Instructions to use aleksahet/divine-tree-92 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aleksahet/divine-tree-92 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="aleksahet/divine-tree-92")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("aleksahet/divine-tree-92") model = AutoModelForMaskedLM.from_pretrained("aleksahet/divine-tree-92") - Notebooks
- Google Colab
- Kaggle
divine-tree-92
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 5.6984
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: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|---|---|---|---|
| 5.7214 | 0.76 | 10000 | 5.7051 |
| 5.7029 | 1.53 | 20000 | 5.7056 |
| 5.7072 | 2.29 | 30000 | 5.7014 |
| 5.6749 | 3.05 | 40000 | 5.6977 |
| 5.7092 | 3.82 | 50000 | 5.6983 |
Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.14.5
- Tokenizers 0.13.3
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