Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-license: cc-by-4.0
+license: apache-2.0
 base_model: Helsinki-NLP/opus-mt-tc-big-en-pt
 tags:
 - generated_from_trainer
@@ -8,6 +8,9 @@ datasets:
 model-index:
 - name: opus-en-to-pt-translate
   results: []
+language:
+- pt
+pipeline_tag: translation
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -29,7 +32,7 @@ From 2004 to 2015, he co-produced and hosted the reality television series The A
 """
 
 from transformers import pipeline
-pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-pt")
+pipe = pipeline("translation", model="rhaymison/opus-en-to-pt-translator")
 print(pipe())
 
 #Trump recebeu um título de bacharel em economia pela Universidade da Pensilvânia em 1968, e o seu pai deu- lhe o nome de presidente do seu negócio imobiliário em 1971.
@@ -38,6 +41,30 @@ print(pipe())
 #licenciamento do nome Trump. De 2004 a 2015, ele produziu em conjunto e alojou a série de reality série The Apprentice.
 ```
 
+```python
+
+from transformers import MarianMTModel, MarianTokenizer
+
+texts = [
+    ">>por<< Trump received a Bachelor of Science in economics from the University of Pennsylvania in 1968, and his father named him president of his real estate business in 1971.",
+    ">>por<< Trump renamed it the Trump Organization and reoriented the company toward building and renovating skyscrapers, hotels, casinos, and golf courses."
+]
+
+model = "rhaymison/opus-en-to-pt-translator"
+tokenizer = MarianTokenizer.from_pretrained(model)
+model = MarianMTModel.from_pretrained(model)
+translated = model.generate(**tokenizer(texts, return_tensors="pt", padding=True))
+
+for t in translated:
+    print( tokenizer.decode(t, skip_special_tokens=True) )
+
+# output:
+# Trump recebeu um título de bacharel em economia pela Universidade da Pensilvânia em 1968, e o seu pai deu- lhe o nome de presidente do seu negócio imobiliário em 1971..
+# Trump mudou o nome para Organização Trump e voltou a orientar a companhia para a construção e reforma de arranha- céus, hotéis, casinos e campos de golfe.
+
+
+```
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
@@ -104,4 +131,4 @@ It achieves the following results on the evaluation set:
 - Transformers 4.38.1
 - Pytorch 2.1.0+cu121
 - Datasets 2.18.0
-- Tokenizers 0.15.2
+- Tokenizers 0.15.2