---
library_name: transformers
language:
- en
metrics:
- accuracy
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
The base model is llama-3-smaug 8b llm .
smaug is suffix used by abacusai that trains llms using DPO strategy

The final model is the base model finetuned using LORA technique on entity-level-sentiment-analysis data.
How does the data look :
{ 'text' : "Ashley was seen with suspects before the day of crime. Witnesses say that Ashley knew about the crime beforehand.
            Police are searching her home in pursuance of evidences.
            Ashley's parents, Ron and Maria are renowned business people of the town who are known for their charity works.",
 'entity': "Ashley",
 'label': 'Negative'}

## Model Details
Open source model that can be used to determine sentiment analysis of a person or company that is mentioned in say, a newspaper article.
### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.


- **Final model finetuned by:** rajiv-data-chef 
- **Funded by [optional]:** rajiv-data-chef
- **Shared by [optional]:** rajiv-data-chef
- **Model type:** llama-3-smaug-8b 
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** abacusai/Llama-3-Smaug-8B

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### 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.

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## 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]
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- **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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**APA:**

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## Glossary [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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