| title | emoji | colorFrom | colorTo | sdk | sdk_version | app_file | pinned | license |
|---|---|---|---|---|---|---|---|---|
FLARE |
🐠 |
pink |
pink |
gradio |
3.34.0 |
app.py |
false |
mit |
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
- Add new
[LAN]_result.csv, which should be format like
| Model | Task Metric 1 | Task Metric 2 |
|---|---|---|
| GPT-4 | 0.28 | 0 |
- Add new COL variable on
app.py
SPA_COLS = [
("Model", "str"),
("MultiFin-F1", "number"),
]- Add new Col categorization on
app.py
spa_cols = [col_name for col_name, _ in SPA_COLS]
spa_cates = {
"Sentiment Analysis": ["Model", "TSA-Acc", "TSA-F1", "FinanceES-Acc", "FinanceES-F1"],
"Examination": ["Model", "EFP-Acc", "EFP-F1", "EFPA-Acc", "EFPA-F1"],
"Classification": ["Model", "MultiFin-Acc", "MultiFin-F1"],
"Text Summarization": ["Model", "FNS-Rouge1", "FNS-Rouge2", "FNS-RougeL",],
}- Add new key to lan dict on
app.py
df_lang = {
"English": create_df_dict("english", eng_cols, eng_cates),
"Spanish": create_df_dict("spanish", spa_cols, spa_cates),
}- If new categories need to define new column selection rules, add it like:
elif key == "Credit Scoring":
tdf = tdf[[val for val in tdf.columns if "Acc" in val]]
elif key == "Text Summarization":
tdf = tdf[[val for val in tdf.columns if "Bert" in val or "Rouge" in val]]
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