Update README.md

README.md

@@ -7,4 +7,56 @@ language:
 - en
 base_model:
 - google-t5/t5-base
----
+---
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+# Load the pre-trained model and tokenizer
+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'):
+    # Prepare the input for the model
+    input_text = f"translate French to English: {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=5, 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)
+    
+    # Decode the generated translation
+    translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return translation
+
+# French sentences from easy to advanced
+sentences = [
+    "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
+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")
+