readme: ---
  language: 
  - multilingual
  - af
  - am
  - ar
  - as
  - az
  - be
  - bg
  - bn
  - br
  - bs
  - ca
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  - yi
  - zh
  ---
  
  # xlm-roberta-large-finetuned-conll02-spanish
  
  #  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
  
  ## Model Description
  
  The XLM-RoBERTa model was proposed in [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data. This model is [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large) fine-tuned with the [CoNLL-2002](https://huggingface.co/datasets/conll2002) dataset in Spanish.
  
  - **Developed by:** See [associated paper](https://arxiv.org/abs/1911.02116)
  - **Model type:** Multi-lingual language model
  - **Language(s) (NLP):** XLM-RoBERTa is a multilingual model trained on 100 different languages; see [GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/xlmr) for full list; model is fine-tuned on a dataset in Spanish.
  - **License:** More information needed
  - **Related Models:** [RoBERTa](https://huggingface.co/roberta-base), [XLM](https://huggingface.co/docs/transformers/model_doc/xlm)
      - **Parent Model:** [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large)
  - **Resources for more information:** 
    -[GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/xlmr)
    -[Associated Paper](https://arxiv.org/abs/1911.02116)
    -[CoNLL-2002 data card](https://huggingface.co/datasets/conll2002)
  
  # Uses
  
  ## Direct Use
  
  The model is a language model. The model can be used for token classification, a natural language understanding task in which a label is assigned to some tokens in a text. 
  
  ## Downstream Use
  
  Potential downstream use cases include Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging. To learn more about token classification and other potential downstream use cases, see the Hugging Face [token classification docs](https://huggingface.co/tasks/token-classification).
  
  ## Out-of-Scope Use
  
  The model should not be used to intentionally create hostile or alienating environments for people. 
  
  # Bias, Risks, and Limitations
  
  **CONTENT WARNING: Readers should be made aware that language generated by this model may be disturbing or offensive to some and may propagate historical and current stereotypes.**
  
  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 following resources for training data and training procedure details: 
  - [XLM-RoBERTa-large model card](https://huggingface.co/xlm-roberta-large)
  - [CoNLL-2002 data card](https://huggingface.co/datasets/conll2002)
  - [Associated paper](https://arxiv.org/pdf/1911.02116.pdf)
   
  # Evaluation
  
  See the [associated paper](https://arxiv.org/pdf/1911.02116.pdf) for evaluation details.
  
  # 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:** 500 32GB Nvidia V100 GPUs (from the [associated paper](https://arxiv.org/pdf/1911.02116.pdf))
  - **Hours used:** More information needed
  - **Cloud Provider:** More information needed
  - **Compute Region:** More information needed
  - **Carbon Emitted:** More information needed
  
  # Technical Specifications
  
  See the [associated paper](https://arxiv.org/pdf/1911.02116.pdf) for further details.
  
  # Citation
  
  **BibTeX:**
  
  ```bibtex
  @article{conneau2019unsupervised,
    title={Unsupervised Cross-lingual Representation Learning at Scale},
    author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, 