End-to-end English text → US K-6 grade level predictor.
Given an English sentence, it returns:
a continuous CEFR score in [1.0, 6.0] (1 = A1 … 6 = C2),
the discrete CEFR level (A1–C2),
a continuous US grade in [0.0, 6.0] (0 = preschool, 1–6 = grades 1–6),
the nearest grade word (preschool, one, … six).
It is a two-stage model:
CEFR head — a 3-layer truncated bert-base-uncased with a single-linear
regression head. This stage is loaded verbatim from
SNALYF/CEFR_Bert_Fine-tuned
(Ace-CEFR baseline, arXiv:2506.14046 §4.5.1).
Grade head — a piecewise-linear isotonic calibrator that maps the CEFR
score onto a numeric US grade. Calibrated on the ednoda K-6 snapshot (2,448
sentences, ~2,989 (text, grade) pairs after multi-grade rows are expanded).
Usage
The model uses a custom modeling.py, so loading needs trust_remote_codeor
a local download of the repo. Either form works:
python
1# Option A: clone or hf_hub_download then load locally2from modeling import TextToGradePredictor
3model = TextToGradePredictor.from_pretrained("YourUser/text-to-grade")4model.eval()56results = model.predict([7"I'm from Canada.",8"The committee unanimously ratified the long-debated treaty.",9])10for r in results:11print(r)12# {'text': "I'm from Canada.", 'cefr_score': 2.313, 'cefr_level': 'A2',13# 'grade_num': 4.05, 'grade_word': 'four'}
predict() batches under the hood (batch_size=32 by default). For raw
forward access you can call model(input_ids, attention_mask, token_type_ids)
or model.cefr_score(list_of_texts) to skip the grade head.
Outputs and label maps
CEFR score
CEFR level
1
A1
2
A2
3
B1
4
B2
5
C1
6
C2
grade num
grade word
0
preschool
1
one
2
two
3
three
4
four
5
five
6
six
Calibration data
The grade head was fit on the ednoda snapshot — a 2,448-row corpus of K-6
English-learner expressions, vocabulary, and question prompts, each labelled
with one or more grade words. Multi-grade rows were exploded so each
(text, grade) becomes its own row (~2,989 pairs).
Per-grade CEFR distribution from the calibration set:
grade
n
mean CEFR
mean level
preschool (0)
5
1.34
A1
one (1)
2
1.39
A1
three (3)
717
1.69
A2
four (4)
757
1.68
A2
five (5)
860
1.88
A2
six (6)
648
2.00
A2
The fit was an sklearn.IsotonicRegression(increasing=True) from CEFR score
to grade number, serialised as a 51-point piecewise-linear table inside
config.json. At inference the model interpolates with np.interp — no scikit
dependency is required to load.
Metrics (train-set, on ednoda)
MAE on grade: 0.92
Exact-grade match: 30.0 %
Within ±1 grade: 82.0 %
These are training-set fit metrics; they are an upper bound on the
calibrator's ceiling, not a held-out estimate. The CEFR head's own held-out
metrics (445-row ACE-CEFR test) are MSE 0.567, 51.5 % exact level, 93.9 %
within ±1 level — see the source repo.
Caveats
Saturation at high CEFR. ednoda content is K-6 English-learner material;
almost all of it scores A1–A2. As a result the calibrator is essentially
flat for B1+: a B2 sentence and a C2 sentence both clip to grade_word = "six".
When the upstream CEFR score is ≥ ~3, treat the grade as "≥ six" rather than
a literal value.
Weak ends of the range. Only 5 preschool and 2 grade-1 rows exist, so
predictions in [0, 1] are unreliable.
Single-sentence input. Both stages were trained on sentence-level text;
do not pass paragraph- or document-length inputs without segmenting first.
MIT (same as the upstream CEFR head). Re-uploading the upstream weights here
is done with credit to the original author; if you are not the same author,
please obtain their consent before publishing this repo.
