[result] Askathon QA comparison table for distilbert and nasa-v6

[NISH1001]

We use askathon cleaned response dataset to evaluate the nasa-v6 models against vanilla distilbert model.
We're using onnx version of nasa-v6 model.

Dataset loader is tentatively:

def load_askathon_clean(path: str) -> pd.DataFrame:
    data = pd.read_csv(path)
    data = data.drop(columns=["Email Address"]).reset_index(drop=True)
    data.rename(columns={
        data.columns[0] : "context",
        data.columns[1]: "id",
        data.columns[2]: "source",
        data.columns[3]: "topics",
        data.columns[4]: "q1",
        data.columns[5]: "a1",
        data.columns[6]: "q2",
        data.columns[7]: "a2",
        data.columns[8]: "q3",
        data.columns[9]: "a3",
        data.columns[10]: "q4",
        data.columns[11]: "a4",
        data.columns[12]: "q5",
        data.columns[13]: "a5"
    }, inplace=True)
    data.drop(columns=["source", "topics"], inplace=True)
    return data

def create_qa_dataset(data: pd.DataFrame) -> pd.DataFrame:
    res = []
    q_keys = [f"q{i}" for i in range(1, 6)]
    a_keys = [f"a{i}" for i in range(1, 6)]
    
    def _index_fn(context: str, answer: str) -> int:
        try:
            return context.lower().index(answer.rstrip(" ,.!?").lower())
        except ValueError:
            return -1
    
    for _df in data.itertuples():
        tmp = []
        for qk, ak in zip(q_keys, a_keys):
            q, a = getattr(_df, qk), getattr(_df, ak)
            
            if not isinstance(a, str):
                continue
            idx = _index_fn(_df.context, a)
            if idx > -1:
                tmp.append(dict(
                    id=str(_df.id),
                    context=_df.context,
                    question=q,
                    answer_text=a,
                    answer_start=idx,
                ))
        res.extend(tmp)
    return pd.DataFrame(res)

data = create_qa_dataset(load_askathon_clean("data/askathon.csv"))

---

metric	distilbert	nasa-v6-onnx
RougeMetric	0.569272	0.60426
BartScore	-3.4921	-3.26777
F1Metric	0.652469	0.687898
MeteorMetric	0.525007	0.563029
BertScore	0.74016	0.760525
AccuracyMetric	0.545293	0.588434
ExactMatchMetric	0.265625	0.28125
BleuMetric	0.228825	0.280672

---

Evaluation is done through evalem with following tentative pipeline code:

from evalem.nlp.evaluators import QAEvaluator
from evalem.nlp.models import QuestionAnsweringHFPipelineWrapper
from evalem.nlp.metrics import BartScore, BertScore, BleuMetric, MeteorMetric, ExactMatchMetric, RougeMetric
from evalem import NamedSimpleEvaluationPipeline
from evalem.misc.utils import build_comparison_table


# define models
wrapped_model = QuestionAnsweringHFPipelineWrapper(device="mps")
wrapped_model_2 = QuestionAnsweringHFPipelineWrapper.from_onnx(
    model="tmp/onnx/nasa-v6/",
    tokenizer="tmp/onnx/nasa-v6/",
    device="mps"
)

# define evaluators/metrics
evaluators_common = [
    QAEvaluator(),
    BertScore(device="mps"),
    BartScore(device="mps"),
    RougeMetric(),
    MeteorMetric(),
    BleuMetric(),
]

# build pipelines
eval_pipe = NamedSimpleEvaluationPipeline(
    model=wrapped_model,
    evaluators=evaluators_common,
    name="distilbert"
)

eval_pipe_2 = NamedSimpleEvaluationPipeline(
    model=wrapped_model_2,
    evaluators=evaluators_common,
    name="nasa-v6-onnx"
)

# evaluate and get comparison table
results = build_comparison_table(
    eval_pipe, eval_pipe_2,
    inputs=list(data[["context", "question"]].T.to_dict().values()),
    references=data["answer_text"].to_list(),
)

---

cc: @muthukumaranR @xhagrg