Davlan commited on
Commit
51a0ef3
1 Parent(s): e8207ac

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +2 -2
README.md CHANGED
@@ -33,7 +33,7 @@ datasets:
33
 
34
  # masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0
35
  ## Model description
36
- **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:
37
 
38
  - Amharic (Amharic)
39
  - Bambara (bam)
@@ -106,7 +106,7 @@ avg |**85.1**| **87.7**
106
  #### Limitations and bias
107
  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.
108
  ## Training data
109
- 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
110
 
111
  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:
112
  Abbreviation|Description
 
33
 
34
  # masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0
35
  ## Model description
36
+ **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:
37
 
38
  - Amharic (Amharic)
39
  - Bambara (bam)
 
106
  #### Limitations and bias
107
  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.
108
  ## Training data
109
+ 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
110
 
111
  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:
112
  Abbreviation|Description