A fine-tuned microsoft/deberta-v3-base for word-level spatial-language detection: given an utterance and a target word, it decides whether that word is being used as spatial language (location, direction, or a spatial relationship) in that context — 1 = spatial, 0 = not. The same word can be spatial in one utterance ("go up the ramp") and not in another ("what's up?"), so the model always judges a word together with its sentence.

For the full pipeline (dictionary gating, calibrated confidence, evaluation) and example datasets, see the GitHub repo: https://github.com/SamAgnoli/spatial-language-classifier

Input format

This is a sentence-pair classifier: pass the utterance as the first segment and the target word as the second — tokenizer(utterance, target_word). Passing a whole sentence on its own is not how the model was trained and gives unreliable results.

Labels

id	label	meaning
0	not_spatial	word is not spatial language
1	spatial	word is spatial language

Usage

python

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

model_id = "SamAgnoli/deberta-v3-base-spatial-language-detection"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

utterance   = "The cat is sitting on top of the bookshelf."
target_word = "top"                       # the word you want judged
inputs = tokenizer(utterance, target_word, return_tensors="pt", truncation=True)
with torch.no_grad():
    logits = model(**inputs).logits
pred = logits.argmax(-1).item()
print(model.config.id2label[pred])        # -> "spatial"

Or with a pipeline (note the text / text_pair keys):

python

from transformers import pipeline

clf = pipeline("text-classification",
               model="SamAgnoli/deberta-v3-base-spatial-language-detection")
print(clf({"text": "The cat is sitting on top of the bookshelf.", "text_pair": "top"}))

Training

Base model: microsoft/deberta-v3-base
Task: binary sentence-pair classification (a word, in its utterance, spatial vs. not)
Split: group-aware 70/15/15 by speaker session (no session spans splits)
Hyperparameters: 2 epochs · lr 2e-5 · batch 16 · weight decay 0.01 · warmup 0.1 · max_length 128 · fp16 · seed 42 · best checkpoint by F1
Class imbalance: no class weighting or resampling. The dictionary gate already raises the positive rate from ~4% of all words to ~30% of candidates, and checkpoint selection used minority-class F1 rather than accuracy.
Framework: 🤗 Transformers

Evaluation (held-out test set)

Reported for two views: dictionary candidates only (the meaningful view — words a spatial dictionary flags as plausibly spatial) and overall (every word, dominated by trivially non-spatial tokens).

Candidates only — 622 words (393 not-spatial, 229 spatial)

class	precision	recall	F1	support
not_spatial	0.938	0.891	0.914	393
spatial	0.827	0.900	0.862	229

Spatial candidate accuracy: 0.894 (556 of 622 words correct) · macro-F1 0.888 · Cohen's κ 0.776

Reading it per class: the model catches 90.0% of truly-spatial words (recall) at 82.7% precision; for non-spatial words it's 89.1% recall at 93.8% precision. (sensitivity 0.900, specificity 0.891, PPV 0.827, NPV 0.938.)

Overall — every word, 5,207 tokens

accuracy 0.987 · spatial-F1 0.862 · Cohen's κ 0.855

The two views share the same spatial predictions (229 spatial words, same 206 caught). Only the non-spatial pool differs, which is why "overall" accuracy looks higher — it's padded with ~4,600 easy non-candidate words the model trivially gets right. Judge the model by the candidates-only view.

Calibration

Raw probabilities are over-confident, so a post-hoc temperature scaling factor (T = 1.887, fit on the validation candidates) rescales them into a calibrated P(spatial) you can read literally. Temperature scaling is monotonic, so the hard 0/1 decision is unchanged. See section 7.5 of the repo.

Intended use & limitations

Built for per-word spatial judgments within an English utterance. In production it is paired with a spatial-dictionary gate that selects candidate words; words the dictionary misses (e.g., misspellings) are never sent to the model.
Trained on parent–child tinkering-reflection speech — performance on other domains, genres, or languages is not guaranteed.
The data is strongly imbalanced (~4% spatial overall); judge quality by the candidates-only view, not the overall numbers.
Review predictions before relying on them in downstream systems.