README.md

metadata

language:
  - swa
tags:
  - NER
datasets:
  - masakhaner
metrics:
  - f1
  - precision
  - recall
license: apache-2.0
widget:
  - text: >-
      Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa, watu takriban 14 zaidi
      wamepata maambukizi ya Covid-19.
model-index:
  - name: arnolfokam/bert-base-uncased-swa
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: masakhaner
          type: masakhaner
          config: swa
          split: test
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9513694054776219
            verified: true
          - name: Precision
            type: precision
            value: 0.9552572706935123
            verified: true
          - name: Recall
            type: recall
            value: 0.9596836847946726
            verified: true
          - name: F1
            type: f1
            value: 0.9574653618835384
            verified: true
          - name: loss
            type: loss
            value: 0.31934216618537903
            verified: true

Model description

bert-base-uncased-swa is a model based on the fine-tuned BERT base uncased model. It has been trained to recognize four types of entities:

• dates & time (DATE)
• Location (LOC)
• Organizations (ORG)
• Person (PER)

Intended Use

• Intended to be used for research purposes concerning Named Entity Recognition for African Languages.
• Not intended for practical purposes.

Training Data

This model was fine-tuned on the Swahili corpus (swa) of the MasakhaNER dataset. However, we thresholded the number of entity groups per sentence in this dataset to 10 entity groups.

Training procedure

This model was trained on a single NVIDIA P5000 from Paperspace

Hyperparameters

• Learning Rate: 5e-5
• Batch Size: 32
• Maximum Sequence Length: 164
• Epochs: 30

Evaluation Data

We evaluated this model on the test split of the Swahili corpus (swa) present in the MasakhaNER with no thresholding.

Metrics

• Precision
• Recall
• F1-score

Limitations

• The size of the pre-trained language model prevents its usage in anything other than research.
• Lack of analysis concerning the bias and fairness in these models may make them dangerous if deployed into production system.
• The train data is a less populated version of the original dataset in terms of entity groups per sentence. Therefore, this can negatively impact the performance.

Caveats and Recommendations

• The topics in the dataset corpus are centered around News. Future training could be done with a more diverse corpus.

Results

Model Name	Precision	Recall	F1-score
bert-base-uncased-swa	83.38	89.32	86.26

Usage

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("arnolfokam/bert-base-uncased-swa")
model = AutoModelForTokenClassification.from_pretrained("bert-base-uncased-swa")

nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa, watu takriban 14 zaidi wamepata maambukizi ya Covid-19."

ner_results = nlp(example)
print(ner_results)