Instructions to use helloyj/my_awesome_eli5_mlm_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use helloyj/my_awesome_eli5_mlm_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="helloyj/my_awesome_eli5_mlm_model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("helloyj/my_awesome_eli5_mlm_model") model = AutoModelForMaskedLM.from_pretrained("helloyj/my_awesome_eli5_mlm_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
my_awesome_eli5_mlm_model
This model is a fine-tuned version of distilbert/distilroberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7956
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.112 | 1.0 | 1910 | 0.9421 |
| 0.9256 | 2.0 | 3820 | 0.8281 |
| 0.8601 | 3.0 | 5730 | 0.7916 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.4.1+cu124
- Datasets 4.4.2
- Tokenizers 0.22.2
- Downloads last month
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Model tree for helloyj/my_awesome_eli5_mlm_model
Base model
distilbert/distilroberta-base