Instructions to use Billwzl/20split_dataset_version2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Billwzl/20split_dataset_version2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Billwzl/20split_dataset_version2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Billwzl/20split_dataset_version2") model = AutoModelForMaskedLM.from_pretrained("Billwzl/20split_dataset_version2", 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 [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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### Framework versions
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- Transformers 4.20.1
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- Pytorch 1.12.0+cu113
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- Datasets 2.
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- Tokenizers 0.12.1
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.0626
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| 2.7621 | 1.0 | 11851 | 2.5216 |
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| 2.5466 | 2.0 | 23702 | 2.4157 |
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| 2.4505 | 3.0 | 35553 | 2.3592 |
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| 2.3798 | 4.0 | 47404 | 2.3028 |
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| 2.3178 | 5.0 | 59255 | 2.2768 |
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| 2.272 | 6.0 | 71106 | 2.2366 |
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| 2.2323 | 7.0 | 82957 | 2.2128 |
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| 2.1928 | 8.0 | 94808 | 2.1797 |
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| 2.157 | 9.0 | 106659 | 2.1667 |
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| 2.1292 | 10.0 | 118510 | 2.1392 |
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| 2.0978 | 11.0 | 130361 | 2.1280 |
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| 2.0725 | 12.0 | 142212 | 2.1106 |
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| 2.052 | 13.0 | 154063 | 2.0944 |
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| 2.0268 | 14.0 | 165914 | 2.0804 |
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| 2.0121 | 15.0 | 177765 | 2.0698 |
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| 1.9997 | 16.0 | 189616 | 2.0626 |
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### Framework versions
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- Transformers 4.20.1
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- Pytorch 1.12.0+cu113
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- Datasets 2.4.0
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- Tokenizers 0.12.1
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