fixed minor bugs preventing sweeps

Author: Ubuntu

src/bart_reddit_lora/train.py

@@ -61,7 +61,7 @@ class CustomTrainingArgs(Seq2SeqTrainingArguments):
     logging_steps: int = 50
     save_total_limit: int = 2
     load_best_model_at_end: bool = True
-    metric_for_best_model: str = "eval/loss"
+    metric_for_best_model: str = "loss"
     greater_is_better: bool = False
 
     fp16: bool = True
@@ -147,7 +147,7 @@ def to_qa(ex):
             "validation": str(data_args.validation_file),
             "test": str(data_args.test_file),
         }
-        ds = load_dataset("csv", data_files=data_files, streaming=True)
+        ds = load_dataset("csv", data_files=data_files)
     # # load and tokenize dataset
     # # load CSVs
     # data_files = {

sweep.yaml

@@ -1,14 +1,10 @@
 # sweep spec for bart-base-reddit-lora
 program: scripts/train.py
-
-project: bart-base-reddit-lora
-entity:  codinglabsong-keio-jp
+name: bart-base-reddit-lora-sweep
 
 method: bayes # {grid | random | bayes}
-run_cap: 15 # sweep run limit
-
 metric: # what to optimise
-  name: eval/loss # must match the key in evaluation.compute_metrics returns
+  name: eval_loss # must match the key in .evaluate() returns
   goal: minimize
 
 parameters:
@@ -22,6 +18,10 @@ parameters:
     values: [32, 64]
   train_sample:
     values: [True]
+run_cap: 10 # sweep run limit
+
+project: bart-base-reddit-lora
+entity: codinglabsong-keio-jp
 
 # early_terminate:
 #   type: hyperband