Neurobiber: Fast and Interpretable Stylistic Feature Extraction
Neurobiber is a transformer-based model that quickly predicts 96 interpretable stylistic features in text. These features are inspired by Biber’s multidimensional framework of linguistic style, capturing everything from pronouns and passives to modal verbs and discourse devices. By combining a robust linguistically informed feature set with the speed of neural inference, NeuroBiber enables large-scale stylistic analyses that were previously infeasible.
Why Neurobiber?
Extracting Biber-style features typically involves running a full parser or specialized tagger, which can be computationally expensive for large datasets or real-time applications. NeuroBiber overcomes these challenges by:
- Operating up to 56x faster than parsing-based approaches.
- Retaining the interpretability of classical Biber-like feature definitions.
- Delivering high accuracy on diverse text genres (e.g., social media, news, literary works).
- Allowing seamless integration with modern deep learning pipelines via Hugging Face.
By bridging detailed linguistic insights and industrial-scale performance, Neurobiber supports tasks in register analysis, style transfer, and more.
Example Script
Below is an example showing how to load Neurobiber from Hugging Face, process single or multiple texts, and obtain a 96-dimensional binary vector for each input.
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]
Single-Text Usage
model, tokenizer = load_model_and_tokenizer()
sample_text = "This is a sample text demonstrating certain stylistic features."
predictions = predict_text(model, tokenizer, sample_text)
print("Binary feature vector:", predictions)
# For example: [0, 1, 0, 1, ... 1, 0] (96-length)
Batch Usage
docs = [
"First text goes here.",
"Second text, slightly different style."
]
model, tokenizer = load_model_and_tokenizer()
preds = predict_batch(model, tokenizer, docs)
print(preds.shape) # (2, 96)
How It Works
Neurobiber is fine-tuned RoBERTa. Given a text:
- The text is split into chunks (up to 512 tokens each).
- Each chunk is fed through the model to produce 96 logistic outputs (one per feature).
- The feature probabilities are aggregated across chunks so that each feature is marked as
1if it appears in at least one chunk.
Each row in preds is a 96-element array corresponding to the feature order in BIBER_FEATURES.
Interpreting Outputs
- Each element in the vector is a binary label (0 or 1), indicating the model’s detection of a specific linguistic feature (e.g., BIN_VBD for past tense verbs).
- For long texts, we chunk them into segments of length 512 tokens. If a feature appears in any chunk, you get a 1 for that feature.