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README.md
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- accuracy
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- f1
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---
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Paper: `FonMTL: Toward Building a Multi-Task Learning Model for Fon Language
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- Official Github: https://github.com/bonaventuredossou/multitask_fon
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`MTL Weighted (ours)` | Multi-Task | MasakhaNER 2.0 & MasakhaPOS | All | FON POS | Accuracy | 89.20 |
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`MTL Weighted (ours)` | Multi-Task | MasakhaNER 2.0 & MasakhaPOS | Fon Data | FON POS | Accuracy | 80.85 |
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# Model End-Points
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- [`multitask_model_fon_False_multiplicative.bin`](https://huggingface.co/bonadossou/multitask_model_fon_False_multiplicative) is the MTL Fon Model which has been pre-trained on all MasakhaNER 2.0 and MasakhaPOS datasets, and merging representations in a multiplicative way.
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- [`multitask_model_fon_True_multiplicative.bin`](https://huggingface.co/bonadossou/multitask-learning-fon-true-multiplicative) is the MTL Fon Model which has been pre-trained only on Fon data from the MasakhaNER 2.0 and MasakhaPOS datasets, and merging representations in a multiplicative way.
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# How to run inference when you have the model
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To run inference with the model(s), you can use the [testing block](https://github.com/bonaventuredossou/multitask_fon/blob/main/code/run_train.py#L209) defined in our MultitaskFON class.
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- accuracy
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- f1
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---
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- Paper: [`FonMTL: Toward Building a Multi-Task Learning Model for Fon Language`](https://arxiv.org/abs/2308.14280), accepted at WiNLP co-located at EMNLP 2023
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- Official Github: https://github.com/bonaventuredossou/multitask_fon
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`MTL Weighted (ours)` | Multi-Task | MasakhaNER 2.0 & MasakhaPOS | All | FON POS | Accuracy | 89.20 |
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`MTL Weighted (ours)` | Multi-Task | MasakhaNER 2.0 & MasakhaPOS | Fon Data | FON POS | Accuracy | 80.85 |
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# Importance of Merging Representation Type
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Merging Type | Models | Task | Metric | Metric's Value |
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| :---: | :---: | :---: | :---: | :---: |
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Multiplicative | MTL Weighted (multi-task; ours; *) | NER | F1-Score | **81.92** |
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Multiplicative | MTL Weighted (multi-task; ours; +) | NER | F1-Score | 64.43 |
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| :---: | :---: | :---: | :---: | :---:|
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Multiplicative | MTL Weighted (multi-task; ours; *) | POS | Accuracy | **89.20** |
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Multiplicative & MTL Weighted (multi-task; ours; +) | POS | Accuracy | 80.85 |
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| :---: | :---: | :---: | :---: | :---: |
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Additive | MTL Weighted (multi-task; ours; *) | NER | F1-Score | 78.91 |
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Additive | MTL Weighted (multi-task; ours; +) | NER | F1-Score | 60.93 |
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| :---: | :---: | :---: | :---: | :---: |
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Additive | MTL Weighted (multi-task; ours; *) | POS | Accuracy | 86.99 |
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Additive | MTL Weighted (multi-task; ours; +) | POS | Accuracy | 78.25 |
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# Model End-Points
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- [`multitask_model_fon_False_multiplicative.bin`](https://huggingface.co/bonadossou/multitask_model_fon_False_multiplicative) is the MTL Fon Model which has been pre-trained on all MasakhaNER 2.0 and MasakhaPOS datasets, and merging representations in a multiplicative way.
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- [`multitask_model_fon_True_multiplicative.bin`](https://huggingface.co/bonadossou/multitask-learning-fon-true-multiplicative) is the MTL Fon Model which has been pre-trained only on Fon data from the MasakhaNER 2.0 and MasakhaPOS datasets, and merging representations in a multiplicative way.
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# How to run inference when you have the model
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To run inference with the model(s), you can use the [testing block](https://github.com/bonaventuredossou/multitask_fon/blob/main/code/run_train.py#L209) defined in our MultitaskFON class.
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# TODO
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- leverage the impact of `the dynamic weighted average loss`
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