Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Needs Clarification (NC): a submission which does not meet the reviewing criteria and needs clarification for its described problem or solution
review_content: summary_of_contributions: The authors used the Hugging Face Trainer to train a Kinyarwanda tweet classification model by finetuning a range of pre-trained Transformer models. They generated their finetuning dataset using the Google API to translate tweets from Kinyarwanda to English.
strengths_and_weaknesses: Although an interesting start, the paper appears incomplete, and so it is hard to form any conclusions about the work as a reviewer.
suggested_changes: Use the most authoritative base-model available, e.g. for ` xlm-roberta-base` use (and cite) the version provided by Facebook, or explain why you have selected a different version of the model.
Work on the formatting of your paper! I would have loved to see/understand the results in the table.
review_title: null

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: The authors compared the performance of a pretrained Kinyarwanda model on Kinyarwanda texts against a pretrained English model on translated kinyarwanda texts. For the English model, the Kinyarwanda texts were translated to English using google translate, the evaluation metric was an F1-score.
strengths_and_weaknesses: **Strengths**   
The authors' attempt to compare the performance of a transformer model trained on Kinyarwanda with a larger English-trained model on English translations of Kinyarwanda texts is intriguing.

**Weakness**  
As mentioned above, it was intriguing, but the authors failed to properly communicate their results. The result table written by the authors was incomplete and one could not properly compare all the results from their findings.


suggested_changes: * Proper clarification of their research question and objective with the use of clear and concise language.
* Also provide a thorough review of the related work and related studies.
* A more detailed report of their approach and findings with interpretation and discussion of findings.
* Use proper formatting for your result table.
review_title: null

Meta-review: metareview: **Summary**
- The paper presents a sentiment classification model for Kinyarwanda by finetuning different pre-trained Transformer models. They used Google Translator to translate tweets from Kinyarwanda to English. 

**Strength**
- The paper has an interesting motivation. However, it’s incomplete work. 
**Weakness**
- The paper is literally incomplete and doesn’t fulfill any of the CCR requirements.

confidence: 5: The area chair is absolutely certain
Decision: Invite to revise