Update README.md

README.md

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 ### Training Data
 
 The primary dataset used to train and evaluate this model is EZ-STANCE. This dataset contains labeled stances on a variate of topics across politics and pop culture. This dataset contains the following four fields which are relevant for this project.
-•	Text: The source Tweet that stances will be generated from.
-•	Target: A modification of the source Tweet which indicates a favorable, neutral, or unfavorable stance. The creators of the datasets used human annotators to generate these values. 
-•	Stance: The stance label for the Target text (Favorable, Unfavorable, or Neutral). 
-	Using this dataset, I was able to provide the model with the source text and ask it to determine whether the author of that text would have a favorable, unfavorable, or no stance towards the target topic or claim. I did not make any modifications to those fields in the training dataset, other than adding structure around the data in the prompt to clarify to the model what I wanted it to provide. The second component of the task was to have the model provide step-by-step reasoning behind the stance it provided. This reasoning was not included in the training dataset, but I thought it was important to have the model generate this reasoning because an explanation for the user to see is important when considering that the original reason behind this model is to help build trust. 
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+Text: The source Tweet that stances will be generated from.
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+Target: A modification of the source Tweet which indicates a favorable, neutral, or unfavorable stance. The creators of the datasets used human annotators to generate these values. 
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+Stance: The stance label for the Target text (Favorable, Unfavorable, or Neutral).
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+Using this dataset, I was able to provide the model with the source text and ask it to determine whether the author of that text would have a favorable, unfavorable, or no stance towards the target topic or claim. I did not make any modifications to those fields in the training dataset, other than adding structure around the data in the prompt to clarify to the model what I wanted it to provide. The second component of the task was to have the model provide step-by-step reasoning behind the stance it provided. This reasoning was not included in the training dataset, but I thought it was important to have the model generate this reasoning because an explanation for the user to see is important when considering that the original reason behind this model is to help build trust. 
 
 ### Training Method
 The base model used for this project was Qwen2.5-7B-Instruct-1M . I chose this model because it could handle large context windows, was instruction tuned, and its relatively low number of parameters would make it more efficient to train. The final model was trained on the stance classification task using the LoRA method of PEFT.  Then, few-shot chain-of-thought prompting was used to ask the final model for reasoning behind the stances it generated. When reviewing the output of the model on my task, I observed that few-shot prompting alone went a very long way in improving the output of the model when having it explain its reasoning, which is why I only trained the model on the stance classification component of the task. I used PEFT over full fine-tuning because I did not want to drastically change my model since it was already performing well on the reasoning task. Also, since I am using a 7B parameter model and my desired model output is open-ended, I had concerns around the efficiency of full-fine tuning. My aim was to take a targeted training approach to assist the model on its classification task.
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 MARKDOWN RESULTS TABLE PLACEHOLDER
 Another limitation of this approach is that I was only training on the stance classification task. Given that the minimally invasive approach of PEFT did not have much impact, it could be worth exploring more impactful approaches like full fine-tuning. In that case, there is a risk that training the entire model will damage its ability to reason logically.
 Great Example of Why “None” is hard:
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 Statement: This had the effect of taking the fringe religious doctrine of NI and making it much more common and American evangelical teaching but also taught a lot of churches that private education is a massive cash cow money laundry tax dodge on top of being a church
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 Topic or Claim: Private education institutions could face scrutiny in the future for potential financial irregularities, leading to investigations into money laundering and tax evasion practices.
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 Stance: FAVOR
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 Response: The author supports the claim by linking private education institutions to financial irregularities, including money laundering and tax evasion, suggesting that these institutions could indeed face future scrutiny and investigations. This aligns with the claim that private education may be at risk due to such practices.
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 Example of the model perhaps confusing sentiment and stance, and maybe struggling with slang:
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 Statement: Let s aim to recycle and reuse as much as we can. . . . . noplastic plasticfree plasticpollution environment environmentalawareness environmentalfriendly savetheplanet plasticpollution noplasticbags noplasticwaste makeachange makeachangetoday
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 Topic or Claim: plasticpollution
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 Stance: FAVOR
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 Response: The author is in favor of reducing plastic pollution by promoting recycling, reusing, and minimizing plastic use. Phrases like "make a change today" and "save the planet" indicate a strong commitment to addressing environmental issues related to plastic pollution.
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 Example of getting the right answer for the wrong reason:
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 Statement: Two young Russian men decided to flee military mobilization and took the boat across the Bering Strait to a small American island off the coast of Alaska. They have now applied for asylum as refugees. Will the U.S. see a new wave of migrants in the north? 99 582 2,367
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 Topic or Claim: Two Russian men have fled military mobilization and taken a boat across the Bering Strait to Alaska, seeking asylum as refugees.
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 Stance: FAVOR
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 Response: The author supports the claim by highlighting the Russian men's actions and the potential for increased migration, suggesting a favorable view of their situation and the broader implications. This indicates an overall positive stance toward the topic.
 
 ## Sources and References