Instructions to use Atnafu/amharic_xlmr_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Atnafu/amharic_xlmr_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Atnafu/amharic_xlmr_base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Atnafu/amharic_xlmr_base") model = AutoModelForMaskedLM.from_pretrained("Atnafu/amharic_xlmr_base") - Notebooks
- Google Colab
- Kaggle
amh_base
This model is a fine-tuned version of Davlan/afro-xlmr-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 4.3301
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: 5e-05
- train_batch_size: 10
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.13.0
- Tokenizers 0.13.3
- Downloads last month
- 9