Update README.md

README.md

@@ -1,37 +1,33 @@
-[tokenizer](#tokenizer) | [model](#model) | [datasets](#datasets) | [plots](#plots) | [fine tuning](#fine-tuning)
-
-# Tokenizer {#tokenizer}
+# Tokenizer
 
 We trained our tokenizer using [sentencepiece](https://github.com/google/sentencepiece)'s unigram tokenizer. Then loaded the tokenizer as MT5TokenizerFast.
 
-## Model {#model}
+## Model
 
 We used [MT5-base](https://huggingface.co/google/mt5-base) model.
 
-## Datasets {#datasets}
+## Datasets
 
 We used [Code Search Net](https://huggingface.co/datasets/code_search_net)'s dataset and some scrapped data from internet to train the model. We maintained a list of datasets where each dataset had codes of same language.
 
-## Plots {#plots}
-
-[train loss](#train_loss) | [evaluation loss](#eval_loss) | [evaluation accuracy](#eval_acc) | [learning rate](#lrs)
+## Plots
 
-### Train loss {#train_loss}
+### Train loss
 
-![train loss](train_loss.png)
+![train loss](https://i.ibb.co/x53Wm8n/train-loss.png)
 
-### Evaluation loss {#eval_loss}
+### Evaluation loss
 
-![eval loss](eval_loss.png)
+![eval loss](https://i.ibb.co/McB2jnf/eval-loss.png)
 
-### Evaluation accuracy {#eval_acc}
+### Evaluation accuracy
 
-![eval accuracy](eval_accuracy.png)
+![eval accuracy](https://i.ibb.co/YDGhLdn/eval-accuracy.png)
 
-### Learning rate {#lrs}
+### Learning rate
 
-![learning rate](learning_rate.png)
+![learning rate](https://i.ibb.co/CMStzWv/learning-rate.png)
 
-## Fine tuning {#fine-tuning}
+## Fine tuning
 
 We fine tuned the model with [CodeXGLUE code-to-code-trans dataset](https://huggingface.co/datasets/code_x_glue_cc_code_to_code_trans), and scrapper data.