updating with some structure for discussion analysis stuff

p2/quest/python_scripts/neurobiber_labeling.py

@@ -0,0 +1,97 @@
+import torch
+import numpy as np
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+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"
+]
+
+def load_model_and_tokenizer():
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=True)
+    model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME).to("cuda")
+    model.eval()
+    return model, tokenizer
+
+def chunk_text(text, chunk_size=CHUNK_SIZE):
+    tokens = text.strip().split()
+    if not tokens:
+        return []
+    return [" ".join(tokens[i:i + chunk_size]) for i in range(0, len(tokens), chunk_size)]
+
+def get_predictions_chunked_batch(model, tokenizer, texts, chunk_size=CHUNK_SIZE, subbatch_size=32):
+    chunked_texts = []
+    chunk_indices = []
+    for idx, text in enumerate(texts):
+        start = len(chunked_texts)
+        text_chunks = chunk_text(text, chunk_size)
+        chunked_texts.extend(text_chunks)
+        chunk_indices.append({
+            'original_idx': idx,
+            'chunk_range': (start, start + len(text_chunks))
+        })
+
+    # If there are no chunks (empty inputs), return zeros
+    if not chunked_texts:
+        return np.zeros((len(texts), model.config.num_labels))
+
+    all_chunk_preds = []
+    for i in range(0, len(chunked_texts), subbatch_size):
+        batch_chunks = chunked_texts[i : i + subbatch_size]
+        encodings = tokenizer(
+            batch_chunks,
+            return_tensors='pt',
+            padding=True,
+            truncation=True,
+            max_length=chunk_size
+        ).to("cuda")
+
+        with torch.no_grad(), torch.amp.autocast("cuda"):
+            outputs = model(**encodings)
+            probs = torch.sigmoid(outputs.logits)
+        all_chunk_preds.append(probs.cpu())
+
+    all_chunk_preds = torch.cat(all_chunk_preds, dim=0) if all_chunk_preds else torch.empty(0)
+    predictions = [None] * len(texts)
+
+    for info in chunk_indices:
+        start, end = info['chunk_range']
+        if start == end:
+            # No tokens => no features
+            pred = torch.zeros(model.config.num_labels)
+        else:
+            # Take max across chunks for each feature
+            chunk_preds = all_chunk_preds[start:end]
+            pred, _ = torch.max(chunk_preds, dim=0)
+        predictions[info['original_idx']] = (pred > 0.5).int().numpy()
+
+    return np.array(predictions)
+
+def predict_batch(model, tokenizer, texts, chunk_size=CHUNK_SIZE, subbatch_size=32):
+    return get_predictions_chunked_batch(model, tokenizer, texts, chunk_size, subbatch_size)
+
+def predict_text(model, tokenizer, text, chunk_size=CHUNK_SIZE, subbatch_size=32):
+    batch_preds = predict_batch(model, tokenizer, [text], chunk_size, subbatch_size)
+    return batch_preds[0]
+
+if __name__ == "__main__":
+    print("my brain hurts!")
+    #https://huggingface.co/Blablablab/neurobiber

p2/quest/slurm_jobs/neurobiber_label.sh

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+#!/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=neurobiber-categorization 
+#SBATCH --output=neurobiber-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 p1 categorization script"
+
+python /home/nws8519/git/mw-lifecycle-analysis/p2/quest/python_scripts/neurobiber_labeling.py
+
+echo "job finished, cleaning up"
+
+conda deactivate
+
+echo "job pau at: $(date)"
+

p2/quest/slurm_jobs/unsupervised_categorizaton_job.sh

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+#!/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=p1-categorization 
+#SBATCH --output=p1-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 olmo
+
+echo "running the p1 categorization script"
+
+
+echo "job finished, cleaning up"
+
+conda deactivate
+
+echo "job pau at: $(date)"
+

p2/quest/todo.txt

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+[ ] generate clean rows for each comment from the discussion data 
+[ ] get data onto quest 
+[ ] run neurobiber over the data set, appending vectors onto the array 
+[ ] set up the unsupervised classification pipeline 
+[ ] iterate the prompt
+[ ] run the classification of the prompt