Instructions to use tiagoblima/debug_t5-small_squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiagoblima/debug_t5-small_squad with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("tiagoblima/debug_t5-small_squad") model = AutoModelForSeq2SeqLM.from_pretrained("tiagoblima/debug_t5-small_squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from tiagoblima/debug_t5-small_squad: direct link, hf CLI and curl.
- Browser
- Download file 1.23 kB
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https://huggingface.co/tiagoblima/debug_t5-small_squad/resolve/main/README.md
- Command line
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hf download hf://tiagoblima/debug_t5-small_squad/README.md
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curl -L -o README.md https://huggingface.co/tiagoblima/debug_t5-small_squad/resolve/main/README.md
1.23 kB
| license: mit | |
| base_model: unicamp-dl/ptt5-small-t5-vocab | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - tiagoblima/qg_squad_v1_pt | |
| model-index: | |
| - name: debug_t5-small_squad | |
| 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. --> | |
| # debug_t5-small_squad | |
| This model is a fine-tuned version of [unicamp-dl/ptt5-small-t5-vocab](https://huggingface.co/unicamp-dl/ptt5-small-t5-vocab) on the tiagoblima/qg_squad_v1_pt dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.5534 | |
| ## 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: 64 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 2.0 | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 4.35.2 | |
| - Pytorch 2.1.0+cu118 | |
| - Datasets 2.15.0 | |
| - Tokenizers 0.15.0 | |