Instructions to use Trickshotblaster/leetcoder-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Trickshotblaster/leetcoder-qa with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Trickshotblaster/leetcoder-qa") model = AutoModelForSeq2SeqLM.from_pretrained("Trickshotblaster/leetcoder-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: leetcoder-qa | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information Keras had access to. You should | |
| probably proofread and complete it, then remove this comment. --> | |
| # leetcoder-qa | |
| This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Train Loss: 4.1922 | |
| - Validation Loss: 3.8900 | |
| - Epoch: 9 | |
| ## 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: | |
| - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 2e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} | |
| - training_precision: float32 | |
| ### Training results | |
| | Train Loss | Validation Loss | Epoch | | |
| |:----------:|:---------------:|:-----:| | |
| | 6.3985 | 4.7493 | 0 | | |
| | 4.8760 | 4.4969 | 1 | | |
| | 4.7016 | 4.3654 | 2 | | |
| | 4.5848 | 4.2650 | 3 | | |
| | 4.5086 | 4.1827 | 4 | | |
| | 4.4307 | 4.1106 | 5 | | |
| | 4.3581 | 4.0471 | 6 | | |
| | 4.2923 | 3.9898 | 7 | | |
| | 4.2397 | 3.9375 | 8 | | |
| | 4.1922 | 3.8900 | 9 | | |
| ### Framework versions | |
| - Transformers 4.30.1 | |
| - TensorFlow 2.12.0 | |
| - Datasets 2.12.0 | |
| - Tokenizers 0.13.3 | |