# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("juancavallotti/roberta-base-culinary")
model = AutoModelForMaskedLM.from_pretrained("juancavallotti/roberta-base-culinary")Quick Links
roberta-base-culinary
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1032
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: 16
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.5135 | 1.0 | 39823 | 1.4635 |
| 1.454 | 2.0 | 79646 | 1.3753 |
| 1.3924 | 3.0 | 119469 | 1.3375 |
| 1.3379 | 4.0 | 159292 | 1.2886 |
| 1.2969 | 5.0 | 199115 | 1.2595 |
| 1.2495 | 6.0 | 238938 | nan |
| 1.1768 | 7.0 | 278761 | 1.2283 |
| 1.1687 | 8.0 | 318584 | 1.2109 |
| 1.2148 | 9.0 | 358407 | 1.1671 |
| 1.133 | 10.0 | 398230 | 1.1721 |
| 1.0882 | 11.0 | 438053 | 1.1624 |
| 1.0749 | 12.0 | 477876 | 1.1321 |
| 1.092 | 13.0 | 517699 | nan |
| 1.0594 | 14.0 | 557522 | 1.1186 |
| 1.0292 | 15.0 | 597345 | 1.1074 |
| 0.9973 | 16.0 | 637168 | 1.1032 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="juancavallotti/roberta-base-culinary")