Re-add acknowledgement, bump version for release

README.md

@@ -168,4 +168,58 @@ mt.translate(
 )
 ```
 
-Learn more in the [huggingface docs](https://huggingface.co/transformers/main_classes/model.html#transformers.generation_utils.GenerationMixin.generate).
+Learn more in the [huggingface docs](https://huggingface.co/transformers/main_classes/model.html#transformers.generation_utils.GenerationMixin.generate).
+
+
+## Acknowledgement
+
+`dl-translate` is built on top of Huggingface's implementation of multilingual BART finetuned on many-to-many translation of over 50 languages, which is [documented here](https://huggingface.co/transformers/master/model_doc/mbart.html). The original paper was written by Tang et. al from Facebook AI Research; you can [find it here](https://arxiv.org/pdf/2008.00401.pdf) and cite it using the following:
+```
+@article{tang2020multilingual,
+  title={Multilingual translation with extensible multilingual pretraining and finetuning},
+  author={Tang, Yuqing and Tran, Chau and Li, Xian and Chen, Peng-Jen and Goyal, Naman and Chaudhary, Vishrav and Gu, Jiatao and Fan, Angela},
+  journal={arXiv preprint arXiv:2008.00401},
+  year={2020}
+}
+```
+
+`dlt` is a wrapper with useful `utils` to save you time. For huggingface's `transformers`, the following snippet is shown as an example:
+```python
+from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
+
+article_hi = "संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है"
+article_ar = "الأمين العام للأمم المتحدة يقول إنه لا يوجد حل عسكري في سوريا."
+
+model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
+tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
+
+# translate Hindi to French
+tokenizer.src_lang = "hi_IN"
+encoded_hi = tokenizer(article_hi, return_tensors="pt")
+generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.lang_code_to_id["fr_XX"])
+tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+# => "Le chef de l 'ONU affirme qu 'il n 'y a pas de solution militaire en Syria."
+
+# translate Arabic to English
+tokenizer.src_lang = "ar_AR"
+encoded_ar = tokenizer(article_ar, return_tensors="pt")
+generated_tokens = model.generate(**encoded_ar, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
+tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+# => "The Secretary-General of the United Nations says there is no military solution in Syria."
+```
+
+With `dlt`, you can run:
+```python
+import dl_translate as dlt
+
+article_hi = "संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है"
+article_ar = "الأمين العام للأمم المتحدة يقول إنه لا يوجد حل عسكري في سوريا."
+
+mt = dlt.TranslationModel()
+translated_fr = mt.translate(article_hi, source=dlt.lang.HINDI, target=dlt.lang.FRENCH)
+translated_en = mt.translate(article_ar, source=dlt.lang.ARABIC, target=dlt.lang.ENGLISH)
+```
+
+Notice you don't have to think about tokenizers, condition generation, pretrained models, and regional codes; you can just tell the model what to translate!
+
+If you are experienced with `huggingface`'s ecosystem, then you should be familiar enough with the example above that you wouldn't need this library. However, if you've never heard of huggingface or mBART, then I hope using this library will give you enough motivation to [learn more about them](https://github.com/huggingface/transformers) :)

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="dl-translate",
-    version="0.1.0rc3",
+    version="0.1.0",
     author="Xing Han Lu",
     author_email="[email protected]",
     description="A deep learning-based translation library built on Huggingface transformers",