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Model: oberbics/llama-3.1-8B-newspaper_argument_mining Source: Original Platform
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---
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library_name: transformers
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tags:
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- llama
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- llama-3.1
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- argument-mining
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- information-extraction
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- historical-texts
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- newspapers
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- lora
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- peft
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- trl
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- grpo
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license: llama3.1
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language:
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- de
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- en
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- it
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- fr
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- multilingual
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base_model: meta-llama/Llama-3.1-8B-Instruct
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datasets:
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- custom
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pipeline_tag: text-generation
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model-index:
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- name: llama-3.1-8B-newspaper_argument_mining
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results:
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- task:
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type: argument-mining
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name: Argument Mining
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dataset:
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type: Historical-newspapers
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name: Italian, German, French, and English Historical Newspapers (1908)
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metrics:
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- name: eval_loss
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type: loss
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value: 2.6980
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---
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# Llama 3.1 8B for Historical Newspaper Argument Mining
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) that has undergone **two-stage training** for argument mining (argumentative unit extraction and enthymeme reconstruction) in historical newspapers.
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## Training Pipeline
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### Stage 1: Supervised Fine-Tuning with LoRA
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Initial fine-tuning using LoRA/PEFT on [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)
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### Stage 2: GRPO Post-Training
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Further optimization on [oberbics/llama-3.1-newspaper-arguments-your_name-optimized_full_V2](https://huggingface.co/oberbics/llama-3.1-newspaper-arguments-your_name-optimized_full_V2) using [TRL](https://github.com/huggingface/trl) with **Group Relative Policy Optimization (GRPO)**, a reinforcement learning method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
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## Model Details
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### Model Description
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This model extracts argumentative units from historical newspaper texts across multiple languages (Italian, German, French, and English), providing structured XML output suitable for digital humanities research and historical discourse analysis. The two-stage training process combines supervised learning for argument structure with reinforcement learning to improve quality and eliminate duplicate extractions.
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**Key Information:**
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- **Developed by:** oberbics
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- **Model type:** Causal Language Model (Fine-tuned with LoRA + GRPO)
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- **Language(s) (NLP):** Italian, German, French, English
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- **License:** Llama 3.1 Community License
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- **Base model:** [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)
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- **Intermediate model:** [oberbics/llama-3.1-newspaper-arguments-your_name-optimized_full_V2](https://huggingface.co/oberbics/llama-3.1-newspaper-arguments-your_name-optimized_full_V2)
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## Intended Uses
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### Primary Use Cases
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- Extracting argumentative units from (historical) newspaper articles
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- Digital humanities research on historical argumentation patterns
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- Large-scale corpus analysis of multilingual newspaper archives
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- Enthymeme reconstruction - Implicit Argument Mining
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### Limitations
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- Optimized for historical newspaper texts from early 20th century
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- May require human verification for complex argumentative structures
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- Performance may vary on texts significantly different from training data (1908 newspapers)
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## Training and Evaluation Data
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The model was trained on a custom dataset of historical newspaper texts from Italian, German, French, and English sources, primarily from 1908, with argumentative annotations.
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## Training Procedure
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### Stage 1: Supervised Fine-Tuning (LoRA/PEFT)
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#### Training Hyperparameters
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- **learning_rate:** 3e-05
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- **train_batch_size:** 1
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- **eval_batch_size:** 8
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- **seed:** 42
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- **gradient_accumulation_steps:** 8
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- **total_train_batch_size:** 8
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- **optimizer:** paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08
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- **lr_scheduler_type:** cosine
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- **lr_scheduler_warmup_ratio:** 0.05
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- **lr_scheduler_warmup_steps:** 50
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- **num_epochs:** 3
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- **mixed_precision_training:** Native AMP
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#### Training Results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 1.5443 | 1.0879 | 50 | 2.6414 |
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| 1.1074 | 2.1758 | 100 | 2.6980 |
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**Final Evaluation Loss:** 2.6980
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### Stage 2: GRPO Post-Training
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This model was further trained using **Group Relative Policy Optimization (GRPO)**, a reinforcement learning method that optimizes the model using group-based rewards to:
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- Improve argument extraction quality
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- Eliminate duplicate extractions
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- Enhance confidence calibration
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- Maintain multilingual performance
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**Training Configuration:**
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| Parameter | Value |
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|-----------|-------|
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| LoRA adapters | ~1-2% parameters updated |
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| Learning rate | 3e-05 |
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| Epochs | 3 |
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| Optimizer | 8-bit + AMP |
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| Schedule | Cosine + warmup |
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## Usage Example
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### Using Transformers (Recommended for Argument Mining)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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# Load model
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model = AutoModelForCausalLM.from_pretrained(
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"oberbics/llama-3.1-8B-newspaper_argument_mining",
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("oberbics/llama-3.1-8B-newspaper_argument_mining")
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tokenizer.pad_token = tokenizer.eos_token
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# System prompt for argument extraction
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SYSTEM_PROMPT = '''You are an expert at analyzing historical texts and you hate to summarize
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OUTPUT FORMAT - EXACTLY these 4 XML tags and NOTHING else:
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<argument>Original argument text OR "NA"</argument>
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<claim>Core claim (implication) in one sentence OR "NA"</claim>
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<explanation>Why this is an argument OR "NA"</explanation>
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<confidence>0-1</confidence>
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EXAMPLE WITH STRONG ARGUMENT:
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<argument>Il giornale L'Italia moderna economica e finanziaria nel numero di oggi propone che non si facciano sottoscrizioni, le quali per quanto larghe sarebbero sempre impari ai bisogni, ma che il Parlamento stabilisca pochi centesimi addizionali per ogni lira su tutte le imposte e tasse (esclusi soltanto i dazi doganali la cui misura è vincolata da trattati di commercio).</argument>
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<claim>Private subscriptions are inadequate for earthquake relief; parliamentary taxation would be more effective.</claim>
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<explanation>The newspaper explicitly argues against private subscriptions as insufficient and proposes a specific alternative solution through parliamentary taxation, making a clear comparative argument about funding mechanisms.</explanation>
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<confidence>0.95</confidence>
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EXAMPLE WITHOUT ARGUMENT:
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<argument>NA</argument>
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<claim>NA</claim>
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<explanation>NA</explanation>
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<confidence>0.9</confidence>
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RULES:
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- CRITICAL: NEVER REPEAT ARGUMENTS - Each argument must be COMPLETELY UNIQUE
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- Only output arguments that appear verbatim (or nearly verbatim) in the text
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- NO SUMMARY; ONLY EXACT EXTRACTION FROM THE TEXT
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- Extract only original text without changes or use NA when you did not find an argument
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- If no argument exists, use NA for ALL fields
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- More than one argument possible for one article'''
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# Example article
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article = """Your historical newspaper text here"""
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# Prepare messages
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": f"Extract argumentative units from historical text in their original form, no summaries.\n{article}"}
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]
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# Generate
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inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
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outputs = model.generate(
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inputs,
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max_new_tokens=800,
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temperature=0.1,
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top_p=0.95,
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repetition_penalty=1.15,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id
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)
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response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
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print(response)
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```
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## Framework Versions
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### Stage 1 (Fine-tuning)
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- **PEFT:** 0.17.1
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- **Transformers:** 4.57.1
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- **PyTorch:** 2.9.0+cu128
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- **Datasets:** 4.3.0
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- **Tokenizers:** 0.22.1
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### Stage 2 (GRPO)
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- **TRL:** 0.25.0.dev0
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- **Transformers:** 4.57.1
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- **PyTorch:** 2.4.0
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- **Datasets:** 4.3.0
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- **Tokenizers:** 0.22.1
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## Citations
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Cite GRPO as:
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```bibtex
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@article{shao2024deepseekmath,
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title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
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author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
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year = 2024,
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eprint = {arXiv:2402.03300},
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}
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```
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Cite TRL as:
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/huggingface/trl}}
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}
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```
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Cite the base Llama 3.1 model as:
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```bibtex
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@article{llama3,
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title={The Llama 3 Herd of Models},
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author={AI@Meta},
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year={2024},
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journal={arXiv preprint arXiv:2407.21783}
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}
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```
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## License
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This model inherits the Llama 3.1 Community License. See [LICENSE](https://ai.meta.com/llama/license/) for details.
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## Model Card Contact
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For questions or issues, please open an issue on the [model repository](https://huggingface.co/oberbics/llama-3.1-8B-newspaper-argument-mining/discussions).
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109
chat_template.jinja
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
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{%- endif %}
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{%- if not tools_in_user_message is defined %}
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{%- set tools_in_user_message = true %}
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{%- endif %}
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{%- if not date_string is defined %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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{%- set system_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- set system_message = "" %}
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{%- endif %}
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{#- System message + builtin tools #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if builtin_tools is defined or tools is not none %}
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{{- "Environment: ipython\n" }}
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{%- endif %}
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{%- if builtin_tools is defined %}
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{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
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{%- endif %}
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{{- "Cutting Knowledge Date: December 2023\n" }}
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{{- "Today Date: " + date_string + "\n\n" }}
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{%- if tools is not none and not tools_in_user_message %}
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{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{%- endif %}
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{{- system_message }}
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{{- "<|eot_id|>" }}
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{#- Custom tools are passed in a user message with some extra guidance #}
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{%- if tools_in_user_message and not tools is none %}
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{#- Extract the first user message so we can plug it in here #}
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{%- if messages | length != 0 %}
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{%- set first_user_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
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{%- endif %}
|
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{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
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{{- "Given the following functions, please respond with a JSON for a function call " }}
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{{- "with its proper arguments that best answers the given prompt.\n\n" }}
|
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
|
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{{- first_user_message + "<|eot_id|>"}}
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{%- endif %}
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{%- for message in messages %}
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{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
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{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
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{%- elif 'tool_calls' in message %}
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{%- if not message.tool_calls|length == 1 %}
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{{- raise_exception("This model only supports single tool-calls at once!") }}
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{%- endif %}
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{%- set tool_call = message.tool_calls[0].function %}
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{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
|
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
||||
{{- "<|python_tag|>" + tool_call.name + ".call(" }}
|
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{%- for arg_name, arg_val in tool_call.arguments | items %}
|
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{{- arg_name + '="' + arg_val + '"' }}
|
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{%- if not loop.last %}
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{{- ", " }}
|
||||
{%- endif %}
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{%- endfor %}
|
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{{- ")" }}
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{%- else %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
||||
{{- '{"name": "' + tool_call.name + '", ' }}
|
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{{- '"parameters": ' }}
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{{- tool_call.arguments | tojson }}
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{{- "}" }}
|
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{%- endif %}
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{%- if builtin_tools is defined %}
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{#- This means we're in ipython mode #}
|
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{{- "<|eom_id|>" }}
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{%- else %}
|
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{{- "<|eot_id|>" }}
|
||||
{%- endif %}
|
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{%- elif message.role == "tool" or message.role == "ipython" %}
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{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
|
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{%- if message.content is mapping or message.content is iterable %}
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{{- message.content | tojson }}
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||||
{%- else %}
|
||||
{{- message.content }}
|
||||
{%- endif %}
|
||||
{{- "<|eot_id|>" }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
|
||||
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23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
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||||
{
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||||
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||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
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oid sha256:91ce09b812b826621c65d41dd558d7b1515442aa481212456f43775f942643ba
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size 17210183
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2070
tokenizer_config.json
Normal file
2070
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
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||||
version https://git-lfs.github.com/spec/v1
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size 7096
|
||||
Reference in New Issue
Block a user