new model and fixed random seed

app.py

@@ -6,6 +6,7 @@ import os
 
 from PIL import Image
 from io import BytesIO
+import transformers
 from transformers import VisionEncoderDecoderModel, VisionEncoderDecoderConfig, DonutProcessor, DonutImageProcessor, AutoTokenizer
 
 from logits_ngrams import NoRepeatNGramLogitsProcessor, get_table_token_ids
@@ -29,6 +30,7 @@ def run_prediction(sample, model, processor, mode):
                     np.float32,
                 ), return_tensors="pt").pixel_values
 
+    transformers.set_seed(42)
     with torch.no_grad():
         outputs = model.generate(
             pixel_values.to(device),
@@ -37,7 +39,7 @@ def run_prediction(sample, model, processor, mode):
             do_sample=True,
             top_p=0.92, 
             top_k=5,
-            no_repeat_ngram_size=25,
+            no_repeat_ngram_size=10,
             num_beams=3,
             output_attentions=False,
             output_hidden_states=False,
@@ -81,7 +83,7 @@ else:
 st.image(image, caption='Your target document')
 
 with st.spinner(f'Processing the document ...'):
-        pre_trained_model = "unstructuredio/chipper-fast-fine-tuning-oct-23-release"
+        pre_trained_model = "unstructuredio/chipper-fast-fine-tuning"
         processor = DonutProcessor.from_pretrained(pre_trained_model, token=os.environ['HF_TOKEN'])
         
         device = "cuda" if torch.cuda.is_available() else "cpu"