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dag).| Metric | Value |
|---|---|
| Corpus BLEU | 15.41 |
| Corpus chrF++ | 36.36 |
| Adapter Type | PEFT LoRA (r=16, alpha=32) |
| Base Model | facebook/nllb-200-distilled-600M |
q_proj, v_proj) and customized embedding modules (shared, lm_head).transformers and peft libraries:1import torch
2from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
3from peft import PeftModel
4
5base_model_name = "facebook/nllb-200-distilled-600M"
6adapter_name = "abdulhafis/en-dag-translator" # or path to loaded adapter directory
7
8# 1. Load extended tokenizer from the adapter
9tokenizer = AutoTokenizer.from_pretrained(adapter_name)
10
11# 2. Load NLLB base model and resize embeddings
12base_model = AutoModelForSeq2SeqLM.from_pretrained(base_model_name)
13base_model.resize_token_embeddings(len(tokenizer))
14
15# 3. Mount PEFT adapter weights
16model = PeftModel.from_pretrained(base_model, adapter_name)
17model.eval()
18
19# 4. Translate English to Dagbani
20tokenizer.src_lang = "eng_Latn"
21text = "The children went to the farm."
22inputs = tokenizer(text, return_tensors="pt")
23
24tokenizer.tgt_lang = "dag_Latn"
25outputs = model.generate(
26 **inputs,
27 max_length=128,
28 num_beams=4,
29 forced_bos_token_id=tokenizer.convert_tokens_to_ids("dag_Latn")
30)
31print(tokenizer.decode(outputs[0], skip_special_tokens=True))