Update README.md

README.md

@@ -4,12 +4,17 @@ language:
 - bn
 metrics:
 - bleu
+- cer
+- wer
+- meteor
 library_name: transformers
 pipeline_tag: text2text-generation
+tags:
+- text-generation-inference
 ---
-# Project Overview: Bengali Text Correction
+# Bengali Text Correction Overview:
 
-The goal of this project was to develop a software model that could fix grammatical and syntax errors in Bengali text. The approach was similar to how a language translator works, where the incorrect sentence is transformed into a correct one. We fine tune a pertained model, namely [mBart50] with a [dataset] of 1.M samples for 6500 steps and achieve a result of 0.44 bleu Score. 
+The goal of this project was to develop a software model that could fix grammatical and syntax errors in Bengali text. The approach was similar to how a language translator works, where the incorrect sentence is transformed into a correct one. We fine tune a pertained model, namely [mBart50](https://huggingface.co/facebook/mbart-large-50) with a [dataset](https://github.com/hishab-nlp/BNSECData) of 1.3 M samples for 6500 steps and achieve a score of `{BLEU: 0.443, CER:0.159, WER:0.406, Meteor: 0.655}`when tested on unseen data. Clone/download this [repo](https://github.com/himisir/Bengali-Sentence-Error-Correction), run the `correction.py` script and type the sentence after the prompt and you are all set.
 
 ## Initial Testing:
 
@@ -67,12 +72,13 @@ correct_bengali_sentence = tokenizer.decode(outputs[0], skip_special_tokens=True
 # আপনি কেমন আছেন?
 ```
 
+If you want to test this model from the terminal, run the `python correction.py` script and type the sentence after the prompt and you are all set. you'll need the `transformers` library to run this script. Install the `transformers` model using `pip install -q transformers[torch] -U`.
 
 #### Important note: You need to make sure if have used `use_safetensors =True` parameter during loading the model.
 
 # General issues faced during the entire journey:
 
-- Issue: The system is not printing any evaluation functions.
+- Issue: The system is not printing any evaluation function.
   Solution: The GPU that I am training on doesn't support FP16/BF16 precision. Commenting out `fp16 =True` in the Seq2SeqTrainingArguments solved the issue.
 
 - Issue: Training on TPU crashes on both Colab and Kaggle.
@@ -85,5 +91,6 @@ The model is clearly overfitting, and we can reduce that. My best guess is that
 I'm also planning to run a 4-bit quantization on the same model to see how it performs against the base model. It should be a fun experiment.
 
 ## Resources and References:
+
 [Dataset Source](https://github.com/hishab-nlp/BNSECData)
 [Model Documentation and Troubleshooting](https://huggingface.co/docs/transformers/model_doc/mbart)