Instructions to use ativilambit/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ativilambit/results with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ativilambit/results") model = AutoModelForSeq2SeqLM.from_pretrained("ativilambit/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: google/flan-t5-base | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - rouge | |
| model-index: | |
| - name: results | |
| 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. --> | |
| # results | |
| This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.0912 | |
| - Rouge1: 0.2091 | |
| - Rouge2: 0.1205 | |
| - Rougel: 0.1845 | |
| - Rougelsum: 0.1965 | |
| ## 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: 0.0003 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 4 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | |
| | No log | 1.0 | 17 | 1.3457 | 0.2023 | 0.0986 | 0.1645 | 0.1834 | | |
| | No log | 2.0 | 34 | 1.1429 | 0.2047 | 0.1129 | 0.1777 | 0.1930 | | |
| | No log | 3.0 | 51 | 1.0912 | 0.2091 | 0.1205 | 0.1845 | 0.1965 | | |
| ### Framework versions | |
| - Transformers 4.33.1 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.14.5 | |
| - Tokenizers 0.13.3 | |