diff --git a/src/experiments/NEC_evaluation/context_sensitivity.py b/src/experiments/NEC_evaluation/context_sensitivity.py
index b72bd445bd3819d628cecb35007594288017de0e..f507f92713d8fc2f00b6b9316ad72121dad6378d 100644
--- a/src/experiments/NEC_evaluation/context_sensitivity.py
+++ b/src/experiments/NEC_evaluation/context_sensitivity.py
@@ -1,3 +1,7 @@
+"""
+This module evaluates the importance of specific context word for the NLI based named entity classification task. Individual words are selectively replaced with a placeholder and subsequent mispredictions are recorded.
+"""
+
 import data.data_manager as data_manager
 from src.common_interface import classify_entity
 
@@ -29,4 +33,4 @@ def run_context_analysis(model_name, dataset, num_sentences):
             print(f"Predicted: {predicted}, True: {entity[1]}")
 
         
-run_context_analysis("Llama-3.1-8B", "FIGER-coarse", 50)
+run_context_analysis("T5-NLI", "FIGER-coarse", 50)
diff --git a/src/experiments/NEC_evaluation/evaluation.py b/src/experiments/NEC_evaluation/evaluation.py
index 8bb9b8502b24198bf2b3f44734f612d9c14b8baf..cc6f6fd24cfd65cdfbcf4729548d3ba8c7ae002b 100644
--- a/src/experiments/NEC_evaluation/evaluation.py
+++ b/src/experiments/NEC_evaluation/evaluation.py
@@ -1,6 +1,7 @@
 """
-This file evaluates all NEC approaches.
+This file evaluates all NEC approaches on all datasets.
 """
+
 import os
 import csv
 import datetime
@@ -108,5 +109,5 @@ def read_NEC_metrics(directory):
         print(f"Model: {model}, Dataset: {dataset}, Accuracy: {avg_accuracy:.2f}%")
 
 
-# run_NEC_tests_all()
+run_NEC_tests_all()
 read_NEC_metrics("results")
diff --git a/src/experiments/finetune_T5/finetune_T5_MLM_entity.py b/src/experiments/finetune_T5/finetune_T5_MLM_entity.py
index de0481b40022b2eee0bde43e196dddadc30e9362..c65999f198697a72eafe3fa8b009e6a3b77dff28 100644
--- a/src/experiments/finetune_T5/finetune_T5_MLM_entity.py
+++ b/src/experiments/finetune_T5/finetune_T5_MLM_entity.py
@@ -1,3 +1,8 @@
+"""
+This module implements the finetuning procedure for the MLM entity task formulation for the named entity classification task.
+By default, the model is finetuned on the FIGER-coarse
+"""
+
 import data.data_manager as data_manager
 from src.models.T5_MLM_entity import finetune_model, set_label_dict
 
diff --git a/src/experiments/finetune_T5/finetune_T5_MLM_label.py b/src/experiments/finetune_T5/finetune_T5_MLM_label.py
index c7707b4d912caf62cab2fa373c84f7a15d3836e3..aed2575eacc6b66c204c21f7f576bc0bba0b3e91 100644
--- a/src/experiments/finetune_T5/finetune_T5_MLM_label.py
+++ b/src/experiments/finetune_T5/finetune_T5_MLM_label.py
@@ -1,3 +1,8 @@
+"""
+This module implements the finetuning procedure for the MLM label task formulation for the named entity classification task.
+By default, the model is finetuned on the FIGER-coarse dataset with 1000 example sentences.
+"""
+
 import data.data_manager as data_manager
 from src.models.T5_MLM_label import finetune_model
 
diff --git a/src/experiments/finetune_T5/finetune_T5_NLI.py b/src/experiments/finetune_T5/finetune_T5_NLI.py
index 6db6dfd2d56b01063f765e59454dda4d6c058971..85dd279cd68c9184849b3b229ae0bccfa44911b2 100644
--- a/src/experiments/finetune_T5/finetune_T5_NLI.py
+++ b/src/experiments/finetune_T5/finetune_T5_NLI.py
@@ -1,3 +1,8 @@
+"""
+This module implements the finetuning procedure for the NLI task formulation for the named entity classification task.
+By default, the model is finetuned on the FIGER-coarse dataset with 1000 example sentences.
+"""
+
 import data.data_manager as data_manager
 from src.models.T5_NLI import finetune_model
 
diff --git a/src/experiments/finetune_T5/plotting/plot_loss.py b/src/experiments/finetune_T5/plotting/plot_loss.py
index 75ab038a5b48b62846e1f69b27d8f6944716453b..18ed38e839bee1f392d45d011174bbd3188918c7 100644
--- a/src/experiments/finetune_T5/plotting/plot_loss.py
+++ b/src/experiments/finetune_T5/plotting/plot_loss.py
@@ -1,3 +1,8 @@
+"""
+This module is used to plot the loss curve of the T5 finetuning tasks. The loss values are extracted from the Slurm output file.
+To use this module, the correct filenames must be substituted in the call to `plot_loss_curve`
+"""
+
 import os
 import re
 import pandas as pd
diff --git a/src/models/T5_MLM_entity.py b/src/models/T5_MLM_entity.py
index 05692edbaa5f484ecc3134fc74a2d4161a93144c..c39c6b046e038f6f427f901f2ec166788ea5c97f 100644
--- a/src/models/T5_MLM_entity.py
+++ b/src/models/T5_MLM_entity.py
@@ -1,3 +1,7 @@
+"""
+This module implements the entity masking approach for the named entity recogition task. It uses the T5 model and allows for finetuning of the model.
+"""
+
 import random
 import numpy as np
 from transformers import T5ForConditionalGeneration, T5Tokenizer, Trainer, TrainingArguments, DataCollatorForSeq2Seq
diff --git a/src/models/T5_MLM_label.py b/src/models/T5_MLM_label.py
index 14004ca763dce4f35c31c33efb731e394d69698b..8f1d54dd4277fe207e52fa0d8f08b2d7c88c56ff 100644
--- a/src/models/T5_MLM_label.py
+++ b/src/models/T5_MLM_label.py
@@ -1,3 +1,7 @@
+"""
+This module implements the label masking approach for the named entity recogition task. It uses the T5 model and allows for finetuning of the model.
+"""
+
 import numpy as np
 from transformers import T5ForConditionalGeneration, T5Tokenizer, Trainer, TrainingArguments, DataCollatorForSeq2Seq
 from datasets import Dataset, DatasetDict
diff --git a/src/models/T5_NLI.py b/src/models/T5_NLI.py
index 4d57ada6ff55d523ca1168769b9589508f0ec600..78c7458911c496801b0066eb3e31de008b33a7e8 100644
--- a/src/models/T5_NLI.py
+++ b/src/models/T5_NLI.py
@@ -1,3 +1,7 @@
+"""
+This module implements the natural language inference approach for the named entity recogition task. It uses the T5 model and allows for finetuning of the model.
+"""
+
 import torch
 from torch.nn.functional import softmax
 from transformers import T5ForConditionalGeneration, T5Tokenizer, Trainer, TrainingArguments, DataCollatorForSeq2Seq
diff --git a/src/models/Word2Vec.py b/src/models/Word2Vec.py
index 741dbf7ecff09ee54f80e0425f2399436edc35da..ef824cf9ea7e84c529032ffae01eb83ca515c8b1 100644
--- a/src/models/Word2Vec.py
+++ b/src/models/Word2Vec.py
@@ -1,3 +1,7 @@
+"""
+This module implements the Word2Vec based approach for the named entity recogition task. It relies on the label dictionary functionality of the data manager for the required representative entities.
+"""
+
 from gensim.models import Word2Vec
 import gensim.downloader as api
 import string
@@ -81,4 +85,4 @@ def classify_entity(entity, labels):
 
     return best_label if best_label else labels[0]
 
-# load_pretrained()
+load_pretrained()