updated sweep.yaml with correct syntax

Author: Ubuntu

src/korea_travel_guide/data.py

@@ -192,7 +192,7 @@ def split_and_save(df, out_dir: Union[str, Path]):
 def tokenize_and_format(
     ds: DatasetDict,
     checkpoint: str = "facebook/bart-base",
-    max_input_length: int = 224,  # max 1024
+    max_input_length: int = 1024,  # max 1024 224
     max_target_length: int = 800,  # max 1024
 ) -> Tuple[DatasetDict, AutoTokenizer]:
     tok = AutoTokenizer.from_pretrained(checkpoint)

src/korea_travel_guide/train.py

@@ -173,7 +173,7 @@ def main() -> None:
         eval_dataset=ds_tok["validation"],
         tokenizer=tok,
         data_collator=data_collator,
-        compute_metrics=build_compute_metrics(tok),
+        # compute_metrics=build_compute_metrics(tok),
     )
 
     trainer.train()

sweep.yaml

@@ -1,23 +1,27 @@
 # sweep spec for Korea-Travel-Guide BART
 program: scripts/train.py
+
+project: bart-base-korea-travel-guide-lora
+entity:  codinglabsong-keio-jp
+
 method: bayes # {grid | random | bayes}
-run_cap: 5 # sweep run limit
+run_cap: 10 # sweep run limit
 
 metric: # what to optimise
-  name: eval/rougeL # must match the key in evaluation.compute_metrics returns
+  name: eval/loss # must match the key in evaluation.compute_metrics returns
   goal: maximize
 
 parameters:
   learning_rate:
-    min: 1e-5
-    max: 1e-3
-    distribution: log_uniform
+    min: 0.00001
+    max: 0.001
+    distribution: log_uniform_values
   num_train_epochs:
-    values: 1
+    values: [1]
   peft_rank:
     values: [4, 8, 16]
   train_sample:
-  values: True
+    values: [True]
 
 early_terminate:
   type: hyperband