Instructions to use hung200504/bert-10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hung200504/bert-10 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="hung200504/bert-10")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hung200504/bert-10") model = AutoModelForQuestionAnswering.from_pretrained("hung200504/bert-10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from hung200504/bert-10: direct link, hf CLI and curl.
- Browser
- Download file 4.57 kB
-
https://huggingface.co/hung200504/bert-10/resolve/main/README.md
- Command line
-
hf download hf://hung200504/bert-10/README.md
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curl -L -o README.md https://huggingface.co/hung200504/bert-10/resolve/main/README.md
4.57 kB
| license: cc-by-4.0 | |
| base_model: deepset/bert-base-cased-squad2 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: bert-10 | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # bert-10 | |
| This model is a fine-tuned version of [deepset/bert-base-cased-squad2](https://huggingface.co/deepset/bert-base-cased-squad2) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 9.5797 | |
| ## 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-07 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 10.8556 | 0.05 | 5 | 12.3235 | | |
| | 10.8413 | 0.09 | 10 | 12.2591 | | |
| | 11.0649 | 0.14 | 15 | 12.1778 | | |
| | 11.6408 | 0.18 | 20 | 12.0989 | | |
| | 11.3732 | 0.23 | 25 | 12.0213 | | |
| | 10.5122 | 0.28 | 30 | 11.9458 | | |
| | 10.6594 | 0.32 | 35 | 11.8691 | | |
| | 10.745 | 0.37 | 40 | 11.7928 | | |
| | 10.8256 | 0.41 | 45 | 11.7163 | | |
| | 10.1627 | 0.46 | 50 | 11.6430 | | |
| | 10.9907 | 0.5 | 55 | 11.5703 | | |
| | 10.1394 | 0.55 | 60 | 11.4997 | | |
| | 9.6059 | 0.6 | 65 | 11.4287 | | |
| | 9.4972 | 0.64 | 70 | 11.3621 | | |
| | 10.2252 | 0.69 | 75 | 11.2949 | | |
| | 10.4887 | 0.73 | 80 | 11.2288 | | |
| | 9.9616 | 0.78 | 85 | 11.1638 | | |
| | 9.5775 | 0.83 | 90 | 11.1003 | | |
| | 9.5971 | 0.87 | 95 | 11.0381 | | |
| | 9.5745 | 0.92 | 100 | 10.9773 | | |
| | 9.3218 | 0.96 | 105 | 10.9178 | | |
| | 9.4906 | 1.01 | 110 | 10.8597 | | |
| | 9.1168 | 1.06 | 115 | 10.8030 | | |
| | 9.8009 | 1.1 | 120 | 10.7465 | | |
| | 9.3632 | 1.15 | 125 | 10.6915 | | |
| | 8.9858 | 1.19 | 130 | 10.6399 | | |
| | 9.2904 | 1.24 | 135 | 10.5874 | | |
| | 9.5344 | 1.28 | 140 | 10.5370 | | |
| | 9.0034 | 1.33 | 145 | 10.4871 | | |
| | 9.3024 | 1.38 | 150 | 10.4384 | | |
| | 8.7905 | 1.42 | 155 | 10.3920 | | |
| | 8.9329 | 1.47 | 160 | 10.3465 | | |
| | 8.9834 | 1.51 | 165 | 10.3027 | | |
| | 8.7307 | 1.56 | 170 | 10.2607 | | |
| | 8.6729 | 1.61 | 175 | 10.2200 | | |
| | 9.1849 | 1.65 | 180 | 10.1794 | | |
| | 9.1618 | 1.7 | 185 | 10.1400 | | |
| | 8.9048 | 1.74 | 190 | 10.1023 | | |
| | 8.9427 | 1.79 | 195 | 10.0655 | | |
| | 9.1052 | 1.83 | 200 | 10.0294 | | |
| | 9.1123 | 1.88 | 205 | 9.9938 | | |
| | 9.0476 | 1.93 | 210 | 9.9604 | | |
| | 8.5532 | 1.97 | 215 | 9.9285 | | |
| | 8.7871 | 2.02 | 220 | 9.8977 | | |
| | 8.5984 | 2.06 | 225 | 9.8690 | | |
| | 8.7009 | 2.11 | 230 | 9.8414 | | |
| | 8.9376 | 2.16 | 235 | 9.8146 | | |
| | 8.3535 | 2.2 | 240 | 9.7906 | | |
| | 8.5805 | 2.25 | 245 | 9.7675 | | |
| | 8.4641 | 2.29 | 250 | 9.7463 | | |
| | 8.3975 | 2.34 | 255 | 9.7263 | | |
| | 8.7698 | 2.39 | 260 | 9.7070 | | |
| | 8.3541 | 2.43 | 265 | 9.6901 | | |
| | 8.5443 | 2.48 | 270 | 9.6743 | | |
| | 8.1539 | 2.52 | 275 | 9.6595 | | |
| | 7.9856 | 2.57 | 280 | 9.6459 | | |
| | 8.2532 | 2.61 | 285 | 9.6333 | | |
| | 8.2116 | 2.66 | 290 | 9.6221 | | |
| | 8.9557 | 2.71 | 295 | 9.6119 | | |
| | 8.0754 | 2.75 | 300 | 9.6032 | | |
| | 7.9534 | 2.8 | 305 | 9.5956 | | |
| | 8.5578 | 2.84 | 310 | 9.5899 | | |
| | 8.6403 | 2.89 | 315 | 9.5848 | | |
| | 8.1103 | 2.94 | 320 | 9.5817 | | |
| | 8.3785 | 2.98 | 325 | 9.5797 | | |
| ### Framework versions | |
| - Transformers 4.34.0 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.14.5 | |
| - Tokenizers 0.14.1 | |