readme: ---
  language: 
  - multilingual
  - en 
  - de
  ---
  
  # xlm-clm-ende-1024
  
  #  Table of Contents
  
  1. [Model Details](#model-details)
  2. [Uses](#uses)
  3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
  4. [Training](#training)
  5. [Evaluation](#evaluation)
  6. [Environmental Impact](#environmental-impact)
  7. [Technical Specifications](#technical-specifications)
  8. [Citation](#citation)
  9. [Model Card Authors](#model-card-authors)
  10. [How To Get Started With the Model](#how-to-get-started-with-the-model)
  
  
  # Model Details
  
  The XLM model was proposed in [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample, Alexis Conneau. xlm-clm-ende-1024 is a transformer pretrained using a causal language modeling (CLM) objective (next token prediction) for English-German.
  
  ## Model Description
  
  - **Developed by:** Guillaume Lample, Alexis Conneau, see [associated paper](https://arxiv.org/abs/1901.07291)
  - **Model type:** Language model
  - **Language(s) (NLP):** English-German
  - **License:** Unknown
  - **Related Models:** [xlm-clm-enfr-1024](https://huggingface.co/xlm-clm-enfr-1024), [xlm-mlm-ende-1024](https://huggingface.co/xlm-mlm-ende-1024), [xlm-mlm-enfr-1024](https://huggingface.co/xlm-mlm-enfr-1024), [xlm-mlm-enro-1024](https://huggingface.co/xlm-mlm-enro-1024)
  - **Resources for more information:** 
    - [Associated paper](https://arxiv.org/abs/1901.07291)
    - [GitHub Repo](https://github.com/facebookresearch/XLM)
    - [Hugging Face Multilingual Models for Inference docs](https://huggingface.co/docs/transformers/v4.20.1/en/multilingual#xlm-with-language-embeddings)
  
  # Uses
  
  ## Direct Use
  
  The model is a language model. The model can be used for causal language modeling. 
  
  ## Downstream Use
  
  To learn more about this task and potential downstream uses, see the [Hugging Face Multilingual Models for Inference](https://huggingface.co/docs/transformers/v4.20.1/en/multilingual#xlm-with-language-embeddings) docs.
  
  ## 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)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
  
  ## Recommendations
  
  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
  
  # Training
  
  See the [associated paper](https://arxiv.org/pdf/1901.07291.pdf) for details on the training data and training procedure.
   
  # Evaluation
  
  ## Testing Data, Factors & Metrics
  
  See the [associated paper](https://arxiv.org/pdf/1901.07291.pdf) for details on the testing data, factors and metrics.
  
  ## Results 
  
  For xlm-clm-ende-1024 results, see Table 2 of the [associated paper](https://arxiv.org/pdf/1901.07291.pdf).
  
  # Environmental Impact
  
  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
  
  - **Hardware Type:** More information needed
  - **Hours used:** More information needed
  - **Cloud Provider:** More information needed
  - **Compute Region:** More information needed
  - **Carbon Emitted:** More information needed
  
  # Technical Specifications
  
  The model developers write: 
  
  > We implement all our models in PyTorch (Paszke et al., 2017), and train them on 64 Volta GPUs for the language modeling tasks, and 8 GPUs for the MT tasks. We use float16 operations to speed up training and to reduce the memory usage of our models.
  
  See the [associated paper](https://arxiv.org/pdf/1901.07291.pdf) for further details.
  
  # Citation
  
  **BibTeX:**
  
  ```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.
  
  # How to Get Started with the Model
  
  Use the code below to get started with the model.
  
  <details>
  <summary> Click to expand </summary>
  
  ```python
  import torch
  from transformers import XLMTokenizer, XLMWithLMHeadModel
  
  tokenizer = XLMTokenizer.from_pretrained("xlm-clm-ende-1024")
  model = XLMWithLMHeadModel.from_pretrained("xlm-clm-ende-1024")
  
  input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")])  # batch size of 1
  
  language_id = tokenizer.lang2id["en"]  # 0
  langs = torch.tensor([language_id] * input_ids.shape[1])  # torch.tensor([0, 0, 0, ..., 0])
  
  # We reshape it to be of size (batch_size, sequence_length)
  langs = langs.view(1, -1)  # is now of shape [1, sequence_length] (we have a batch size of 1)
  
  outputs = model(input_ids, langs=langs)
  ```
  
  </details>