license: mit
language:
- en
base_model: microsoft/deberta-v3-base
pipeline_tag: text-classification
metrics:
- f1
- accuracy
tags:
- text-classification
- deberta-v3
- spatial-language
- spatial-reasoning
library_name: transformers
DeBERTa-v3-base — Spatial Language Detection
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
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):
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
- 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 — 799 words (570 not-spatial, 229 spatial)
| class | precision | recall | F1 | support |
|---|---|---|---|---|
| not_spatial | 0.958 | 0.925 | 0.941 | 570 |
| spatial | 0.827 | 0.900 | 0.862 | 229 |
Overall accuracy: 0.917 (733 of 799 words correct) · macro-F1 0.901 · Cohen's κ 0.803
Reading it per class: the model catches 90.0% of truly-spatial words (recall) at 82.7% precision; for non-spatial words it's 92.5% recall at 95.8% precision. (In clinical terms: sensitivity 0.900, specificity 0.925, PPV 0.827, NPV 0.958.)
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 ~4,400 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 = 1.816, 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.