Update README.md

README.md

@@ -18,24 +18,14 @@ model_name = "SynapseQAI/T5-base-wmt14"
 tokenizer = T5Tokenizer.from_pretrained(model_name)
 model = T5ForConditionalGeneration.from_pretrained(model_name)
 
-# Function to translate using a different generation strategy
-def translate(sentence, strategy='beam_search'):
+# Function to translate using beam search (default strategy)
+def translate(sentence):
     # Prepare the input for the model
-    input_text = f"translate French to English: {sentence}"
+    input_text = f": {sentence}"
     input_ids = tokenizer(input_text, return_tensors="pt").input_ids
 
-    # Choose generation strategy
-    if strategy == 'beam_search':
-        outputs = model.generate(input_ids, num_beams=3, max_length=50, early_stopping=True)
-    elif strategy == 'top_k_sampling':
-        outputs = model.generate(input_ids, do_sample=True, top_k=50, max_length=50)
-    elif strategy == 'top_p_sampling':
-        outputs = model.generate(input_ids, do_sample=True, top_p=0.92, max_length=50)
-    elif strategy == 'temperature_sampling':
-        outputs = model.generate(input_ids, do_sample=True, temperature=0.7, max_length=50)
-    else:
-        # Default to greedy decoding
-        outputs = model.generate(input_ids, max_length=50)
+    # Generate translation using beam search
+    outputs = model.generate(input_ids, num_beams=3, max_length=50, early_stopping=True)
     
     # Decode the generated translation
     translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
@@ -43,23 +33,18 @@ def translate(sentence, strategy='beam_search'):
 
 # French sentences from easy to advanced
 sentences = [
+    "Le soleil se lève à l'est et se couche à l'ouest.",
+    "Les scientifiques travaillent dur pour trouver un remède.",
+    "La capitale de la France est Paris.",
+    "Il a plu toute la journée hier.",
+    "Je voudrais un café s'il vous plaît.",
     "Il fait beau aujourd'hui.",
     "J'aime lire des livres et regarder des films pendant mon temps libre.",
     "Si j'avais su que tu venais, j'aurais préparé quelque chose de spécial pour le dîner.",
     "Même si les avancées technologiques apportent de nombreux avantages, elles posent également des défis éthiques considérables qu'il nous faut relever."
 ]
 
-# Translate each sentence with different strategies
+# Translate each sentence and print the best translation
 for sentence in sentences:
-    translated_sentence = translate(sentence, strategy='beam_search')  # You can try 'top_k_sampling', 'top_p_sampling', 'temperature_sampling'
-    print(f"French: {sentence}\nEnglish (Beam Search): {translated_sentence}\n")
-
-    translated_sentence = translate(sentence, strategy='top_k_sampling')
-    print(f"English (Top-k Sampling): {translated_sentence}\n")
-    
-    translated_sentence = translate(sentence, strategy='top_p_sampling')
-    print(f"English (Top-p Sampling): {translated_sentence}\n")
-    
-    translated_sentence = translate(sentence, strategy='temperature_sampling')
-    print(f"English (Temperature Sampling): {translated_sentence}\n")
-
+    translated_sentence = translate(sentence)
+    print(f"French: {sentence}\nEnglish: {translated_sentence}\n")