Instructions to use ShynBui/s21 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShynBui/s21 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="ShynBui/s21")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ShynBui/s21") model = AutoModelForQuestionAnswering.from_pretrained("ShynBui/s21", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ShynBui/s21: direct link, hf CLI and curl.
- Browser
- Download file 1.05 kB
-
https://huggingface.co/ShynBui/s21/resolve/main/README.md
- Command line
-
hf download hf://ShynBui/s21/README.md
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curl -L -o README.md https://huggingface.co/ShynBui/s21/resolve/main/README.md
1.05 kB
| license: apache-2.0 | |
| base_model: bert-base-cased | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - squad_v2 | |
| model-index: | |
| - name: s21 | |
| 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. --> | |
| # s21 | |
| This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad_v2 dataset. | |
| ## 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: 0.0004 | |
| - 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 | |
| ### Framework versions | |
| - Transformers 4.31.0 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.14.3 | |
| - Tokenizers 0.13.3 | |