Update README.md

README.md

@@ -10,4 +10,30 @@ widget:
 
 # NMT for Finno-Ugric Languages
 
-This is an NMT system for translating between Võro, Livonian, North Sami, South Sami as well as Estonian, Finnish, Latvian and English. It was created by fine-tuning Facebook's m2m100-418M on the liv4ever and smugri datasets.
+This is an NMT system for translating between Võro, Livonian, North Sami, South Sami as well as Estonian, Finnish, Latvian and English. It was created by fine-tuning Facebook's m2m100-418M on the liv4ever and smugri datasets.
+
+## Tokenizer
+Four language codes were added to the tokenizer: __liv__, __vro__, __sma__ and __sme__. Currently the m2m100 tokenizer loads the list of languages from a hard-coded list, so it has to be updated after loading; see the code example below.
+
+## Usage example
+Install the transformers and sentencepiece libraries: `pip install sentencepiece transformers`
+
+```from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+tokenizer = AutoTokenizer.from_pretrained("tartuNLP/m2m100_418M_smugri")
+#Fix the language codes in the tokenizer
+tokenizer.id_to_lang_token = dict(list(tokenizer.id_to_lang_token.items()) + list(tokenizer.added_tokens_decoder.items()))
+tokenizer.lang_token_to_id = dict(list(tokenizer.lang_token_to_id.items()) + list(tokenizer.added_tokens_encoder.items()))
+tokenizer.lang_code_to_token = { k.replace("_", ""): k for k in tokenizer.additional_special_tokens }
+tokenizer.lang_code_to_id = { k.replace("_", ""): v for k, v in tokenizer.lang_token_to_id.items() }
+
+model = AutoModelForSeq2SeqLM.from_pretrained("tartuNLP/m2m100_418M_smugri")
+
+tokenizer.src_lang = 'liv'
+
+encoded_src = tokenizer("Līvõ kēļ jelāb!!", return_tensors="pt")
+
+encoded_out = model.generate(**encoded_src, forced_bos_token_id = tokenizer.get_lang_id("sme"))
+print(tokenizer.batch_decode(encoded_out, skip_special_tokens=True))
+```
+
+The output is `Livčča giella eallá.`