Instructions to use adityay1221/Pixie.30.32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adityay1221/Pixie.30.32 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("adityay1221/Pixie.30.32") model = AutoModelForSeq2SeqLM.from_pretrained("adityay1221/Pixie.30.32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - bleu | |
| model-index: | |
| - name: Pixie.30.32 | |
| 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. --> | |
| # Pixie.30.32 | |
| This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.1623 | |
| - Bleu: 47.6437 | |
| ## 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: 1e-05 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 32 | |
| - seed: 121 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 30 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Bleu | | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:| | |
| | No log | 9.09 | 100 | 1.5563 | 21.3462 | | |
| | No log | 18.18 | 200 | 1.2493 | 29.2353 | | |
| | No log | 27.27 | 300 | 1.1670 | 32.5700 | | |
| ### Framework versions | |
| - Transformers 4.18.0 | |
| - Pytorch 1.11.0a0+17540c5 | |
| - Datasets 2.1.0 | |
| - Tokenizers 0.12.1 | |