Instructions to use Lupova/ask_sience_mlm_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lupova/ask_sience_mlm_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Lupova/ask_sience_mlm_model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Lupova/ask_sience_mlm_model") model = AutoModelForMaskedLM.from_pretrained("Lupova/ask_sience_mlm_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
ask_sience_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: 2.2115
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_FUSED 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 |
|---|---|---|---|
| No log | 1.0 | 36 | 2.1924 |
| No log | 2.0 | 72 | 1.9173 |
| No log | 3.0 | 108 | 2.0150 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cpu
- Datasets 4.0.0
- Tokenizers 0.22.1
- Downloads last month
- 10
Model tree for Lupova/ask_sience_mlm_model
Base model
distilbert/distilroberta-base