Michael Beukman
commited on
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Parent(s):
0fe7618
Fixed a typo.
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README.md
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@@ -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
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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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@@ -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 = "
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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 = "Kò sí ẹ̀rí tí ó fi ẹsẹ̀ rinlẹ̀ ."
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ner_results = nlp(example)
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print(ner_results)
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