Instructions to use Pannathorn/my_nt_MLM2_EngV with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pannathorn/my_nt_MLM2_EngV with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Pannathorn/my_nt_MLM2_EngV")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Pannathorn/my_nt_MLM2_EngV") model = AutoModelForMaskedLM.from_pretrained("Pannathorn/my_nt_MLM2_EngV", device_map="auto") - Notebooks
- Google Colab
- Kaggle
my_nt_MLM2_EngV
This model is a fine-tuned version of Pannathorn/fine-tune_eli5_mlm_model on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.4173
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: 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 15 | 2.7032 |
| No log | 2.0 | 30 | 2.3360 |
| No log | 3.0 | 45 | 2.3399 |
| 2.5406 | 4.0 | 60 | 2.3121 |
| 2.5406 | 5.0 | 75 | 2.4076 |
| 2.5406 | 6.0 | 90 | 2.1131 |
| 2.3199 | 7.0 | 105 | 2.4668 |
| 2.3199 | 8.0 | 120 | 2.3048 |
| 2.3199 | 9.0 | 135 | 2.4295 |
| 2.2345 | 10.0 | 150 | 2.2955 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
- Downloads last month
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Model tree for Pannathorn/my_nt_MLM2_EngV
Base model
distilbert/distilroberta-base Finetuned
Pannathorn/fine-tune_eli5_mlm_model