| --- |
| license: mit |
| base_model: Tommert25/RobBERTBestModelOct11 |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: RobBERTBestModelOct13 |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # RobBERTBestModelOct13 |
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| This model is a fine-tuned version of pdelobelle/robbert-v2-dutch-base (https://huggingface.co/ pdelobelle/robbert-v2-dutch-base) on the annotated part of the Moroccorp. |
| It achieves the following results on the evaluation set: |
| - eval_loss: 0.3695 |
| - eval_precisions: 0.8647 |
| - eval_recall: 0.8151 |
| - eval_f-measure: 0.8341 |
| - eval_accuracy: 0.9448 |
| - eval_runtime: 9.7585 |
| - eval_samples_per_second: 82.698 |
| - eval_steps_per_second: 5.226 |
| - step: 0 |
|
|
| ## Model description |
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| More information needed |
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| ## Intended uses & limitations |
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| More information needed |
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| ## Training and evaluation data |
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| Both training and evalutation data are sampled from the Moroccorp, a dataset that consists of chat conversation from an internet forum for Moroccan-Dutch people called maroc.nl |
| The dataset is labeled on word-level with labels for the three most common languages in the dataset: Dutch (NL), English (ENG), Moroccan Languages (MOR). |
| Additionally, labels for Named entities (NAME), language independent utterances (NON) and words from other languages (OTH) are used. |
|
|
| ## Training procedure |
| Here is the code to run this model: |
| https://colab.research.google.com/drive/1h_HiQkoo_yALTvHtiWleF9MMvCmPqmXk?usp=sharing |
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| ### Training hyperparameters |
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| The following hyperparameters were used during training: |
| - learning_rate: 7.5e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 14 |
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|
| ### Framework versions |
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| - Transformers 4.34.0 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.14.5 |
| - Tokenizers 0.14.1 |
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