Instructions to use CNR-ILC/gs-GreBerta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CNR-ILC/gs-GreBerta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="CNR-ILC/gs-GreBerta")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("CNR-ILC/gs-GreBerta") model = AutoModelForMaskedLM.from_pretrained("CNR-ILC/gs-GreBerta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
gs-GreBerta
This model is a fine-tuned version of bowphs/GreBerta on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.3458
- Top1: 38.0
- Top5: 61.0
- Top10: 69.3333
- Top20: 72.3333
- Bertscore F1 Top1: 86.1297
- Bertscore F1 Top1 Mean: 86.1297
- Bertscore F1 Top5: 92.0578
- Bertscore F1 Top5 Mean: 81.3940
- Bertscore F1 Top10: 94.0554
- Bertscore F1 Top10 Mean: 80.0775
- Bertscore F1 Top20: 94.9076
- Bertscore F1 Top20 Mean: 78.9279
- Cos Sim Top1 Max: 71.9315
- Cos Sim Top1 Mean: 71.9315
- Cos Sim Top5 Max: 81.2391
- Cos Sim Top5 Mean: 64.2925
- Cos Sim Top10 Max: 83.9104
- Cos Sim Top10 Mean: 62.5278
- Cos Sim Top20 Max: 85.4491
- Cos Sim Top20 Mean: 61.1446
- Composite Score: 54.9657
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: 1.22833541147989e-06
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Top1 | Top5 | Top10 | Top20 | Bertscore F1 Top1 | Bertscore F1 Top1 Mean | Bertscore F1 Top5 | Bertscore F1 Top5 Mean | Bertscore F1 Top10 | Bertscore F1 Top10 Mean | Bertscore F1 Top20 | Bertscore F1 Top20 Mean | Cos Sim Top1 Max | Cos Sim Top1 Mean | Cos Sim Top5 Max | Cos Sim Top5 Mean | Cos Sim Top10 Max | Cos Sim Top10 Mean | Cos Sim Top20 Max | Cos Sim Top20 Mean | Composite Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.8080 | 1.0 | 441 | 2.5453 | 36.6667 | 61.0 | 66.6667 | 70.3333 | 85.8308 | 85.8308 | 92.1664 | 81.3928 | 93.7891 | 79.9716 | 94.7290 | 78.8533 | 71.0352 | 71.0352 | 80.7255 | 63.8912 | 83.5151 | 62.0287 | 85.1105 | 60.6243 | 53.8509 |
| 2.5855 | 2.0 | 882 | 2.3822 | 38.3333 | 61.0 | 68.6667 | 72.3333 | 86.0617 | 86.0617 | 92.0687 | 81.4034 | 93.9262 | 80.0525 | 94.9252 | 78.9209 | 71.6309 | 71.6309 | 81.2905 | 64.2855 | 83.8109 | 62.4660 | 85.3562 | 61.1656 | 54.9821 |
| 2.5899 | 3.0 | 1323 | 2.3488 | 38.0 | 61.0 | 69.3333 | 72.3333 | 86.1297 | 86.1297 | 92.0578 | 81.3940 | 94.0554 | 80.0775 | 94.9076 | 78.9279 | 71.9315 | 71.9315 | 81.2391 | 64.2925 | 83.9104 | 62.5278 | 85.4491 | 61.1446 | 54.9657 |
Framework versions
- Transformers 5.9.0
- Pytorch 2.12.0+cu126
- Datasets 3.6.0
- Tokenizers 0.22.2
- Downloads last month
- 17
Model tree for CNR-ILC/gs-GreBerta
Base model
bowphs/GreBerta