# Load model directly
from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained("Harshatheeswar/Ltg_bert", trust_remote_code=True, dtype="auto")Quick Links
Ltg_bert
This model is a fine-tuned version of babylm/ltgbert-10m-2024 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0000
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0001 | 0.9997 | 2779 | 0.0000 |
| 0.0 | 1.9997 | 5559 | 0.0000 |
| 0.0 | 2.9998 | 8339 | 0.0000 |
| 0.0 | 3.9998 | 11119 | 0.0000 |
| 0.0 | 4.9984 | 13895 | 0.0000 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for Harshatheeswar/Ltg_bert
Base model
babylm/ltgbert-10m-2024
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Harshatheeswar/Ltg_bert", trust_remote_code=True)