# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Falah/Mask_awesome_eli5_mlm_model")
model = AutoModelForMaskedLM.from_pretrained("Falah/Mask_awesome_eli5_mlm_model")Quick Links
Mask_awesome_eli5_mlm_model
This model is a fine-tuned version of distilroberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.9796
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.7361 | 1.0 | 1131 | 2.0661 |
| 1.8475 | 2.0 | 2262 | 2.0314 |
| 1.983 | 3.0 | 3393 | 2.0085 |
| 2.0677 | 4.0 | 4524 | 1.9931 |
Framework versions
- Transformers 4.27.1
- Pytorch 2.0.1+cu118
- Datasets 2.9.0
- 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="Falah/Mask_awesome_eli5_mlm_model")