Update README.md

README.md

@@ -10,41 +10,51 @@ This is a pre-trained language translation model that aims to create a translati
 
 ## Model Details
 
-- Transformer architecture used.
-- Trained on a 210000 corpus pairs.
-- Pre-trained Helsinki-NLP/opus-mt-en-swc.
-- 2 models to enforce biderectional translation.
+- Transformer architecture used
+- Trained on a 210000 corpus pairs
+- Pre-trained Helsinki-NLP/opus-mt-en-swc
+- 2 models to enforce biderectional translation
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
 
 
 
-- **Developed by:** [Peter Rogendo]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** Peter Rogendo
+- **Model type:** Transformer
+- **Language(s) (NLP):** Transformer, Pandas, Numpy
+- **License:** Distributed under the MIT License
+- **Finetuned from model [Helsinki-NLP/opus-mt-en-swc]:** [This pre-trained model was re-trained on a swahili-english sentence pairs that were collected across Kenya. Swahili is the national language and is among the top three of the most spoken language in Africa. The sentences that were used to train this model were 210000 in total.]
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [https://github.com/Rogendo/Eng-Swa-Translator]
+- **Paper [optional]:** 
+- **Demo [optional]:** 
 
 ## Uses
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+This translation model is intended to be used in many cases, from language translators, screen assistants, to even in official cases such as translating legal documents.
 
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+# Use a pipeline as a high-level helper
+
+        from transformers import pipeline
+        
+        pipe = pipeline("text2text-generation", model="Rogendo/sw-en")
+
+# Load model directly
+
+        from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+        
+        tokenizer = AutoTokenizer.from_pretrained("Rogendo/sw-en")
+        model = AutoModelForSeq2SeqLM.from_pretrained("Rogendo/sw-en")
 
 ### Downstream Use [optional]