To implement cross-lingual transfer learning with mBERT, fine-tune a multilingual BERT checkpoint on labeled examples in a source language, then evaluate the saved model on held-out examples in one or more target languages. Choose the cased or uncased checkpoint for your data, pair its tokenizer with the matching task-specific model, and measure each target language separately; multilingual pretraining does not guarantee equal performance across languages.
What cross-lingual transfer with mBERT means
In this workflow, a model learns a supervised task from labeled data in one language and is tested on data in another. For example, train a sentiment classifier on labeled English reviews and test it on held-out Spanish reviews. The target-language test set must have labels if you want to calculate metrics.
First identify the prediction unit: sequence classification predicts a label for a whole example, such as a review; token classification predicts a label for each token, such as a named entity tag. The task determines both the model head and how labels must be prepared.
Choose an mBERT checkpoint
Hugging Face’s Transformers v4.33.3 multilingual-model guide lists bert-base-multilingual-cased for 104 languages and bert-base-multilingual-uncased for 102. These are documentation-listed coverage counts, not measures of accuracy or a guarantee of equal quality across the languages. The guide says these checkpoints do not require language embeddings at inference and should infer language from context: Hugging Face multilingual models guide.
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When to use cased or uncased
The cased checkpoint retains letter case; the uncased variant does not. Retaining case may help when capitalization carries information, for example in names or acronyms. The available documentation does not establish that either variant is universally better, so compare them on your task’s validation data if the choice matters.
Also inspect how each tokenizer handles representative text in both the source and target languages. A checkpoint’s listed language coverage is not a substitute for checking tokenization or measuring downstream results. If you compare variants, use the same data splits and evaluation procedure.
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Load a matching tokenizer and task head
Load the tokenizer and model from the same checkpoint identifier. For sequence classification, Hugging Face’s Hub example pairs AutoTokenizer with AutoModelForSequenceClassification from the cased mBERT checkpoint. Set the number of output labels to match your task and maintain an explicit mapping between label names and IDs.
from transformers import AutoModelForSequenceClassification, AutoTokenizer
checkpoint = "google-bert/bert-base-multilingual-cased"
label2id = {"negative": 0, "positive": 1}
id2label = {index: label for label, index in label2id.items()}
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(
checkpoint,
num_labels=len(label2id),
label2id=label2id,
id2label=id2label,
)The model-loading pairing is shown in this Hub configuration and usage example: mBERT-based downstream checkpoint configuration and example. For token-level tasks, use a token-classification model head instead. Word labels must be aligned with tokenizer output: a word may split into multiple subword tokens, and special tokens also appear. Decide consistently how to assign or ignore labels for those positions, and evaluate using the same alignment convention.
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Tokenize the data for the task
Apply the same preprocessing at training, validation, target-language evaluation, and inference. Use truncation and choose an explicit maximum sequence length that fits the examples and the selected model’s configuration. For example, one retrieved downstream configuration based on google-bert/bert-base-multilingual-cased records 12 layers, hidden size 768, 12 attention heads, vocabulary size 119547, and a maximum position length of 512. Those values describe that configuration, not every mBERT checkpoint or Transformers release. Inspect the configuration for the exact checkpoint you load rather than assuming 512 applies universally.
encoded = tokenizer(
texts,
truncation=True,
max_length=MAX_LENGTH,
)Replace MAX_LENGTH with a value selected for your task and checkpoint; for batch training, use the padding strategy supported by your chosen data pipeline. Check the resulting examples, especially truncation of long inputs, and verify that sequence labels or token-label alignments remain correct.
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Fine-tune on source-language labels
Split the labeled source-language data into training and validation sets before training. Fine-tune the task-specific model on the training set and use validation results to select settings or checkpoints. Keep target-language test examples out of model selection; otherwise the reported target result is no longer an unbiased final evaluation.
Transformers training APIs and defaults can change. The code above demonstrates checkpoint and task-head loading, not a complete, version-pinned training script. For a runnable training pipeline, consult the current official task guide for your Transformers version, pin Transformers and dependencies, and verify the training arguments, preprocessing, label mapping, evaluation, and save/reload steps against that version.
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Evaluate transfer language by language
Evaluate each target language on its own held-out examples. Report per-language results rather than pooling all languages into a single score that can conceal weak transfer. For classification, include the overall metric appropriate to the task and class-level results when useful; for imbalanced labels, accuracy alone may be misleading. Compare against a meaningful baseline and inspect errors by language, class, and input length.
- Keep the evaluation setup consistent across languages, including label definitions and preprocessing.
- Record each language’s sample size and label distribution alongside its metrics.
- Check whether errors cluster around translation choices, domain differences, spelling, named entities, or tokenization.
- Use validation data for model selection and reserve target-language test data for final reporting.
This evaluation is what establishes whether transfer worked for your language pair and task. The multilingual nature of the checkpoint alone does not establish the result.
Save a reproducible model
Save the fine-tuned model and tokenizer together, and keep the label mapping and preprocessing choices with the experiment. Transformers models and tokenizers provide save_pretrained for writing their artifacts to a directory:
output_dir = "./mb ert-cross-lingual".replace(" ", "")
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)For later inference, load both artifacts from the same saved directory. Record the checkpoint used to initialize training, the library and dependency versions, the maximum sequence length, label mapping, and the evaluation split definitions so another run can reproduce the inputs and interpretation of the output.
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mBERT is an encoder-based starting point for downstream understanding tasks such as sequence or token classification. It is not the same model family as mBART, which Hugging Face documents as a multilingual encoder-decoder model focused on machine translation. For translation or text generation, choose a model and workflow designed for that output task rather than treating a classification head as a generator: Hugging Face mBART documentation.
Quick Recap
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