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import streamlit as st
import torch
from transformers import NllbTokenizer, AutoModelForSeq2SeqLM
def fix_tokenizer(tokenizer, new_lang='fer_Latn'):
""" Add a new language token to the tokenizer vocabulary (this should be done each time after its initialization) """
old_len = len(tokenizer) - int(new_lang in tokenizer.added_tokens_encoder)
tokenizer.lang_code_to_id[new_lang] = old_len-1
tokenizer.id_to_lang_code[old_len-1] = new_lang
# always move "mask" to the last position
tokenizer.fairseq_tokens_to_ids["<mask>"] = len(tokenizer.sp_model) + len(tokenizer.lang_code_to_id) + tokenizer.fairseq_offset
tokenizer.fairseq_tokens_to_ids.update(tokenizer.lang_code_to_id)
tokenizer.fairseq_ids_to_tokens = {v: k for k, v in tokenizer.fairseq_tokens_to_ids.items()}
if new_lang not in tokenizer._additional_special_tokens:
tokenizer._additional_special_tokens.append(new_lang)
# clear the added token encoder; otherwise a new token may end up there by mistake
tokenizer.added_tokens_encoder = {}
tokenizer.added_tokens_decoder = {}
MODEL_URL = "DinoDelija/nllb_english_fering"
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_URL)
tokenizer = NllbTokenizer.from_pretrained(MODEL_URL)
fix_tokenizer(tokenizer)
def translate(
text,
model,
tokenizer,
src_lang='eng_Latn',
tgt_lang='fer_Latn',
max_length='auto',
num_beams=4,
n_out=None,
**kwargs
):
tokenizer.src_lang = src_lang
encoded = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
if max_length == 'auto':
max_length = int(32 + 2.0 * encoded.input_ids.shape[1])
model.eval()
generated_tokens = model.generate(
**encoded.to(model.device),
forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang],
max_length=max_length,
num_beams=num_beams,
num_return_sequences=n_out or 1,
**kwargs
)
out = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
if isinstance(text, str) and n_out is None:
return out[0]
return
# pipeline = pipeline(task="translation", model="DinoDelija/nllb_english_fering")
st.title("Translate English To Fering")
eng_sentence = st.text_input("English Sentence", key="eng")
translation = translate(eng_sentence, model=model, tokenizer=tokenizer)
print(eng_sentence)
print(translation)
# st.write('Fering transaltion of the sentence is: ', translation)
if translation != None:
st.markdown(f'''Fering: \n
{translation}''') |