diff --git a/expemb/LICENSE b/LICENSE
similarity index 100%
rename from expemb/LICENSE
rename to LICENSE
diff --git a/README.md b/README.md
index 2fce8419d9b20996fd665f3bd953c187f1501d0b..acddba0c67e40a96ffc951bd05a1b953815926fd 100644
--- a/README.md
+++ b/README.md
@@ -7,6 +7,10 @@ Setup the environment using `conda` as follows:
 conda env create -n expembtx -f environment.yml
 ```
 
+## Datasets
+The datasets are available [here](https://osf.io/9tdqg/?view_only=78c364b3c71f43b5b414deac81cf863b).
+
+
 ## Training and Evaluation
 ### Setup
 To run the training and evaluation pipeline in this repository, [eqnet](https://github.com/mast-group/eqnet/) is required. As it can not be installed as a dependency, clone this repository and add it to `PYTHONPATH`.
@@ -24,32 +28,32 @@ Example:
 python train_expembtx.py \
     --train_file <TRAIN_FILE> \
     --val_file <VAL_FILE> \
-    --n_epochs 100 \
+    --n_epochs <N_EPOCHS> \
     --norm_first True \
     --optim Adam \
     --weight_decay 0 \
     --lr 0.0001 \
-    --train_batch_size 128 \
+    --train_batch_size <TRAIN_BATCH_SIZE> \
     --run_name <RUN_NAME> \
-    --val_batch_size 256 \
+    --val_batch_size <EVAL_BATCH_SIZE> \
     --grad_clip_val 1 \
     --max_out_len 256 \
     --precision 16 \
     --save_dir <OUT_DIR> \
-    --early_stopping 5 \
-    --n_min_epochs 10 \
+    --early_stopping <EARLY_STOPPING> \
+    --n_min_epochs <N_MIN_EPOCHS> \
     --label_smoothing 0.1 \
     --seed 42
 ```
 
-Add `--semvec` option to the above-mentioned command for the SemVec datasets.
+Add `--semvec` option to the above-mentioned command for the SemVec datasets. For the SemVec datasets, `<TRAIN_FILE>` is not the original training file provided with the SemVec datasets but a version in the input-output format.
 
-For all supported options, use `python train_expembtx.py --help` or refer to [TrainingAgruments](expemb/args.py#TestingArguments).
+For all supported options, use `python train_expembtx.py --help` or refer to [TrainingAgruments](expemb/args.py#TrainingAgruments).
 
 ### Evaluation
-To evaluate a trained model, `test_expembtx.py` may be used.
+To evaluate a trained model, `test_expembtx.py` may be used. The options may vary depending if the model is trained on the Equivalent Expressions Dataset or the SemVec datasets.
 
-Example:
+For the Equivalent Expressions Dataset, the following command may be used to test the model accuracy. On completion, it will generate a file containing the results inside `<SAVED_MODEL_DIR>` with `<RESULT_FILE_PREFIX>` as the file name prefix.
 ```
 python test_expembtx.py \
     --test_file <TEST_FILE> \
@@ -60,6 +64,16 @@ python test_expembtx.py \
     --batch_size 32
 ```
 
+For the SemVec datasets, the following command may be used.
+```
+python test_expembtx.py \
+    --test_file <TEST_FILE> \
+    --full_file <SEMVEC_FULL_DATASET> \
+    --ckpt_name best_max \
+    --save_dir <SAVED_MODEL_DIR> \
+    --semvec
+```
+
 For all supported options, use `python test_expembtx.py --help` or refer to [TestingArguments](expemb/args.py#TestingArguments).
 
 ## Embedding Mathematics
@@ -91,5 +105,5 @@ For all supported options, use `python run_embmath.py --help` or refer to [Dista
 ## Embedding Plots
 For embedding plots, refer to [embedding_plots.ipynb](notebooks/embedding_plots.ipynb).
 
-## Wandb Integration
-This repository supports wandb integration. To start using it, login to wandb using `wandb login`. To disable wandb, set the environment variable `WANDB_MODE=offline`.
\ No newline at end of file
+## Weights & Biases (wandb) Integration
+This repository supports wandb integration. To start using it, login to wandb using `wandb login`. To disable wandb, set the environment variable `WANDB_MODE=offline`.
diff --git a/data.dvc b/data.dvc
index 9e6eb57d08ec837186f04357cce51a6b58f697f7..6525e677da476a2ffa7c7f9e933fbdb4b8263b3c 100644
--- a/data.dvc
+++ b/data.dvc
@@ -1,5 +1,5 @@
 outs:
-- md5: dd9adab06b0b971ca76b127229ca272e.dir
-  size: 1056242338
-  nfiles: 125
+- md5: 8f77cd8265892df56a3ffd2a7a785b2b.dir
+  size: 1056244911
+  nfiles: 127
   path: data