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README.md

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+---
+language: 
+- multilingual
+- en 
+- ro
+---
+
+# xlm-mlm-enro-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-mlm-enro-1024 is a transformer pretrained using a masked language modeling (MLM) objective for English-Romanian. This model uses language embeddings to specify the language used at inference. See the [Hugging Face Multilingual Models for Inference docs](https://huggingface.co/docs/transformers/v4.20.1/en/multilingual#xlm-with-language-embeddings) for further details.
+
+## 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-clm-ende-1024](https://huggingface.co/xlm-clm-ende-1024), [xlm-mlm-enfr-1024](https://huggingface.co/xlm-mlm-enfr-1024), [xlm-mlm-ende-1024](https://huggingface.co/xlm-mlm-ende-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 masked language modeling. 
+
+## Downstream Use
+
+To learn more about this task and potential downstream uses, see the Hugging Face [fill mask docs](https://huggingface.co/tasks/fill-mask) and 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
+
+The model developers write: 
+
+> In all experiments, we use a Transformer architecture with 1024 hidden units, 8 heads, GELU activations (Hendrycks and Gimpel, 2016), a dropout rate of 0.1 and learned positional embeddings. We train our models with the Adam op- timizer (Kingma and Ba, 2014), a linear warm- up (Vaswani et al., 2017) and learning rates varying from 10^−4 to 5.10^−4.
+
+See the [associated paper](https://arxiv.org/pdf/1901.07291.pdf) for links, citations, and further details on the training data and training procedure.
+
+The model developers also write that: 
+
+> If you use these models, you should use the same data preprocessing / BPE codes to preprocess your data.
+
+See the associated [GitHub Repo](https://github.com/facebookresearch/XLM#ii-cross-lingual-language-model-pretraining-xlm) for further details.
+ 
+# Evaluation
+
+## Testing Data, Factors & Metrics
+
+The model developers evaluated the model on the [WMT'16 Romanian-English](https://huggingface.co/datasets/wmt16) dataset using the [BLEU metric](https://huggingface.co/spaces/evaluate-metric/bleu). See the [associated paper](https://arxiv.org/pdf/1901.07291.pdf) for further details on the testing data, factors and metrics.
+
+## Results 
+
+For xlm-mlm-enro-1024 results, see Tables 1-3 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
+
+More information needed. This model uses language embeddings to specify the language used at inference. See the [Hugging Face Multilingual Models for Inference docs](https://huggingface.co/docs/transformers/v4.20.1/en/multilingual#xlm-with-language-embeddings) for further details.