diff --git a/p2/quest/biberplus-categorization.log b/p2/quest/biberplus-categorization.log
new file mode 100644
index 0000000..6f453be
--- /dev/null
+++ b/p2/quest/biberplus-categorization.log
@@ -0,0 +1,3 @@
+starting the job at: Tue Jul 22 16:43:27 CDT 2025
+setting up the environment
+running the biberplus labeling script
diff --git a/p2/quest/python_scripts/biberplus_labeling.py b/p2/quest/python_scripts/biberplus_labeling.py
new file mode 100644
index 0000000..1094110
--- /dev/null
+++ b/p2/quest/python_scripts/biberplus_labeling.py
@@ -0,0 +1,95 @@
+'''
+import torch
+import numpy as np
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import random 
+import pandas as pd 
+
+MODEL_NAME = "Blablablab/neurobiber"
+CHUNK_SIZE = 512  # Neurobiber was trained with max_length=512
+
+# List of the 96 features that Neurobiber can predict
+BIBER_FEATURES = [
+    "BIN_QUAN","BIN_QUPR","BIN_AMP","BIN_PASS","BIN_XX0","BIN_JJ",
+    "BIN_BEMA","BIN_CAUS","BIN_CONC","BIN_COND","BIN_CONJ","BIN_CONT",
+    "BIN_DPAR","BIN_DWNT","BIN_EX","BIN_FPP1","BIN_GER","BIN_RB",
+    "BIN_PIN","BIN_INPR","BIN_TO","BIN_NEMD","BIN_OSUB","BIN_PASTP",
+    "BIN_VBD","BIN_PHC","BIN_PIRE","BIN_PLACE","BIN_POMD","BIN_PRMD",
+    "BIN_WZPRES","BIN_VPRT","BIN_PRIV","BIN_PIT","BIN_PUBV","BIN_SPP2",
+    "BIN_SMP","BIN_SERE","BIN_STPR","BIN_SUAV","BIN_SYNE","BIN_TPP3",
+    "BIN_TIME","BIN_NOMZ","BIN_BYPA","BIN_PRED","BIN_TOBJ","BIN_TSUB",
+    "BIN_THVC","BIN_NN","BIN_DEMP","BIN_DEMO","BIN_WHQU","BIN_EMPH",
+    "BIN_HDG","BIN_WZPAST","BIN_THAC","BIN_PEAS","BIN_ANDC","BIN_PRESP",
+    "BIN_PROD","BIN_SPAU","BIN_SPIN","BIN_THATD","BIN_WHOBJ","BIN_WHSUB",
+    "BIN_WHCL","BIN_ART","BIN_AUXB","BIN_CAP","BIN_SCONJ","BIN_CCONJ",
+    "BIN_DET","BIN_EMOJ","BIN_EMOT","BIN_EXCL","BIN_HASH","BIN_INF",
+    "BIN_UH","BIN_NUM","BIN_LAUGH","BIN_PRP","BIN_PREP","BIN_NNP",
+    "BIN_QUES","BIN_QUOT","BIN_AT","BIN_SBJP","BIN_URL","BIN_WH",
+    "BIN_INDA","BIN_ACCU","BIN_PGAS","BIN_CMADJ","BIN_SPADJ","BIN_X"
+]
+'''
+import pandas as pd
+import numpy as np
+from biberplus.tagger import load_config, load_pipeline, calculate_tag_frequencies 
+import cupy
+import random 
+
+def biberplus_labeler(text):
+    print(len(text))
+    config = load_config()
+    config.update({'use_gpu': False, 'biber': True, 'function_words': False, 'token_normalization': 100})
+    pipeline = load_pipeline(config)
+    #test =  ['London-based DJ Imogen takes on the NTS airwaves, bouncing between fuzzy electro and punishing techno.', ' Built upon the spaCy library, it delivers fast part-of-speech tagging along with supplemental features such as a function word tagger, PCA, and factor analysis']
+    features_list = []
+    for message in text:
+        message_label = calculate_tag_frequencies(message, pipeline, config)
+        mean_row = message_label.set_index('tag')['mean']
+        mean_row = mean_row.rename(lambda tag: f"normalized_{tag}")
+        features_list.append(mean_row)
+    print(len(features_list))
+    frequencies_df = pd.DataFrame(features_list)
+    frequencies_df['comment_text'] = text
+    frequencies_df = frequencies_df.reset_index(drop=True)
+    return frequencies_df
+ 
+if __name__ == "__main__":
+    #https://huggingface.co/Blablablab/neurobiber
+    '''
+    docs = [
+    "First text goes here.",
+    "Second text, slightly different style."
+    ]
+    '''
+    #loading in the discussion data from the universal CSV
+    first_discussion_df = pd.read_csv("/home/nws8519/git/mw-lifecycle-analysis/p2/071425_master_discussion_data.csv")
+    #formatting for the neurobiber model
+    docs = first_discussion_df["comment_text"].astype(str).tolist()
+    #load model and run
+    #model, tokenizer = load_model_and_tokenizer()
+    preds_df = biberplus_labeler(docs)
+    #new columns in the df for the predicted neurobiber items 
+    #preds_cols = [f"neurobiber_{i+1}" for i in range(96)]
+    #preds_df = pd.DataFrame(preds, columns=preds_cols, index=first_discussion_df.index)
+    final_discussion_df = pd.concat([first_discussion_df, preds_df], axis=1)
+    #print(type(preds))
+    #assigning the preditions as a new column 
+    '''
+    final_discussion_df = pd.merge(
+        first_discussion_df,
+        preds_df,
+        on='comment_text',    # replace with your actual key
+        how='inner'
+    )
+    '''
+    print(len(final_discussion_df))
+    #final_discussion_df["biberplus_preds"] = list(preds)
+    #assert that order has been preserved 
+    for _ in range(10):
+        random_index = random.choice(first_discussion_df.index)
+        assert first_discussion_df.loc[random_index, "comment_text"] == final_discussion_df.loc[random_index, "comment_text"]
+    #assert that there are the same number of rows in first_discussion_df and second_discussion_df
+    assert len(first_discussion_df) == len(final_discussion_df)
+    # if passing the prior asserts, let's write to a csv
+    final_discussion_df.to_csv("/home/nws8519/git/mw-lifecycle-analysis/p2/quest/072225_biberplus_labels.csv", index=False)
+    print('biberplus labeling pau')
+
diff --git a/p2/quest/slurm_jobs/biberplus_label.sh b/p2/quest/slurm_jobs/biberplus_label.sh
new file mode 100644
index 0000000..df79d4f
--- /dev/null
+++ b/p2/quest/slurm_jobs/biberplus_label.sh
@@ -0,0 +1,32 @@
+#!/bin/bash
+#SBATCH -A p32852
+#SBATCH -p gengpu
+#SBATCH --gres=gpu:a100:1
+#SBATCH --nodes=2
+#SBATCH --ntasks-per-node=1
+#SBATCH --time=24:00:00
+#SBATCH --mem=64G
+#SBATCH --cpus-per-task=4
+#SBATCH --job-name=biberplus-categorization 
+#SBATCH --output=biberplus-categorization.log
+#SBATCH --mail-type=BEGIN,END,FAIL
+#SBATCH --mail-user=gaughan@u.northwestern.edu
+
+echo "starting the job at: $(date)"
+
+echo "setting up the environment"
+
+module purge
+eval "$(conda shell.bash hook)"
+conda activate neurobiber
+
+echo "running the biberplus labeling script"
+
+python /home/nws8519/git/mw-lifecycle-analysis/p2/quest/python_scripts/biberplus_labeling.py
+
+echo "job finished, cleaning up"
+
+conda deactivate
+
+echo "job pau at: $(date)"
+