Update README.md

README.md

@@ -1,85 +1,58 @@
 ---
 license: mit
+library_name: transformers
 ---
+
 # Swahili-English Translation Model
 
 ## Model Details
 
 - **Pre-trained Model**: Rogendo/sw-en
-- ## Model Details
+- **Architecture**: Transformer
+- **Training Data**: Trained on 210,000 Swahili-English corpus pairs
+- **Base Model**: Helsinki-NLP/opus-mt-en-swc
+- **Training Method**: Fine-tuned with an emphasis on bidirectional translation between Swahili and English.
 
-- 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. -->
-
-
+This Swahili-English translation model was developed to handle translations between Swahili, one of Africa's most spoken languages, and English. It was trained on a diverse dataset sourced from OPUS, leveraging the Transformer architecture for effective translation.
 
 - **Developed by:** Peter Rogendo, Frederick Kioko
-- **Model type:** Transformer
-- **Language(s) (NLP):** Transformer, Pandas, Numpy
+- **Model Type:** Transformer
+- **Languages:** Swahili, English
 - **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.]
 
-  - **Corpus Name**: WikiMatrix
+### Training Data
+
+The model was fine-tuned on the following datasets:
+  
+  - **WikiMatrix:**
     - **Package**: WikiMatrix.en-sw in Moses format
-    - **Website**: [WikiMatrix](http://opus.nlpl.eu/WikiMatrix-v1.php)
-    - **Release**: v1
-    - **Release Date**: Wed Nov 4 15:07:29 EET 2020
     - **License**: [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode)
-    - **Citation**: Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman, WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia, arXiv, July 11 2019.
+    - **Citation**: Holger Schwenk et al., WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia, arXiv, July 2019.
 
-  - **Corpus Name**: ParaCrawl
+  - **ParaCrawl:**
     - **Package**: ParaCrawl.en-sw in Moses format
-    - **Website**: [ParaCrawl](http://opus.nlpl.eu/ParaCrawl-v9.php)
-    - **Release**: v9
-    - **Release Date**: Fri Mar 25 12:20:25 EET 2022
     - **License**: [CC0](http://paracrawl.eu/download.html)
-    - **Acknowledgement**: Please acknowledge the ParaCrawl project at [ParaCrawl](http://paracrawl.eu) and OPUS for the service.
+    - **Acknowledgement**: Please acknowledge the ParaCrawl project at [ParaCrawl](http://paracrawl.eu).
 
-  - **Corpus Name**: TICO-19
+  - **TICO-19:**
     - **Package**: tico-19.en-sw in Moses format
-    - **Website**: [TICO-19](http://opus.nlpl.eu/tico-19-v2020-10-28.php)
-    - **Release**: v2020-10-28
-    - **Release Date**: Wed Oct 28 08:44:31 EET 2020
     - **License**: [CC0](https://tico-19.github.io/LICENSE.md)
-    - **Citation**: J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012).
-
-## Model Description
-
-- **Developed By**: Bildad Otieno
-- **Model Type**: Transformer
-- **Language(s)**: Swahili and English
-- **License**: Distributed under the MIT License
-- **Training Data**: The model was fine-tuned using a collection of datasets from OPUS, including WikiMatrix, ParaCrawl, and TICO-19. The datasets provide a diverse range of translation examples between Swahili and English.
-
-# Use a pipeline as a high-level helper
-
-        from transformers import pipeline
-        
-        # Initialize the translation pipeline
-        translator = pipeline("translation", model="Bildad/Swahili-English_Translation")
-        
-        # Translate text
-        translation = translator("Habari yako?")[0]
-        translated_text = translation["translation_text"]
-        
-        print(translated_text)
+    - **Citation**: J. Tiedemann, 2012, Parallel Data, Tools, and Interfaces in OPUS.
 
-# Load model directly
+## Usage
 
-        from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-        
-        tokenizer = AutoTokenizer.from_pretrained("Bildad/Swahili-English_Translation")
-        model = AutoModelForSeq2SeqLM.from_pretrained("Bildad/Swahili-English_Translation")
+### Using a Pipeline as a High-Level Helper
 
-## Model Card Authors 
+```python
+from transformers import pipeline
 
-Bildad Otieno
+# Initialize the translation pipeline
+translator = pipeline("translation", model="Bildad/Swahili-English_Translation")
 
-## Model Card Contact
+# Translate text
+translation = translator("Habari yako?")[0]
+translated_text = translation["translation_text"]
 
-[email protected]
+print(translated_text)