XLM

This model was released on 2019-01-22 and added to Hugging Face Transformers on 2020-11-16.

XLM

XLM demonstrates cross-lingual pretraining with two approaches, unsupervised training on a single language and supervised training on more than one language with a cross-lingual language model objective. The XLM model supports the causal language modeling objective, masked language modeling, and translation language modeling (an extension of the BERT) masked language modeling objective to multiple language inputs).

You can find all the original XLM checkpoints under the Facebook AI community organization.

Tip

Click on the XLM models in the right sidebar for more examples of how to apply XLM to different cross-lingual tasks like classification, translation, and question answering.

The example below demonstrates how to predict the <mask> token with Pipeline, AutoModel and from the command line.

import torch
from transformers import pipeline

pipeline = pipeline(
    task="fill-mask",
    model="facebook/xlm-roberta-xl",
    dtype=torch.float16,
    device=0
)
pipeline("Bonjour, je suis un modèle <mask>.")

import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained(
    "FacebookAI/xlm-mlm-en-2048",
)
model = AutoModelForMaskedLM.from_pretrained(
    "FacebookAI/xlm-mlm-en-2048",
    dtype=torch.float16,
    device_map="auto",
)
inputs = tokenizer("Hello, I'm a <mask> model.", return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model(**inputs)
    predictions = outputs.logits.argmax(dim=-1)

predicted_token = tokenizer.decode(predictions[0][inputs["input_ids"][0] == tokenizer.mask_token_id])
print(f"Predicted token: {predicted_token}")

echo -e "Plants create <mask> through a process known as photosynthesis." | transformers run --task fill-mask --model FacebookAI/xlm-mlm-en-2048 --device 0

XLMConfig

[[autodoc]] XLMConfig

XLMTokenizer

[[autodoc]] XLMTokenizer - build_inputs_with_special_tokens - get_special_tokens_mask - create_token_type_ids_from_sequences - save_vocabulary

XLM specific outputs

[[autodoc]] models.xlm.modeling_xlm.XLMForQuestionAnsweringOutput

XLMModel

[[autodoc]] XLMModel - forward

XLMWithLMHeadModel

[[autodoc]] XLMWithLMHeadModel - forward

XLMForSequenceClassification

[[autodoc]] XLMForSequenceClassification - forward

XLMForMultipleChoice

[[autodoc]] XLMForMultipleChoice - forward

XLMForTokenClassification

[[autodoc]] XLMForTokenClassification - forward

XLMForQuestionAnsweringSimple

[[autodoc]] XLMForQuestionAnsweringSimple - forward

XLMForQuestionAnswering

[[autodoc]] XLMForQuestionAnswering - forward