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Tools

Human Annotations

Humans can annotate the results (e.g. great, ok, bad)

Doccano

See https://doccano.github.io/doccano/ for installation.

  1. Create a new project e.g. Area Summary
  2. Create the tags e.g. Good, Bad, Neutral.
  3. We want these options for the project:
  • Allow single label.
  • Randomize Document order
  • Share annotations across users

Import dataset

We want the data in the following format for doccano:

[
    {
        "context": "Terrible customer service.",
        "response": ["negative"]
    },
    {
        "context": "Really great transaction.",
        "response": ["positive"]
    },
    ...
]

Here is an example of how to convert an unlabeled dataset for import into doccano. This creates a single input file from all model outputs merged together. The results are randomized in doccano and can be rated blind:

cat model_outputs/area_summary/*/*.yaml  | python script/import.py > model_outputs/area_summary/dataset.json

Create labels of Good, Bad, and Neutral and export the dataset when done labeling.

Export Ratings

Convert the labeled JSONL output into yaml.

$ cat model_outputs/area_summary_agent/annotations.jsonl  | python script/export.py  > model_outputs/area_summary_agent/annotations.yaml

The output file contains the labeled task results which can be joined with the original model output and used for computing metrics:

---
uuid: e0fee440-e99d-4ecf-8dc9-8f686e51db3e
task_id: apartament4-bedroom-1-bedroom-1-light-on
label:
- Good

---
uuid: c28639af-7184-42c6-a0e5-7de453e8413e
task_id: apartament4-bedroom-1-bedroom-1-light-off
label:
- Neutral

Metrics

Compute metrics of annotated datasets.

Human annotated dataset.

Here is an example of dumping the aggregate stats of doccano ratings by model:

$ python3 metrics/human_eval_metrics.py --model_outputs model_outputs/area_summary/
---
gemini-pro:
  Bad: 7
  Good: 46
  Neutral: 7
gpt-3.5:
  Bad: 3
  Good: 55
  Neutral: 2
mistral-7b-instruct:
  Bad: 29
  Good: 22
  Neutral: 9

Offline Evaluation

This is an example of computing an offline evaluation from a labeled dataset:

$ python3 metrics/offline_eval_metrics.py --model_outputs=model_outputs/anomaly/
---
leaderboard:
  mistral-7b-instruct-10-shot: 91.1%
  llama3-10-shot: 87.8%
  llama3-zero-shot: 87.8%
  gpt-3.5-zero-shot: 87.8%
  mistral-7b-instruct-zero-shot: 84.4%
  gemma-zero-shot: 83.3%
  gpt-3.5-10-shot: 80.0%
  gemma-5-shot: 77.8%
  gemma-3-shot: 76.7%
  gemma-10-shot: 53.3%
hardest_tasks:
- The hallway motion sensor is triggered, indicating movement. (0.0)
- The hallway light is on and the motion sensor is triggered. (0.0)
- The hallway lights are off, and the hallway motion sensor has not detected any movement
  for the past hour. (0.0)
- The garage door has been open for 12 minutes. (0.0)
- The hallway lights are on and the motion sensor has detected motion. (11.11111111111111)