first description

README.md

@@ -0,0 +1,73 @@
+---
+language:
+  - spa
+  - agr
+tags:
+  - translation
+  - nllb
+  - aguaruna
+  - spanish
+license: apache-2.0
+---
+
+# NLLB-200-600M Fine-tuned for Aguaruna-Spanish Translation
+
+This is a **NLLB-200-600M** model fine-tuned for translating between Aguaruna and Spanish languages.
+
+## How to use the model:
+
+```python
+!pip install sentencepiece transformers==4.33
+import torch
+from transformers import NllbTokenizer, AutoModelForSeq2SeqLM
+
+def fix_tokenizer(tokenizer, new_lang='agr_Latn'):
+    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
+    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)
+    tokenizer.added_tokens_encoder = {}
+    tokenizer.added_tokens_decoder = {}
+
+MODEL_URL = "hectordiazgomez/nllb-spa-awa-v3"
+model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_URL)
+tokenizer = NllbTokenizer.from_pretrained(MODEL_URL)
+fix_tokenizer(tokenizer)
+
+def translate(
+    text,
+    model,
+    tokenizer,
+    src_lang='agr_Latn',
+    tgt_lang='spa_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("Uchi piipichi buuke baejai.", model=model, tokenizer=tokenizer)
+# El niño se quedo con el pelo.
+```