Instructions to use datarpit/toy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use datarpit/toy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="datarpit/toy")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("datarpit/toy") model = AutoModelForQuestionAnswering.from_pretrained("datarpit/toy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: toy | |
| 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. --> | |
| # toy | |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2124 | |
| ## 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-05 | |
| - 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: 50 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:-----:|:---------------:| | |
| | 0.4798 | 1.0 | 231 | 0.2252 | | |
| | 0.3378 | 2.0 | 462 | 0.1777 | | |
| | 0.1024 | 3.0 | 693 | 0.1586 | | |
| | 0.0736 | 4.0 | 924 | 0.1664 | | |
| | 0.1237 | 5.0 | 1155 | 0.1692 | | |
| | 0.1049 | 6.0 | 1386 | 0.1818 | | |
| | 0.0239 | 7.0 | 1617 | 0.2127 | | |
| | 0.0036 | 8.0 | 1848 | 0.1888 | | |
| | 0.0051 | 9.0 | 2079 | 0.2061 | | |
| | 0.0003 | 10.0 | 2310 | 0.1905 | | |
| | 0.0005 | 11.0 | 2541 | 0.2011 | | |
| | 0.0003 | 12.0 | 2772 | 0.1928 | | |
| | 0.0029 | 13.0 | 3003 | 0.2563 | | |
| | 0.0002 | 14.0 | 3234 | 0.2076 | | |
| | 0.0002 | 15.0 | 3465 | 0.1980 | | |
| | 0.0001 | 16.0 | 3696 | 0.2013 | | |
| | 0.0001 | 17.0 | 3927 | 0.2089 | | |
| | 0.0001 | 18.0 | 4158 | 0.1984 | | |
| | 0.0001 | 19.0 | 4389 | 0.2017 | | |
| | 0.0001 | 20.0 | 4620 | 0.2013 | | |
| | 0.0001 | 21.0 | 4851 | 0.2142 | | |
| | 0.0001 | 22.0 | 5082 | 0.1943 | | |
| | 0.0001 | 23.0 | 5313 | 0.2003 | | |
| | 0.0 | 24.0 | 5544 | 0.2015 | | |
| | 0.0001 | 25.0 | 5775 | 0.2031 | | |
| | 0.0002 | 26.0 | 6006 | 0.2600 | | |
| | 0.0022 | 27.0 | 6237 | 0.2269 | | |
| | 0.0 | 28.0 | 6468 | 0.2125 | | |
| | 0.0 | 29.0 | 6699 | 0.2172 | | |
| | 0.0 | 30.0 | 6930 | 0.2185 | | |
| | 0.0 | 31.0 | 7161 | 0.2004 | | |
| | 0.0 | 32.0 | 7392 | 0.2077 | | |
| | 0.0 | 33.0 | 7623 | 0.2333 | | |
| | 0.0003 | 34.0 | 7854 | 0.2102 | | |
| | 0.0 | 35.0 | 8085 | 0.2095 | | |
| | 0.0 | 36.0 | 8316 | 0.2030 | | |
| | 0.0 | 37.0 | 8547 | 0.2038 | | |
| | 0.0 | 38.0 | 8778 | 0.2062 | | |
| | 0.0 | 39.0 | 9009 | 0.2080 | | |
| | 0.0 | 40.0 | 9240 | 0.2083 | | |
| | 0.0 | 41.0 | 9471 | 0.2063 | | |
| | 0.0 | 42.0 | 9702 | 0.2146 | | |
| | 0.0 | 43.0 | 9933 | 0.2168 | | |
| | 0.0 | 44.0 | 10164 | 0.2112 | | |
| | 0.0 | 45.0 | 10395 | 0.2109 | | |
| | 0.0 | 46.0 | 10626 | 0.2116 | | |
| | 0.0 | 47.0 | 10857 | 0.2122 | | |
| | 0.0 | 48.0 | 11088 | 0.2122 | | |
| | 0.0 | 49.0 | 11319 | 0.2124 | | |
| | 0.0 | 50.0 | 11550 | 0.2124 | | |
| ### Framework versions | |
| - Transformers 4.17.0 | |
| - Pytorch 1.10.0 | |
| - Datasets 1.18.3 | |
| - Tokenizers 0.11.6 | |