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dee3f71
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Parent(s):
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Upload gramformer.py
Browse files- gramformer.py +128 -0
gramformer.py
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class Gramformer:
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def __init__(self, models=1, use_gpu=False):
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from transformers import AutoTokenizer
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from transformers import AutoModelForSeq2SeqLM
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#from lm_scorer.models.auto import AutoLMScorer as LMScorer
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import errant
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self.annotator = errant.load('en')
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if use_gpu:
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device= "cuda:0"
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else:
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device = "cpu"
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batch_size = 1
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#self.scorer = LMScorer.from_pretrained("gpt2", device=device, batch_size=batch_size)
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self.device = device
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correction_model_tag = "prithivida/grammar_error_correcter_v1"
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self.model_loaded = False
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if models == 1:
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self.correction_tokenizer = AutoTokenizer.from_pretrained(correction_model_tag, use_auth_token=False)
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self.correction_model = AutoModelForSeq2SeqLM.from_pretrained(correction_model_tag, use_auth_token=False)
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self.correction_model = self.correction_model.to(device)
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self.model_loaded = True
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print("[Gramformer] Grammar error correct/highlight model loaded..")
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elif models == 2:
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# TODO
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print("TO BE IMPLEMENTED!!!")
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def correct(self, input_sentence, max_candidates=1):
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if self.model_loaded:
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correction_prefix = "gec: "
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input_sentence = correction_prefix + input_sentence
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input_ids = self.correction_tokenizer.encode(input_sentence, return_tensors='pt')
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input_ids = input_ids.to(self.device)
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preds = self.correction_model.generate(
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input_ids,
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do_sample=True,
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max_length=128,
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# top_k=50,
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# top_p=0.95,
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num_beams=7,
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early_stopping=True,
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num_return_sequences=max_candidates)
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corrected = set()
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for pred in preds:
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corrected.add(self.correction_tokenizer.decode(pred, skip_special_tokens=True).strip())
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#corrected = list(corrected)
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#scores = self.scorer.sentence_score(corrected, log=True)
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#ranked_corrected = [(c,s) for c, s in zip(corrected, scores)]
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#ranked_corrected.sort(key = lambda x:x[1], reverse=True)
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return corrected
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else:
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print("Model is not loaded")
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return None
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def highlight(self, orig, cor):
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edits = self._get_edits(orig, cor)
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orig_tokens = orig.split()
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ignore_indexes = []
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for edit in edits:
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edit_type = edit[0]
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edit_str_start = edit[1]
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edit_spos = edit[2]
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edit_epos = edit[3]
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edit_str_end = edit[4]
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# if no_of_tokens(edit_str_start) > 1 ==> excluding the first token, mark all other tokens for deletion
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for i in range(edit_spos+1, edit_epos):
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ignore_indexes.append(i)
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if edit_str_start == "":
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if edit_spos - 1 >= 0:
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new_edit_str = orig_tokens[edit_spos - 1]
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edit_spos -= 1
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else:
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new_edit_str = orig_tokens[edit_spos + 1]
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edit_spos += 1
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if edit_type == "PUNCT":
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st = "<a type='" + edit_type + "' edit='" + \
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edit_str_end + "'>" + new_edit_str + "</a>"
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else:
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st = "<a type='" + edit_type + "' edit='" + new_edit_str + \
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" " + edit_str_end + "'>" + new_edit_str + "</a>"
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orig_tokens[edit_spos] = st
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elif edit_str_end == "":
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st = "<d type='" + edit_type + "' edit=''>" + edit_str_start + "</d>"
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orig_tokens[edit_spos] = st
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else:
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st = "<c type='" + edit_type + "' edit='" + \
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edit_str_end + "'>" + edit_str_start + "</c>"
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orig_tokens[edit_spos] = st
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for i in sorted(ignore_indexes, reverse=True):
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del(orig_tokens[i])
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return(" ".join(orig_tokens))
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def detect(self, input_sentence):
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# TO BE IMPLEMENTED
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pass
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def _get_edits(self, orig, cor):
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orig = self.annotator.parse(orig)
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cor = self.annotator.parse(cor)
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alignment = self.annotator.align(orig, cor)
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edits = self.annotator.merge(alignment)
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if len(edits) == 0:
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return []
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edit_annotations = []
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for e in edits:
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e = self.annotator.classify(e)
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edit_annotations.append((e.type[2:], e.o_str, e.o_start, e.o_end, e.c_str, e.c_start, e.c_end))
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if len(edit_annotations) > 0:
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return edit_annotations
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else:
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return []
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def get_edits(self, orig, cor):
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return self._get_edits(orig, cor)
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