---
base_model: meta-llama/Llama-3.1-8B
library_name: peft
datasets:
- baartmar/nsm_dataset
license: cc-by-nc-sa-4.0
language:
- en
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
A fine-tuned LLM for paraphrasing word-meanings using the natural semantic primes.


## Model Details

### Model Description

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A fine-tuned LLM for paraphrasing word-meanings using the natural semantic primes.


- **Developed by:** 
- **Model type:** Text Generation
- **Language(s) (NLP):** English
- **License:** cc-by-nc-sa-4.0
- **Finetuned from model:** Llama-3.1-8B

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/OSU-STARLAB/DeepNSM
- **Paper:** https://arxiv.org/abs/2505.11764

## Uses

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
To use the DeepNSM model to generate an NSM paraphrase of a word-meaning, you should structure your prompts in the following format:
```
Word: dangerous
Examples:
don't go near the dangerous fire
walking outside at night is dangerous
Paraphrase:
```

### Downstream Use [optional]

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[More Information Needed]

### Out-of-Scope Use

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[More Information Needed]

## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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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.

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure

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#### 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]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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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]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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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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### Framework versions

- PEFT 0.15.2