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import language_tool_python
from transformers import AutoTokenizer, AutoModelForMaskedLM
import torch
import numpy as np
import spacy
tool = language_tool_python.LanguageTool('en-US')
model_name="distilbert-base-multilingual-cased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForMaskedLM.from_pretrained(model_name)
model.eval()
nlp = spacy.load("en_core_web_sm")
def pseudo_perplexity(text, max_len=128):
"""
We want to return
{
"score": normalized value from 0 to 100,
"errors": [
{
"start": word index,
"end": word index,
"message": "error message"
}
]
}
"""
input_ids = tokenizer.encode(text, return_tensors="pt")[0]
if len(input_ids) > max_len:
raise ValueError(f"Input too long for model (>{max_len} tokens).")
loss_values = []
for i in range(1, len(input_ids) - 1): # skip [CLS] and [SEP]
masked_input = input_ids.clone()
masked_input[i] = tokenizer.mask_token_id
with torch.no_grad():
outputs = model(masked_input.unsqueeze(0))
logits = outputs.logits[0, i]
probs = torch.softmax(logits, dim=-1)
true_token_id = input_ids[i].item()
prob_true_token = probs[true_token_id].item()
log_prob = np.log(prob_true_token + 1e-12)
loss_values.append(-log_prob)
# get longest sequence of tokens with perplexity over some threshold
threshold = 12 # Define a perplexity threshold
longest_start, longest_end = 0, 0
current_start, current_end = 0, 0
max_length = 0
curr_loss = 0
for i, loss in enumerate(loss_values):
if loss > threshold:
if current_start == current_end: # Start a new sequence
current_start = i
current_end = i + 1
curr_loss = loss
else:
if current_end - current_start > max_length:
longest_start, longest_end = current_start, current_end
max_length = current_end - current_start
current_start, current_end = 0, 0
if current_end - current_start > max_length: # Check the last sequence
longest_start, longest_end = current_start, current_end
longest_sequence = (longest_start, longest_end)
ppl = np.exp(np.mean(loss_values))
res = {
"score": __fluency_score_from_ppl(ppl),
"errors": [
{
"start": longest_sequence[0],
"end": longest_sequence[1],
"message": f"Perplexity above threshold: {curr_loss}"
}
]
}
return res
def __fluency_score_from_ppl(ppl, midpoint=20, steepness=0.3):
"""
Use a logistic function to map perplexity to 0–100.
Midpoint is the PPL where score is 50.
Steepness controls curve sharpness.
"""
score = 100 / (1 + np.exp(steepness * (ppl - midpoint)))
return round(score, 2)
def grammar_errors(text) -> tuple[int, list[str]]:
"""
Returns
int: number of grammar errors
list: grammar errors
tuple: (start, end, error message)
"""
matches = tool.check(text)
grammar_score = len(matches)/len(text.split())
r = []
for match in matches:
words = text.split()
char_to_word = []
current_char = 0
for i, word in enumerate(words):
for _ in range(len(word)):
char_to_word.append(i)
current_char += len(word)
if current_char < len(text): # Account for spaces between words
char_to_word.append(i)
current_char += 1
start = char_to_word[match.offset]
end = char_to_word[match.offset + match.errorLength - 1] + 1
r.append({"start": start, "end": end, "message": match.message})
struct_err = __check_structural_grammar(text)
r.extend(struct_err)
res = {
"score": __grammar_score_from_prob(grammar_score),
"errors": r
}
return res
def __grammar_score_from_prob(error_ratio, steepness=10):
"""
Transform the number of errors divided by words into a score from 0 to 100.
Steepness controls how quickly the score drops as errors increase.
"""
score = 100 / (1 + np.exp(steepness * error_ratio))
return round(score, 2)
def __check_structural_grammar(text):
doc = nlp(text)
issues = []
# 1. Missing main verb (ROOT)
root_verbs = [tok for tok in doc if tok.dep_ == "ROOT" and tok.pos_ in {"VERB", "AUX"}]
if not root_verbs:
root_root = [tok for tok in doc if tok.dep_ == "ROOT"]
token = root_root[0] if root_root else doc[0]
issues.append({
"start": token.i,
"end": token.i + 1,
"message": "Sentence is missing a main verb (no ROOT verb)."
})
# 2. Verb(s) present but no subject
verbs = [tok for tok in doc if tok.pos_ in {"VERB", "AUX"}]
subjects = [tok for tok in doc if tok.dep_ in {"nsubj", "nsubjpass"}]
if verbs and not subjects:
for verb in verbs:
issues.append({
"start": verb.i,
"end": verb.i + 1,
"message": "Sentence has verb(s) but no subject (possible fragment)."
})
# 3. Dangling prepositions
for tok in doc:
if tok.pos_ == "ADP" and len(list(tok.children)) == 0:
issues.append({
"start": tok.i,
"end": tok.i + 1,
"message": f"Dangling preposition '{tok.text}' (no object or complement)."
})
# 4. Noun pile-up (no verbs, all tokens are nominal)
if not any(tok.pos_ in {"VERB", "AUX"} for tok in doc) and \
all(tok.pos_ in {"NOUN", "PROPN", "ADJ", "DET", "NUM"} for tok in doc if tok.is_alpha):
token = doc[0]
issues.append({
"start": token.i,
"end": token.i + 1,
"message": "Sentence lacks a verb or any verbal structure (nominal phrase pile-up)."
})
# 5. Multiple ROOTs (possible run-on)
root_count = sum(1 for tok in doc if tok.dep_ == "ROOT")
if root_count > 1:
for tok in doc:
if tok.dep_ == "ROOT":
issues.append({
"start": tok.i,
"end": tok.i + 1,
"message": "Sentence has multiple ROOTs — possible run-on sentence."
})
return issues
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