# Accessing and analyzing evaluation results


After your evaluation job completes successfully, you can access and analyze the results using the information in this section. Based on the `output_s3_path` (such as `s3://output_path/`) defined in the recipe, the output structure is the following:

```
job_name/
├── eval-result/
│    └── results_[timestamp].json
│    └── inference_output.jsonl (only present for gen_qa)
│    └── details/
│        └── model/
│            └── execution-date-time/
│                └──details_task_name_#_datetime.parquet
└── tensorboard-results/
    └── eval/
        └── events.out.tfevents.[timestamp]
```

Metrics results are stored in the specified S3 output location `s3://output_path/job_name/eval-result/result-timestamp.json`.

Tensorboard results are stored in the S3 path `s3://output_path/job_name/eval-tensorboard-result/eval/event.out.tfevents.epoch+ip`.

All inference outputs, except for `llm_judge` and `strong_reject`, are stored in the S3 path: `s3://output_path/job_name/eval-result/details/model/taskname.parquet`.

For `gen_qa`, the `inference_output.jsonl` file contains the following fields for each JSON object:
+ prompt - The final prompt submitted to the model
+ inference - The raw inference output from the model
+ gold - The target response from the input dataset
+ metadata - The metadata string from the input dataset if provided

To visualize your evaluation metrics in Tensorboard, complete the following steps:

1. Navigate to SageMaker AI Tensorboard.

1. Select **S3 folders**.

1. Add your S3 folder path, for example `s3://output_path/job-name/eval-tensorboard-result/eval`.

1. Wait for synchronization to complete.

The time series, scalars, and text visualizations are available.

We recommend the following best practices:
+ Keep your output paths organized by model and benchmark type.
+ Maintain consistent naming conventions for easy tracking.
+ Save extracted results in a secure location.
+ Monitor TensorBoard sync status for successful data loading.

You can find SageMaker HyperPod job error logs in the CloudWatch log group `/aws/sagemaker/Clusters/cluster-id`.

## Log Probability Output Format


When `top_logprobs` is configured in your inference settings, the evaluation output includes token-level log probabilities in the parquet files. Each token position contains a dictionary of the top candidate tokens with their log probabilities in the following structure:

```
{
"Ġint": {"logprob_value": -17.8125, "decoded_value": " int"},
"Ġthe": {"logprob_value": -2.345, "decoded_value": " the"}
}
```

Each token entry contains:
+ `logprob_value`: The log probability value for the token
+ `decoded_value`: The human-readable decoded string representation of the token

The raw tokenizer token is used as the dictionary key to ensure uniqueness, while `decoded_value` provides a readable interpretation.