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# xlm-roberta-large-masakhaner
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## Model description
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**xlm-roberta-large-masakhaner** is the first **Named Entity Recognition** model for 10 African languages (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) based on a fine-tuned XLM-RoBERTa large model. It achieves the **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER).
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Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset.
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## Intended uses & limitations
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#### How to use
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You can use this model with Transformers *pipeline* for NER.
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#### Limitations and bias
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This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
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## Training data
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This model was fine-tuned on 10 African NER datasets (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) Masakhane[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset
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The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
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Abbreviation|Description
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# xlm-roberta-large-masakhaner
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## Model description
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**xlm-roberta-large-masakhaner** is the first **Named Entity Recognition** model for 10 African languages (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) based on a fine-tuned XLM-RoBERTa large model. It achieves the **state-of-the-art performance** for the NER task. It has been trained to recognize four types of entities: dates & times (DATE), location (LOC), organizations (ORG), and person (PER).
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Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of African language datasets obtained from Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset.
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## Intended uses & limitations
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#### How to use
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You can use this model with Transformers *pipeline* for NER.
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#### Limitations and bias
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This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
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## Training data
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This model was fine-tuned on 10 African NER datasets (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahilu, Wolof, and Yorùbá) Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset
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The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
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Abbreviation|Description
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