# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("mfigurski80/relation-distilbert-inv")
model = AutoModelForMaskedLM.from_pretrained("mfigurski80/relation-distilbert-inv")Quick Links
relation-distilbert
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6465
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.6363 | 1.0 | 2812 | 0.6465 |
| 0.6361 | 2.0 | 5624 | 0.6465 |
| 0.6358 | 3.0 | 8436 | 0.6465 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mfigurski80/relation-distilbert-inv")