Instructions to use znavidi/ROBERTA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use znavidi/ROBERTA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="znavidi/ROBERTA")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("znavidi/ROBERTA") model = AutoModelForMaskedLM.from_pretrained("znavidi/ROBERTA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
ROBERTA
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 4.3616
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: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 5.9429 | 10.87 | 500 | 5.3878 |
| 4.9002 | 21.74 | 1000 | 5.0254 |
| 4.2192 | 32.61 | 1500 | 4.5682 |
| 3.6352 | 43.48 | 2000 | 4.4108 |
| 3.1499 | 54.35 | 2500 | 4.2052 |
| 2.6935 | 65.22 | 3000 | 4.6229 |
| 2.3329 | 76.09 | 3500 | 4.3204 |
| 1.9895 | 86.96 | 4000 | 4.3616 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
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