# Fine tuning nllb model

**URL:** <https://discuss.huggingface.co/t/fine-tuning-nllb-model/30883>\
**Category:** Beginners\
**Created:** [February 1, 2023, 7:12am UTC](https://discuss.huggingface.co/t/fine-tuning-nllb-model/30883 "2023-02-01T07:12:23Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![anjalyjayakrishnan](https://avatars.discourse-cdn.com/v4/letter/a/b782af/32.png) [@anjalyjayakrishnan](https://discuss.huggingface.co/u/anjalyjayakrishnan)\
**Post date:** [February 1, 2023, 7:12am UTC](https://discuss.huggingface.co/t/fine-tuning-nllb-model/30883/1 "2023-02-01T07:12:23Z")

</div>

Hi,

I am trying to fine tune NLLB model for Hindi-Dogri(Indian languages) translation. Referred the translation pipeline given in the page [Translation - Hugging Face Course](https://huggingface.co/course/chapter7/4?fw=tf) . My dataset looks like as given below:

DatasetDict({  
train: Dataset({  
features: [‘id’, ‘translation’],  
num\_rows: 6356  
})  
test: Dataset({  
features: [‘id’, ‘translation’],  
num\_rows: 1589  
})  
})

Individual item looks like:

{‘id’: 5583,  
‘translation’: {‘do’: ‘जिनेंगी अस मनुक्खें दे ज्ञान थमां सखाई दियें गल्लें च नेई लेकन पवित्र आत्मा थमां सखाई दियें गल्लें च पवित्र आत्मा आत्मिक ज्ञान कन्ने आत्मिक गल्लें गी खोलीखोली दसदा ऐ’,  
‘hi’: ‘जिनको हम मनुष्यों के ज्ञान की सिखाई हुई बातों में नहीं परन्तु पवित्र आत्मा की सिखाई हुई बातों में आत्मा आत्मिक ज्ञान से आत्मिक बातों की व्याख्या करती है’}}

```auto
tokenizer = NllbTokenizerFast.from_pretrained(
    "facebook/nllb-200-distilled-600M", src_lang="hin_Deva", tgt_lang="dog_Deva"
)
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)
training_args = Seq2SeqTrainingArguments(
    output_dir="hi_do_model",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    weight_decay=0.01,
    save_total_limit=3,
    num_train_epochs=1,
    predict_with_generate=True,
    fp16=True,
    push_to_hub=True,
)
trainer = Seq2SeqTrainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_data["train"],
    eval_dataset=tokenized_data["test"],
    tokenizer=tokenizer,
    data_collator=data_collator,
    compute_metrics=compute_metrics,
)
trainer.train()

```

When calling `trainer.train()` getting :

`The following columns in the training set don't have a corresponding argument in `M2M100ForConditionalGeneration.forward` and have been ignored: translation, id. If translation, id are not expected by M2M100ForConditionalGeneration.forward, you can safely ignore this message.`

What this message means? Is the data format is wrong? What data format required for nllb?