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Fixed some typos.

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@@ -20,11 +20,11 @@ This is a token classification (specifically NER) model that fine-tuned [xlm-rob
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  More information, and other similar models can be found in the [main Github repository](https://github.com/Michael-Beukman/NERTransfer).
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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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  ### Contact & More information
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  For more information about the models, including training scripts, detailed results and further resources, you can visit the the [main Github repository](https://github.com/Michael-Beukman/NERTransfer). You can contact me by filing an issue on this repository.
@@ -35,7 +35,7 @@ In the interest of openness, and reporting resources used, we list here how long
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  ## Data
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  The train, evaluation and test datasets were taken directly from the MasakhaNER [Github](https://github.com/masakhane-io/masakhane-ner) repository, with minimal to no preprocessing, as the original dataset is already of high quality.
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- The motivation for the use of this data is that it is the "first large, publicly available, high­ quality dataset for named entity recognition (NER) in ten African languages" ([source](https://arxiv.org/pdf/2103.11811.pdf)). The high-quality data, as well as the groundwork laid by the paper introducing it are some more reasons why this dataset was used. For evaluation, the dedicated test split was used, which is from the same distribution as the training data, so the models may not generalise to other distributions, and further testing would need to be done to investigate this. The exact distribution of the data is covered in detail [here](https://arxiv.org/abs/2103.11811).
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  ## Intended Use
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  This model are intended to be used for NLP research into e.g. interpretability or transfer learning. Using this model in production is not supported, as generalisability and downright performance is limited. In particular, this is not designed to be used in any important downstream task that could affect people, as harm could be caused by the limitations of the model, described next.
@@ -54,12 +54,12 @@ Additionally, this model has not been verified in practice, and other, more subt
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  ### Privacy & Ethical Considerations
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  The data comes from only publicly available news sources, the only available data should cover public figures and those that agreed to be reported on. See the original MasakhaNER paper for more details.
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- No explicit ethical considerations or adjustments were made during fine-tuning of these models.
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  ## Metrics
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  The language adaptive models achieve (mostly) superior performance over starting with xlm-roberta-base. Our main metric was the aggregate F1 score for all NER categories.
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- These metrics are on the test set for MasakhaNER, so the data distribution is similar to the training set, so these results do not directly indicate how well models generalise.
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  We do find large variation in transfer results when starting from different seeds (5 different seeds were tested), indicating that the fine-tuning process for transfer might be unstable.
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  The metrics used were chosen to be consistent with previous work, and to facilitate research. Other metrics may be more appropriate for other purposes.
@@ -70,7 +70,7 @@ In general, this model performed worse on the 'date' category compared to others
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  Here are some performance details on this specific model, compared to others we trained.
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  All of these metrics were calculated on the test set, and the seed was chosen that gave the best overall F1 score. The first three result columns are averaged over all categories, and the latter 4 provide performance broken down by category.
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- These models can predict the following label for a token ([source](https://huggingface.co/Davlan/xlm-roberta-large-masakhaner)):
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  Abbreviation|Description
@@ -93,7 +93,7 @@ I-LOC |Location
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  | [xlm-roberta-base-finetuned-swahili-finetuned-ner-wolof](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-wolof) | [swa](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) | wol | 69.01 | 73.25 | 65.23 | 27.00 | 85.00 | 52.00 | 67.00 |
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  | [xlm-roberta-base-finetuned-ner-wolof](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-ner-wolof) | [base](https://huggingface.co/xlm-roberta-base) | wol | 66.12 | 69.46 | 63.09 | 30.00 | 84.00 | 54.00 | 59.00 |
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  ## Usage
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- To use these models, you can do the following, with just changing the model name ([source](https://huggingface.co/dslim/bert-base-NER)):
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  ```
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  from transformers import AutoTokenizer, AutoModelForTokenClassification
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  More information, and other similar models can be found in the [main Github repository](https://github.com/Michael-Beukman/NERTransfer).
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  ## About
23
+ This model 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.
24
  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 model is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
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  ### Contact & More information
30
  For more information about the models, including training scripts, detailed results and further resources, you can visit the the [main Github repository](https://github.com/Michael-Beukman/NERTransfer). You can contact me by filing an issue on this repository.
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  ## Data
37
  The train, evaluation and test datasets were taken directly from the MasakhaNER [Github](https://github.com/masakhane-io/masakhane-ner) repository, with minimal to no preprocessing, as the original dataset is already of high quality.
38
+ The motivation for the use of this data is that it is the "first large, publicly available, high­ quality dataset for named entity recognition (NER) in ten African languages" ([source](https://arxiv.org/pdf/2103.11811.pdf)). The high-quality data, as well as the groundwork laid by the paper introducing it are some more reasons why this dataset was used. For evaluation, the dedicated test split was used, which is from the same distribution as the training data, so this model may not generalise to other distributions, and further testing would need to be done to investigate this. The exact distribution of the data is covered in detail [here](https://arxiv.org/abs/2103.11811).
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  ## Intended Use
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  This model are intended to be used for NLP research into e.g. interpretability or transfer learning. Using this model in production is not supported, as generalisability and downright performance is limited. In particular, this is not designed to be used in any important downstream task that could affect people, as harm could be caused by the limitations of the model, described next.
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  ### Privacy & Ethical Considerations
55
  The data comes from only publicly available news sources, the only available data should cover public figures and those that agreed to be reported on. See the original MasakhaNER paper for more details.
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+ No explicit ethical considerations or adjustments were made during fine-tuning of this model.
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  ## Metrics
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  The language adaptive models achieve (mostly) superior performance over starting with xlm-roberta-base. Our main metric was the aggregate F1 score for all NER categories.
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+ These metrics are on the test set for MasakhaNER, so the data distribution is similar to the training set, so these results do not directly indicate how well these models generalise.
63
  We do find large variation in transfer results when starting from different seeds (5 different seeds were tested), indicating that the fine-tuning process for transfer might be unstable.
64
 
65
  The metrics used were chosen to be consistent with previous work, and to facilitate research. Other metrics may be more appropriate for other purposes.
70
  Here are some performance details on this specific model, compared to others we trained.
71
  All of these metrics were calculated on the test set, and the seed was chosen that gave the best overall F1 score. The first three result columns are averaged over all categories, and the latter 4 provide performance broken down by category.
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+ This model can predict the following label for a token ([source](https://huggingface.co/Davlan/xlm-roberta-large-masakhaner)):
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  Abbreviation|Description
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  | [xlm-roberta-base-finetuned-swahili-finetuned-ner-wolof](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-wolof) | [swa](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) | wol | 69.01 | 73.25 | 65.23 | 27.00 | 85.00 | 52.00 | 67.00 |
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  | [xlm-roberta-base-finetuned-ner-wolof](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-ner-wolof) | [base](https://huggingface.co/xlm-roberta-base) | wol | 66.12 | 69.46 | 63.09 | 30.00 | 84.00 | 54.00 | 59.00 |
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  ## Usage
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+ To use this model (or others), you can do the following, just changing the model name ([source](https://huggingface.co/dslim/bert-base-NER)):
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  ```
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  from transformers import AutoTokenizer, AutoModelForTokenClassification