Instructions to use xummer/adversarial_qa_dbert_based_on with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xummer/adversarial_qa_dbert_based_on with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("xummer/adversarial_qa_dbert_based_on") model = AutoModelForSeq2SeqLM.from_pretrained("xummer/adversarial_qa_dbert_based_on") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: google-t5/t5-large | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - bleu | |
| model-index: | |
| - name: fft-t5-large/adversarial_qa_dbert_based_on | |
| 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. --> | |
| # fft-t5-large/adversarial_qa_dbert_based_on | |
| This model is a fine-tuned version of [google-t5/t5-large](https://huggingface.co/google-t5/t5-large) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.1381 | |
| - Exact Match: 0.3467 | |
| - Bleu: 0.3083 | |
| ## 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: 0.0002 | |
| - train_batch_size: 2 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - distributed_type: multi-GPU | |
| - num_devices: 4 | |
| - total_train_batch_size: 8 | |
| - total_eval_batch_size: 64 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 5.0 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Exact Match | Bleu | | |
| |:-------------:|:-----:|:----:|:---------------:|:-----------:|:------:| | |
| | 1.0162 | 1.0 | 63 | 0.7607 | 0.2754 | 0.2749 | | |
| | 0.3929 | 2.0 | 126 | 0.7943 | 0.2959 | 0.2412 | | |
| | 0.1542 | 3.0 | 189 | 1.0053 | 0.3018 | 0.2720 | | |
| | 0.0544 | 4.0 | 252 | 1.1005 | 0.3457 | 0.3185 | | |
| | 0.0239 | 5.0 | 315 | 1.1381 | 0.3467 | 0.3083 | | |
| ### Framework versions | |
| - Transformers 4.41.2 | |
| - Pytorch 2.2.1+cu121 | |
| - Datasets 2.20.0 | |
| - Tokenizers 0.19.1 | |