Instructions to use NasimB/bert-dp-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NasimB/bert-dp-4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NasimB/bert-dp-4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NasimB/bert-dp-4") model = AutoModelForMaskedLM.from_pretrained("NasimB/bert-dp-4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
update model card README.md
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README.md
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This model is a fine-tuned version of [](https://huggingface.co/) on the generator dataset.
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It achieves the following results on the evaluation set:
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- Loss: 5.
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## Model description
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 1000
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- num_epochs:
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- mixed_precision_training: Native AMP
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### Training results
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| 5.2684 | 35.92 | 19000 | 5.6687 |
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| 5.2609 | 37.81 | 20000 | 5.6684 |
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| 5.2606 | 39.7 | 21000 | 5.6719 |
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### Framework versions
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This model is a fine-tuned version of [](https://huggingface.co/) on the generator dataset.
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It achieves the following results on the evaluation set:
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- Loss: 5.6691
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## Model description
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 1000
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- num_epochs: 50
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- mixed_precision_training: Native AMP
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### Training results
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| 5.2684 | 35.92 | 19000 | 5.6687 |
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| 5.2609 | 37.81 | 20000 | 5.6684 |
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| 5.2606 | 39.7 | 21000 | 5.6719 |
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| 5.2624 | 41.59 | 22000 | 5.6697 |
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| 5.2551 | 43.48 | 23000 | 5.6718 |
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| 5.2461 | 45.37 | 24000 | 5.6699 |
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| 5.2431 | 47.26 | 25000 | 5.6692 |
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| 5.2414 | 49.15 | 26000 | 5.6691 |
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### Framework versions
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