Michael Beukman commited on
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3482540
1 Parent(s): eb54b7d

Fixed a typo.

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  1. README.md +2 -2
README.md CHANGED
@@ -21,7 +21,7 @@ More information, and other similar models can be found in the [main Github repo
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  ## About
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  This models is transformer based and was fine-tuned on the MasakhaNER dataset. It is a named entity recognition dataset, containing mostly news articles in 10 different African languages.
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- The model was fine-tuned for 50 epochs, with a maximum sequence length of 200, 32 batch size, 5e-5 learning rate. This process was repeated 5 times (with different random seeds), and this uploaded model performed the best out of those 5 seeds (aggregate F1 on on test set).
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  This model was fine-tuned by me, Michael Beukman while doing a project at the University of the Witwatersrand, Johannesburg. This is version 1, as of 20 November 2021.
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  This models is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
@@ -103,7 +103,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
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  model = AutoModelForTokenClassification.from_pretrained(model_name)
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  nlp = pipeline("ner", model=model, tokenizer=tokenizer)
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- example = "A (Wolof) sentence that may contain entities"
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  ner_results = nlp(example)
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  print(ner_results)
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  ## About
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  This models is transformer based and was fine-tuned on the MasakhaNER dataset. It is a named entity recognition dataset, containing mostly news articles in 10 different African languages.
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+ The model was fine-tuned for 50 epochs, with a maximum sequence length of 200, 32 batch size, 5e-5 learning rate. This process was repeated 5 times (with different random seeds), and this uploaded model performed the best out of those 5 seeds (aggregate F1 on test set).
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  This model was fine-tuned by me, Michael Beukman while doing a project at the University of the Witwatersrand, Johannesburg. This is version 1, as of 20 November 2021.
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  This models is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
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  model = AutoModelForTokenClassification.from_pretrained(model_name)
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  nlp = pipeline("ner", model=model, tokenizer=tokenizer)
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+ example = "SAFIYETU BÉEY Céy Koronaa !"
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  ner_results = nlp(example)
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  print(ner_results)