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README.md
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# masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0
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## Model description
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**masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0** is a **Named Entity Recognition (NER)** model for 21 African languages. Specifically, this model is a *Davlan/afro-xlmr-large* model that was fine-tuned on an aggregation of African language datasets obtained from two versions of MasakhaNER dataset i.e. [MasakhaNER 1.0](https://huggingface.co/datasets/masakhaner) and [MasakhaNER 2.0](https://huggingface.co/datasets/masakhane/masakhaner2). The languages covered are:
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- Amharic (Amharic)
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- Bambara (bam)
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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 the aggregation of [MasakhaNER 1.0](https://huggingface.co/datasets/masakhaner) and [MasakhaNER 2.0](https://huggingface.co/datasets/masakhane/masakhaner2) datasets
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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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# masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0
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## Model description
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**masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0** is a **Named Entity Recognition (NER)** model for 21 African languages. Specifically, this model is a *Davlan/afro-xlmr-large* model that was fine-tuned on an aggregation of African language datasets obtained from two versions of MasakhaNER dataset i.e. [MasakhaNER 1.0](https://huggingface.co/datasets/masakhaner) and [MasakhaNER 2.0](https://huggingface.co/datasets/masakhane/masakhaner2.0). The languages covered are:
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- Amharic (Amharic)
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- Bambara (bam)
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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 the aggregation of [MasakhaNER 1.0](https://huggingface.co/datasets/masakhaner) and [MasakhaNER 2.0](https://huggingface.co/datasets/masakhane/masakhaner2.0) datasets
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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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