RobBERTBestModelOct13
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
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
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
Training hyperparameters
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
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Tommert25/RobBERTBestModelOct13")