Update README.md

README.md

@@ -3,9 +3,76 @@ language:
 - ja
 - ain
 pipeline_tag: translation
+license: cc-by-nc-4.0
 ---
 
-## Disclaimer
+# Disclaimer
 This model is only an preliminary experimental result and is not suitable for any sort of serious use. This model's capability is at best extremely limited and unreliable.
 
-That said, look forward to good things to come. This is my debut to the field of Ainu NLP.
+That said, look forward to good things to come. This is my debut to the field of Ainu NLP.
+
+# Acknowledgements
+I am indebted to [Michal Ptaszynski](https://huggingface.co/ptaszynski) for his guidance and encouragement, [Karol Nowakowski](https://huggingface.co/karolnowakowski) for his work to compile an expansive parallel corpus, [David Dale](https://huggingface.co/cointegrated) for his [Medium article](https://cointegrated.medium.com/how-to-fine-tune-a-nllb-200-model-for-translating-a-new-language-a37fc706b865) that helped me to quickly and smoothly take this first step.
+
+# How to use this model
+The following is adapted from [slone/nllb-rus-tyv-v1](https://huggingface.co/slone/nllb-rus-tyv-v1).
+
+```Python
+# the version of transformers is important!
+!pip install sentencepiece transformers==4.33
+import torch
+from transformers import NllbTokenizer, AutoModelForSeq2SeqLM
+
+def fix_tokenizer(tokenizer, new_lang='ain_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 = "TwentyNine/nllb-jpn-ain-v1"
+model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_URL)
+tokenizer = NllbTokenizer.from_pretrained(MODEL_URL)
+fix_tokenizer(tokenizer)
+
+def translate(
+    text,
+    model,
+    tokenizer,
+    src_lang='jpn_Jpan',
+    tgt_lang='ain_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 
+
+translate("肉が食べたいな。", model=model, tokenizer=tokenizer)
+# 'kam c=e rusuy na.'
+```