aehrc
/

cxrmate-rrg24

Feature Extraction

vision-encoder-decoder

Model card Files Files and versions

anicolson commited on Jan 23

Commit

fc8cc82

·

verified ·

1 Parent(s): c1b861c

Update README.md

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -61,7 +61,7 @@ batch = next(iter(dataloader))
 output_ids = model.generate(
     pixel_values=batch['images'],
     max_length=512,
-    num_beams=4,
     bad_words_ids=[[tokenizer.convert_tokens_to_ids('[NF]')], [tokenizer.convert_tokens_to_ids('[NI]')]],
 )
 findings, impression = model.split_and_decode_sections(output_ids, tokenizer)
@@ -73,7 +73,7 @@ findings, impression = model.split_and_decode_sections(output_ids, tokenizer)
 output_ids = model.generate(
     pixel_values=batch['images'],
     max_length=512,
-    num_beams=4,
     bad_words_ids=[[tokenizer.convert_tokens_to_ids('[NF]')]],
     eos_token_id=tokenizer.sep_token_id,
 )
@@ -86,7 +86,7 @@ findings, _ = model.split_and_decode_sections(output_ids, tokenizer)
 output_ids = model.generate(
     pixel_values=batch['images'],
     max_length=512,
-    num_beams=4,
     bad_words_ids=[[tokenizer.convert_tokens_to_ids('[NI]')]],
     input_ids=torch.tensor([[tokenizer.bos_token_id, tokenizer.convert_tokens_to_ids('[NF]'), tokenizer.sep_token_id]]*mbatch_size, device=device, dtype=torch.long),
 )

 output_ids = model.generate(
     pixel_values=batch['images'],
     max_length=512,
+    num_beams=1,
     bad_words_ids=[[tokenizer.convert_tokens_to_ids('[NF]')], [tokenizer.convert_tokens_to_ids('[NI]')]],
 )
 findings, impression = model.split_and_decode_sections(output_ids, tokenizer)
 output_ids = model.generate(
     pixel_values=batch['images'],
     max_length=512,
+    num_beams=1,
     bad_words_ids=[[tokenizer.convert_tokens_to_ids('[NF]')]],
     eos_token_id=tokenizer.sep_token_id,
 )
 output_ids = model.generate(
     pixel_values=batch['images'],
     max_length=512,
+    num_beams=1,
     bad_words_ids=[[tokenizer.convert_tokens_to_ids('[NI]')]],
     input_ids=torch.tensor([[tokenizer.bos_token_id, tokenizer.convert_tokens_to_ids('[NF]'), tokenizer.sep_token_id]]*mbatch_size, device=device, dtype=torch.long),
 )