updating readme

README.md

@@ -1,10 +1,12 @@
 Hugging Face's logo
 ---
-language: yo
+language: 
+- yo
+- en
 datasets:
 - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
 ---
-# mT5_base_yoruba_adr
+# mT5_base_yor_eng_mt
 ## Model description
 **mT5_base_yor_eng_mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned  mT5-base  model.  It establishes a **strong baseline** for automatically translating texts from Yorùbá to English.  
 
@@ -13,14 +15,15 @@ Specifically, this model is a *mT5_base* model that was fine-tuned on  JW300 Yor
 #### How to use
 You can use this model with Transformers *pipeline* for ADR.
 ```python
-from transformers import AutoTokenizer, AutoModelForTokenClassification
-from transformers import pipeline
-tokenizer = AutoTokenizer.from_pretrained("")
-model = AutoModelForTokenClassification.from_pretrained("")
-nlp = pipeline("", model=model, tokenizer=tokenizer)
-example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria"
-ner_results = nlp(example)
-print(ner_results)
+from transformers import MT5ForConditionalGeneration, T5Tokenizer
+
+model = MT5ForConditionalGeneration.from_pretrained("Davlan/mt5_base_yor_eng_mt")
+tokenizer = T5Tokenizer.from_pretrained("google/mt5-base")
+input_string = "Akọni ajìjàgbara obìnrin tó sun àtìmalé torí owó orí"
+inputs = tokenizer.encode(input_string, return_tensors="pt")
+generated_tokens = model.generate(inputs)
+results = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+print(results)
 ```
 #### Limitations and bias
 This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.