--- a
+++ b/src/experiments/bilstm.py
@@ -0,0 +1,150 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+Experiments on the BiLSTM model and the different datasets (i.e. n2c2, DDI)
+"""
+
+# Base Dependencies
+# -----------------
+from copy import deepcopy
+from pathlib import Path
+from os.path import join as pjoin
+
+# Package Dependencies
+# --------------------
+from .common import final_repetition
+
+# Local Dependencies
+# ------------------
+from training.config import PLExperimentConfig, BaalExperimentConfig
+from training.bilstm import BilstmTrainer
+from utils import set_seed
+
+# 3rd-Party Dependencies
+# ----------------------
+from datasets import load_from_disk
+
+# Constants
+# ----------
+from constants import (
+    DDI_HF_TEST_PATH,
+    DDI_HF_TRAIN_PATH,
+    N2C2_HF_TRAIN_PATH,
+    N2C2_HF_TEST_PATH,
+    N2C2_REL_TYPES,
+    BaalQueryStrategy,
+    EXP_RANDOM_SEEDS
+)
+
+MODEL_NAME = "bilstm"
+
+
+def bilstm_passive_learning_n2c2(init_repetition: int = 0, n_repetitions: int = 5, logging: bool = True):
+
+    config = PLExperimentConfig(
+        max_epoch=25,
+        batch_size=32
+    )
+  
+    for rel_type in N2C2_REL_TYPES:
+        # load datasets
+        train_dataset = load_from_disk(
+            str(Path(pjoin(N2C2_HF_TRAIN_PATH, MODEL_NAME, rel_type)))
+        )
+        test_dataset = load_from_disk(
+            str(Path(pjoin(N2C2_HF_TEST_PATH, MODEL_NAME, rel_type)))
+        )
+
+        # create trainer
+        trainer = BilstmTrainer("n2c2", train_dataset, test_dataset, rel_type)
+            
+        for repetition in range(init_repetition, final_repetition(init_repetition, n_repetitions)):
+            # set random seed
+            random_seed: int = EXP_RANDOM_SEEDS[repetition]
+            set_seed(random_seed)
+            config.seed = random_seed
+            # train passive learning
+            trainer.train_passive_learning(config=config, logging=logging)
+
+
+def bilstm_passive_learning_ddi(init_repetition:int = 0, n_repetitions: int = 5, logging: bool = True):
+    config = PLExperimentConfig(
+        max_epoch=25,
+        batch_size=32
+    )
+
+    # load datasets
+    train_dataset = load_from_disk(str(Path(pjoin(DDI_HF_TRAIN_PATH, MODEL_NAME))))
+    test_dataset = load_from_disk(str(Path(pjoin(DDI_HF_TEST_PATH, MODEL_NAME))))
+
+    # create trainer
+    trainer = BilstmTrainer("ddi", train_dataset, test_dataset)
+
+    # train passive learing
+    for repetition in range(init_repetition, final_repetition(init_repetition, n_repetitions)):
+        # set random seed
+        random_seed: int = EXP_RANDOM_SEEDS[repetition]
+        set_seed(random_seed)
+        config.seed = random_seed
+        
+        trainer.train_passive_learning(config=config, logging=logging)
+
+
+def bilstm_active_learning_n2c2(init_repetition:int = 0, n_repetitions: int = 5, logging: bool = True):
+
+    config = BaalExperimentConfig(
+        max_epoch=15,
+        batch_size=32,
+        all_bayesian=False
+    )
+    
+    
+    for repetition in range(init_repetition, final_repetition(init_repetition, n_repetitions)):
+        # set random seed
+        random_seed: int = EXP_RANDOM_SEEDS[repetition]
+        set_seed(random_seed)
+        config.seed = random_seed
+
+        for rel_type in N2C2_REL_TYPES:
+
+            # load datasets
+            train_dataset = load_from_disk(
+                str(Path(pjoin(N2C2_HF_TRAIN_PATH, MODEL_NAME, rel_type)))
+            )
+            test_dataset = load_from_disk(
+                str(Path(pjoin(N2C2_HF_TEST_PATH, MODEL_NAME, rel_type)))
+            )
+
+            # create trainer
+            trainer = BilstmTrainer(
+                dataset="n2c2",
+                train_dataset=train_dataset,
+                test_dataset=test_dataset,
+                relation_type=rel_type,
+            )
+
+            for query_strategy in BaalQueryStrategy:
+                exp_config = deepcopy(config)
+                trainer.train_active_learning(query_strategy, config=exp_config, logging=logging)
+
+
+def bilstm_active_learning_ddi(init_repetition:int = 0, n_repetitions: int = 5, logging: bool = True):
+    config = BaalExperimentConfig(max_epoch=15,  batch_size=32, all_bayesian=False)
+
+    # load datasets
+    train_dataset = load_from_disk(str(Path(pjoin(DDI_HF_TRAIN_PATH, MODEL_NAME))))
+    test_dataset = load_from_disk(str(Path(pjoin(DDI_HF_TEST_PATH, MODEL_NAME))))
+
+    # create trainer
+    trainer = BilstmTrainer("ddi", train_dataset, test_dataset)
+
+    for repetition in range(init_repetition, final_repetition(init_repetition, n_repetitions)):
+        # set random seed
+        random_seed: int = EXP_RANDOM_SEEDS[repetition]
+        set_seed(random_seed)
+        config.seed = random_seed
+
+        for query_strategy in BaalQueryStrategy:
+            exp_config = deepcopy(config)
+            trainer.train_active_learning(query_strategy, config=exp_config, logging=logging)