Instructions to use Yasmin-0000/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yasmin-0000/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Yasmin-0000/model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Yasmin-0000/model") model = AutoModelForMaskedLM.from_pretrained("Yasmin-0000/model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
model
This model is a fine-tuned version of Twitter/twhin-bert-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.9800
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.4104 | 1.0 | 300 | 2.2115 |
| 2.0795 | 2.0 | 600 | 2.1065 |
| 2.0714 | 3.0 | 900 | 2.0681 |
| 2.0559 | 4.0 | 1200 | 2.0397 |
| 2.1248 | 5.0 | 1500 | 1.9800 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
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Model tree for Yasmin-0000/model
Base model
Twitter/twhin-bert-large