masakhane
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afroxlmr-large-ner-masakhaner-1.0_2.0

Token Classification

Inference Endpoints

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Davlan commited on Dec 15, 2022

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@@ -75,7 +75,7 @@ print(ner_results)
 #### Limitations and bias
 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.
 ## Training data
-This model was fine-tuned on 10 African NER datasets (Amharic, Hausa, Igbo, Kinyarwanda, Luganda, Nigerian Pidgin, Swahili, Wolof, and Yorùbá) Masakhane [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset
 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:
 Abbreviation|Description

 #### Limitations and bias
 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.
 ## Training data
+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
 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:
 Abbreviation|Description