Datasets:
Tasks:
Fill-Mask
Formats:
csv
Sub-tasks:
masked-language-modeling
Size:
1M - 10M
ArXiv:
Tags:
afrolm
active learning
language modeling
research papers
natural language processing
self-active learning
License:
Commit
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daaa87d
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Parent(s):
b25c891
Update README.md
Browse files
README.md
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@@ -84,6 +84,8 @@ tokenizer = XLMRobertaTokenizer.from_pretrained("bonadossou/afrolm_active_learni
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tokenizer.model_max_length = 256
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```
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## Reproducing our result: Training and Evaluation
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- To train the network, run `python active_learning.py`. You can also wrap it around a `bash` script.
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tokenizer.model_max_length = 256
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```
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`Autotokenizer` class does not successfully load our tokenizer. So we recommend to use directly the `XLMRobertaTokenizer` class. Depending on your task, you will load the according mode of the model. Read the [XLMRoberta Documentation](https://huggingface.co/docs/transformers/model_doc/xlm-roberta)
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## Reproducing our result: Training and Evaluation
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- To train the network, run `python active_learning.py`. You can also wrap it around a `bash` script.
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