Xlm Mlm En 2048

🚀 xlm-mlm-en-2048

The XLM model is a transformer pretrained on English text using a masked language modeling objective, offering capabilities for masked language modeling tasks.

🚀 Quick Start

Use the code below to get started with the model. See the Hugging Face XLM docs for more examples.

from transformers import XLMTokenizer, XLMModel
import torch

tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-en-2048")
model = XLMModel.from_pretrained("xlm-mlm-en-2048")

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)

last_hidden_states = outputs.last_hidden_state

✨ Features

• Multilingual Capability: The XLM model can handle multiple languages, with this specific model trained on English text.
• Pretraining Objectives: It can be pretrained with causal language modeling (CLM), masked language modeling (MLM), or Translation Language Modeling (TLM) objectives.

📚 Documentation

Model Details

The XLM model was proposed in Cross-lingual Language Model Pretraining by Guillaume Lample and Alexis Conneau. It’s a transformer pretrained with either a causal language modeling (CLM) objective (next token prediction), a masked language modeling (MLM) objective (BERT-like), or a Translation Language Modeling (TLM) object (extension of BERT’s MLM to multiple language inputs). This model is trained with a masked language modeling objective on English text.

Model Description

Uses

Direct Use

The model is a language model. The model can be used for masked language modeling.

Downstream Use

To learn more about this task and potential downstream uses, see the Hugging Face fill mask docs and the Hugging Face Multilingual Models for Inference docs. Also see the associated paper.

Out-of-Scope Use

The model should not be used to intentionally create hostile or alienating environments for people.

Bias, Risks, and Limitations

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.

Training

More information needed. See the associated GitHub Repo.

Evaluation

More information needed. See the associated GitHub Repo.

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Citation

BibTeX:

@article{lample2019cross,
  title={Cross-lingual language model pretraining},
  author={Lample, Guillaume and Conneau, Alexis},
  journal={arXiv preprint arXiv:1901.07291},
  year={2019}
}

APA:

• Lample, G., & Conneau, A. (2019). Cross-lingual language model pretraining. arXiv preprint arXiv:1901.07291.

Model Card Authors

This model card was written by the team at Hugging Face.