Instructions to use RogerB/KinyaBERT-small-finetuned-kintweets with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RogerB/KinyaBERT-small-finetuned-kintweets with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="RogerB/KinyaBERT-small-finetuned-kintweets")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("RogerB/KinyaBERT-small-finetuned-kintweets") model = AutoModelForMaskedLM.from_pretrained("RogerB/KinyaBERT-small-finetuned-kintweets") - Notebooks
- Google Colab
- Kaggle
KinyaBERT-small-finetuned-kintweets
This model is a fine-tuned version of jean-paul/KinyaBERT-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 4.3637
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.4855 | 1.0 | 90 | 4.2106 |
| 4.1658 | 2.0 | 180 | 4.1444 |
| 4.0402 | 3.0 | 270 | 4.1616 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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