# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("danielcfox/my_awesome_eli5_mlm_model")
model = AutoModelForMaskedLM.from_pretrained("danielcfox/my_awesome_eli5_mlm_model")Quick Links
danielcfox/my_awesome_eli5_mlm_model
This model is a fine-tuned version of distilroberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 1.8774
- Validation Loss: 1.7770
- 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 |
|---|---|---|
| 2.0293 | 1.8310 | 0 |
| 1.9232 | 1.7786 | 1 |
| 1.8774 | 1.7770 | 2 |
Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for danielcfox/my_awesome_eli5_mlm_model
Base model
distilbert/distilroberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="danielcfox/my_awesome_eli5_mlm_model")