Initial model card

README.md

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+---
+language: 
+- multilingual
+- en 
+- fr
+---
+
+# xlm-clm-enfr-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-enfr-1024 is a transformer pretrained using a causal language modeling (CLM) objective (next token prediction) for English-French.
+
+## 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-French
+- **License:** Unknown
+- **Related Models:** [xlm-clm-ende-1024](https://huggingface.co/xlm-clm-ende-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 (next token prediction). 
+
+## 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-enfr-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-enfr-1024")
+model = XLMWithLMHeadModel.from_pretrained("xlm-clm-enfr-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>