Instructions to use hung200504/bert-18 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hung200504/bert-18 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-18")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hung200504/bert-18") model = AutoModelForQuestionAnswering.from_pretrained("hung200504/bert-18", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from hung200504/bert-18: direct link, hf CLI and curl.
- Browser
- Download file 2.94 kB
-
https://huggingface.co/hung200504/bert-18/resolve/main/README.md
- Command line
-
hf download hf://hung200504/bert-18/README.md
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curl -L -o README.md https://huggingface.co/hung200504/bert-18/resolve/main/README.md
2.94 kB
| license: cc-by-4.0 | |
| base_model: deepset/bert-base-cased-squad2 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: bert-18 | |
| 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-18 | |
| 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: 8.3340 | |
| ## 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: 0.0001 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - 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 | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 11.2713 | 0.09 | 5 | 12.1606 | | |
| | 11.199 | 0.18 | 10 | 11.9115 | | |
| | 10.6074 | 0.27 | 15 | 11.6709 | | |
| | 10.5475 | 0.36 | 20 | 11.4407 | | |
| | 10.3761 | 0.45 | 25 | 11.2173 | | |
| | 10.2166 | 0.55 | 30 | 11.0033 | | |
| | 9.4143 | 0.64 | 35 | 10.7983 | | |
| | 9.8307 | 0.73 | 40 | 10.6034 | | |
| | 9.3026 | 0.82 | 45 | 10.4169 | | |
| | 9.0636 | 0.91 | 50 | 10.2387 | | |
| | 8.7689 | 1.0 | 55 | 10.0700 | | |
| | 8.7969 | 1.09 | 60 | 9.9094 | | |
| | 8.7596 | 1.18 | 65 | 9.7588 | | |
| | 8.8433 | 1.27 | 70 | 9.6152 | | |
| | 8.3576 | 1.36 | 75 | 9.4808 | | |
| | 8.6226 | 1.45 | 80 | 9.3540 | | |
| | 8.3176 | 1.55 | 85 | 9.2346 | | |
| | 8.2174 | 1.64 | 90 | 9.1231 | | |
| | 8.0514 | 1.73 | 95 | 9.0198 | | |
| | 8.0813 | 1.82 | 100 | 8.9240 | | |
| | 7.6971 | 1.91 | 105 | 8.8362 | | |
| | 7.865 | 2.0 | 110 | 8.7562 | | |
| | 7.7614 | 2.09 | 115 | 8.6834 | | |
| | 7.6525 | 2.18 | 120 | 8.6179 | | |
| | 7.7074 | 2.27 | 125 | 8.5593 | | |
| | 7.7802 | 2.36 | 130 | 8.5073 | | |
| | 7.4788 | 2.45 | 135 | 8.4625 | | |
| | 7.6863 | 2.55 | 140 | 8.4245 | | |
| | 7.3113 | 2.64 | 145 | 8.3934 | | |
| | 7.6127 | 2.73 | 150 | 8.3692 | | |
| | 7.471 | 2.82 | 155 | 8.3509 | | |
| | 7.4979 | 2.91 | 160 | 8.3393 | | |
| | 7.5977 | 3.0 | 165 | 8.3340 | | |
| ### Framework versions | |
| - Transformers 4.34.1 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.14.5 | |
| - Tokenizers 0.14.1 | |