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de0dd09 6eedef4 de0dd09 6eedef4 de0dd09 6eedef4 de0dd09 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | from typing import Dict, List, Any
import numpy as np
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
SAHIDIC_TAG = "з"
BOHAIRIC_TAG = "б"
GENERATION_KWARGS = {
"max_new_tokens": 128,
"min_new_tokens": 1,
"early_stopping": True,
"num_beams": 5,
"output_scores": True,
"return_dict_in_generate": True,
}
COPTIC_TO_GREEK = {
"ⲁ": "α",
"ⲃ": "β",
"ⲅ": "γ",
"ⲇ": "δ",
"ⲉ": "ε",
"ⲋ": "ϛ",
"ⲍ": "ζ",
"ⲏ": "η",
"ⲑ": "θ",
"ⲓ": "ι",
"ⲕ": "κ",
"ⲗ": "λ",
"ⲙ": "μ",
"ⲛ": "ν",
"ⲝ": "ξ",
"ⲟ": "ο",
"ⲡ": "π",
"ⲣ": "ρ",
"ⲥ": "σ",
"ⲧ": "τ",
"ⲩ": "υ",
"ⲫ": "φ",
"ⲭ": "χ",
"ⲯ": "ψ",
"ⲱ": "ω",
"ϣ": "s",
"ϥ": "f",
"ϧ": "k",
"ϩ": "h",
"ϫ": "j",
"ϭ": "c",
"ϯ": "t",
}
def greekify(coptic_text: str) -> str:
chars = []
for c in coptic_text:
l_c = c.lower()
chars.append(COPTIC_TO_GREEK.get(l_c, l_c))
return "".join(chars)
class EndpointHandler:
def __init__(self, path: str = ""):
self.model = AutoModelForSeq2SeqLM.from_pretrained(path)
self.tokenizer = AutoTokenizer.from_pretrained(path)
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
Translate Coptic text to English.
data args:
inputs (:obj:`str`): Coptic text to translate
from_bohairic (:obj:`bool`, optional): Input is Bohairic dialect (default: Sahidic)
output_confidence (:obj:`bool`, optional): Include confidence score
Return:
A :obj:`list` of :obj:`dict`: translation results
"""
inputs = data.pop("inputs", data)
from_bohairic = data.pop("from_bohairic", False)
output_confidence = data.pop("output_confidence", False)
text = greekify(inputs.lower())
if from_bohairic:
text = f"{BOHAIRIC_TAG} {text}"
else:
text = f"{SAHIDIC_TAG} {text}"
input_tensors = self.tokenizer.encode(text, return_tensors="pt")
outputs = self.model.generate(
input_tensors[:, : self.tokenizer.model_max_length],
**GENERATION_KWARGS,
)
translated_text = self.tokenizer.decode(
outputs.sequences[0], skip_special_tokens=True
)
if not output_confidence:
return [{"translation": translated_text}]
scores = outputs.scores
confidences = [
torch.softmax(score, dim=-1).max().item() for score in scores
]
num_words = len(translated_text.split())
scaled_probability = np.exp(sum(np.log(confidences)) / num_words)
return [{"translation": translated_text, "confidence": scaled_probability}]
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