Update README.md

README.md

@@ -10,12 +10,16 @@ datasets:
 
 # Introduction
 
-[HuggingFace](https://huggingface.co/) is one of the most useful libraries for a NLP researcher / developer as it provides numerous pre-trained models, datasets, and tons of utility functions for NLP. In this repository, I'm trying to setup a complete pipeline for a Machine Learning project and the task I've chosen for the setup is Question Generation for Paragraphs. This is a seq2seq task for which I intend to fine-tune a pre-trained encoder-decoder Transformer model for Extractive Summarization like BART / Pegasus. More specifically, I'm finetuning the `sshleifer/distilbart-cnn-6-6` model on the SQuAD dataset.
+This model checkpoint is obtained by fine-tuning the `sshleifer/distilbart-cnn-6-6` summarization checkpoint on the SQuAD dataset.
 
 # Usage
 
 The input format is as follows: `[answer] <s> [passage]`. The model will predict the question that corresponds to the answer from the passage.
 
+# Plot
+
+![Training Run](plots/train_run_6.png)
+
 # Dataset
 
 The goal of Question Generation is to generate a valid and fluent question according to a given passage and the target answer. Hence, the input to the model will be a passage context and an answer, and the output / target will be the question for the given answer. Question Generation can be used in many scenarios, such as automatic tutoring systems, improving the performance of Question Answering models and enabling chat-bots to lead a conversation. The final dataset is created by taking the union of the following Question Answering Datasets. The dataset must have the following three columns: context, question, answer.
@@ -26,7 +30,7 @@ Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset,
 
 ### Preprocessing
 
-The first step is to remove questions which don't have answers. After that, we split the train set into Train and Eval sets and treat the dev set as the test set.
+The first step is to remove questions that don't have answers. After that, we split the train set into Train and Eval sets and treat the dev set as the test set.
 
 ### Stats