Update: 사용 예제 정리

README.md

@@ -110,20 +110,16 @@ Just using `evaluate-metric/bleu` and `evaluate-metric/rouge` in huggingface `ev
 from transformers.pipelines import Text2TextGenerationPipeline
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 texts = ["그러게 누가 6시까지 술을 마시래?"]
-tokenizer = AutoTokenizer.from_pretrained(
-    args.model_name_or_path,
-)
-model = AutoModelForSeq2SeqLM.from_pretrained(
-    args.model_name_or_path,
-)
+tokenizer = AutoTokenizer.from_pretrained("lIlBrother/ko-barTNumText")
+model = AutoModelForSeq2SeqLM.from_pretrained("lIlBrother/ko-barTNumText")
 # BartText2TextGenerationPipeline is implemented above (see 'Use')
 seq2seqlm_pipeline = BartText2TextGenerationPipeline(model=model, tokenizer=tokenizer)
 kwargs = {
-    "min_length": args.min_length,
-    "max_length": args.max_length,
-    "num_beams": args.beam_width,
-    "do_sample": args.do_sample,
-    "num_beam_groups": args.num_beam_groups,
+    "min_length": 0,
+    "max_length": 1206,
+    "num_beams": 100,
+    "do_sample": False,
+    "num_beam_groups": 1,
 }
 pred = seq2seqlm_pipeline(texts, **kwargs)
 print(pred)