# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("dot-ammar/dotless_mask_model-small")
model = AutoModelForMaskedLM.from_pretrained("dot-ammar/dotless_mask_model-small")Quick Links
dot-ammar/dotless_mask_model-small
This model is a fine-tuned version of distilbert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 5.7096
- Validation Loss: 5.4659
- Epoch: 2
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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 6.9175 | 6.2919 | 0 |
| 6.0859 | 5.7798 | 1 |
| 5.7096 | 5.4659 | 2 |
Framework versions
- Transformers 4.33.0
- TensorFlow 2.12.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dot-ammar/dotless_mask_model-small")