### Issues

1. **Issue: Missing Model Output for Queries**
   - **Evidence:** Entries like `Q: I will have been watching the concert for an hour. Change tense to PRESENT PERFECT CONTINUOUS:&numsp;A:&numsp;`
   - **Description:** The `dummy_model.transcript.md` file lists queries without corresponding outputs (`A:`), leading to confusion about expected model responses.

2. **Issue: Inconsistency in Task Description**
   - **Evidence:** `README.md` states, “the language model is required to output the same sentence if the target tense matches the input tense,” but lacks examples.
   - **Description:** The documentation does not clarify how to assess the model when input and target tenses are identical, potentially causing misunderstandings.

3. **Issue: Grammatical Errors in Example Queries**
   - **Evidence:** Query “Q: I had been writting articles on different topics before you came. Change tense to FUTURE PERFECT CONTINUOUS:&numsp;A:&numsp;”
   - **Description:** The typo "writting" should be "writing," which can mislead models and provide incorrect context.

4. **Issue: Redundant Information in Metadata**
   - **Evidence:** `task.json` includes unnecessary metadata like “canary GUID” and "START HEADER."
   - **Description:** Excess metadata can obscure task details, complicating the extraction of relevant information.

5. **Issue: Lack of Reference Context in Data Source Explanation**
   - **Evidence:** `README.md` mentions, “The sentences are either hand-picked from English grammar worksheets… or constructed manually.”
   - **Description:** The dataset lacks citations for the grammar worksheets, raising concerns about source quality and reliability.

These issues highlight areas for improving dataset clarity, usability, and accuracy.