EXTREMELY NEGATIVE (-3)**: *desperatio, exitium, cruciatus, malum* |
| | - Examples: Utter despair, destruction, torture, ultimate evil |
| |
|
| | ## Performance |
| |
|
| | | Configuration | Accuracy | Notes | |
| | |:---|:---:|:---| |
| | | Base Llama 3.1 (zero-shot) | 43.8% | Unreliable, biased toward extremes | |
| | | LoRA Adapter (raw predictions) | 37.5% | Systematic but conservative | |
| | | **LoRA + Linguistic Rules** | **75.0%** | Production-ready | |
| |
|
| | ### Category-Level Performance |
| | - **Neutral Detection:** 100% accuracy (3/3 test cases) |
| | - **Moderate Categories:** 100% accuracy (learned systematic patterns) |
| | - **Extreme Categories:** 83.3% accuracy (with intensity calibration) |
| |
|
| | ## Training Approach |
| |
|
| | Our training methodology combined multiple data sources and validation strategies: |
| |
|
| | ### Data Pipeline (5-day development cycle) |
| |
|
| | **Phase 1: Initial Generation** |
| | - Few-shot generation using base Llama 3.1 |
| | - Context-aware synthetic examples |
| | - Balanced across all six sentiment categories |
| |
|
| | **Phase 2: Consensus Filtering** |
| | - Trained multiple LoRA variants on hand-annotated data |
| | - Consensus filtering: kept examples where ≥2 models agreed |
| | - Reduced noise and improved training data quality |
| |
|
| | **Phase 3: Corpus Mining** |
| | - Mined authentic Ancient Latin texts from Perseus Digital Library |
| | - Extracted high-confidence positive examples (previously underrepresented) |
| | - Combined ~40,000 corpus examples with synthetic data |
| |
|
| | **Phase 4: Final Training & Iteration** |
| | - Balanced dataset: 9,000 examples (1,500 per category) |
| | - Distributed training with data-parallel strategy |
| | - Multiple training runs to optimize hyperparameters |
| |
|
| | ### Final Training Configuration |
| | - **Training Examples:** 9,000 (balanced across 7 categories) |
| | - **Training Epochs:** 15 |
| | - **Architecture:** LoRA adapter (rank: 128, alpha: 256) |
| | - **Optimization:** 8-bit quantization for efficiency |
| | - **Hardware:** High-performance GPU cluster |
| | - **Framework:** PyTorch, HuggingFace Transformers, PEFT |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | from transformers import AutoModelForCausalLM, AutoTokenizer |
| | from peft import PeftModel |
| | import torch |
| | |
| | # Load base model and adapter |
| | base_model = "meta-llama/Llama-3.1-8B-Instruct" |
| | model = AutoModelForCausalLM.from_pretrained( |
| | base_model, |
| | device_map="auto", |
| | torch_dtype=torch.float16, |
| | trust_remote_code=True |
| | ) |
| | |
| | # Load Team Trojan Parse's adapter |
| | # Replace YOUR_USERNAME with your Hugging Face username |
| | model = PeftModel.from_pretrained(model, "YOUR_USERNAME/augustulus-latin-sentiment-lora") |
| | tokenizer = AutoTokenizer.from_pretrained(base_model) |
| | |
| | # Classify sentiment |
| | def classify_latin_sentiment(text): |
| | prompt = f'''Classify the sentiment of this Latin text as: VERY NEGATIVE, MODERATELY NEGATIVE, NEUTRAL, MODERATELY POSITIVE, VERY POSITIVE, or EXTREMELY POSITIVE. |
| | |
| | Latin text: {text} |
| | |
| | Sentiment:''' |
| | |
| | inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| | outputs = model.generate( |
| | **inputs, |
| | max_new_tokens=20, |
| | temperature=0.1, |
| | do_sample=False |
| | ) |
| | |
| | response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| | return response.split("Sentiment:")[-1].strip() |
| | |
| | # Example: Extreme positive (triumph) |
| | text = "Victoria splendidissima! Dux gloriam aeternam meruit!" |
| | print(classify_latin_sentiment(text)) |
| | # Output: EXTREMELY POSITIVE |
| | |
| | # Example: Extreme negative (despair) |
| | text = "Bellum crudele et longum populum afflixerat." |
| | print(classify_latin_sentiment(text)) |
| | # Output: VERY NEGATIVE |
| | ``` |
| |
|
| | --- |
| |
|
| | ## GGUF Model Download and Local Usage (Merged Fine-Tune) |
| |
|
| | The LoRA adapter has been merged with the base model and quantized to **Q8_0** (8-bit) precision for efficient deployment on CPU/GPU via tools like `llama.cpp` and `Ollama`. |
| | |
| | ### 💾 File Details |
| | - **File Name:** `augustulus-latin-sentiment-8b-q8_0.gguf` |
| | - **Size:** 8.0 GB |
| | - **Quantization:** Q8_0 (Recommended for best balance of speed and accuracy) |
| | |
| | --- |
| | |
| | ### Llama 3.1 License Notice |
| | **IMPORTANT**: This model (including the GGUF file) is a derivative of Meta’s **Llama 3.1** model and is governed by the [**Meta Llama 3.1 Community License**](https://llama.meta.com/llama3_1/license). |
| | |
| | - **Attribution**: If you redistribute or build products with this model, you must include the statement **“Built with Meta Llama 3”** in a prominent location (e.g., README, UI footer, about page). |
| | - **Commercial Use**: Allowed without additional permission as long as your product or service has **fewer than 700 million monthly active users**. Above that threshold, you need a separate commercial license from Meta. |
| | |
| | See the full license text here: https://llama.meta.com/llama3_1/license |
| | --- |
| |
|
| | ### Usage Example (with Ollama) |
| | This workflow uses a custom **Modelfile** to set the strict sentiment task and gives the model a simple local name. |
| |
|
| | #### Create Modelfile |
| | Save the following content as a file named `Modelfile`: |
| |
|
| | ```text |
| | # Modelfile for the Augustulus Latin Sentiment Model |
| | |
| | FROM hf.co/TronCodes/augustulus-latin-sentiment-lora/augustulus-latin-sentiment-8b-q8_0.gguf |
| | |
| | SYSTEM """ |
| | You are Augustulus, an expert in Classical Latin sentiment analysis. Your task is to respond ONLY with one of the following exact labels: EXTREMELY POSITIVE, VERY POSITIVE, MODERATELY POSITIVE, NEUTRAL, MODERATELY NEGATIVE, VERY NEGATIVE, or EXTREMELY NEGATIVE. Do not provide any conversational text or explanation. |
| | """ |
| | |
| | TEMPLATE """ |
| | {{ if .System }}<|start_header_id|>system<|end_header_id|>{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|> |
| | """ |
| | |
| | PARAMETER temperature 0.1 |
| | PARAMETER num_predict 20 |
| | PARAMETER stop "<|eot_id|>" |
| | ``` |
| |
|
| | --- |
| |
|
| | #### Create and Run |
| | ```bash |
| | ollama create augustulus-latin -f Modelfile |
| | ollama run augustulus-latin |
| | ``` |
| |
|
| |
|
| | ## Acknowledgments |
| |
|
| | We gratefully acknowledge: |
| | * Dr. Eleni Bozia (Ph.D., Dr. phil.) - Senior Project Advisor |
| | * University of Florida Department of Humanities - Computing resources and support |
| | * Perseus Digital Library - Access to Classical Latin corpus |
| | * Meta AI - Llama 3.1 base model |
| | * HuggingFace - PEFT library and model hosting infrastructure |
| |
|
| | ## Citation |
| |
|
| | ```bibtex |
| | @misc{trojan_parse_latin_sentiment_2025, |
| | author = {{Team Trojan Parse}}, |
| | title = {Augustulus Latin Sentiment Analysis LoRA}, |
| | year = {2025}, |
| | publisher = {University of Florida}, |
| | journal = {HuggingFace Model Hub}, |
| | howpublished = {\\url{[https://huggingface.co/TronCodes/augustulus-latin-sentiment-lora](https://huggingface.co/TronCodes/augustulus-latin-sentiment-lora)}} |
| | } |
