Instructions to use RogerB/deberta-base-finetuned-kintweetsE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RogerB/deberta-base-finetuned-kintweetsE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="RogerB/deberta-base-finetuned-kintweetsE")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("RogerB/deberta-base-finetuned-kintweetsE") model = AutoModelForMaskedLM.from_pretrained("RogerB/deberta-base-finetuned-kintweetsE") - Notebooks
- Google Colab
- Kaggle
deberta-base-finetuned-kintweetsE
This model is a fine-tuned version of microsoft/deberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.4010
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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.7396 | 1.0 | 1000 | 3.9160 |
| 3.7652 | 2.0 | 2000 | 3.4778 |
| 3.5318 | 3.0 | 3000 | 3.3727 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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