Transformers
PyTorch
TensorFlow
Safetensors
t5
text2text-generation
generated_from_keras_callback
text-generation-inference
Instructions to use 0x70DA/t5-v1_1-base-abs_qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 0x70DA/t5-v1_1-base-abs_qa with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("0x70DA/t5-v1_1-base-abs_qa") model = AutoModelForSeq2SeqLM.from_pretrained("0x70DA/t5-v1_1-base-abs_qa") - Notebooks
- Google Colab
- Kaggle
t5-v1_1-base-abs_qa
This model is a fine-tuned version of MahmoudH/t5-v1_1-base-finetuned-sci_summ on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.5545
- Validation Loss: 0.6041
- Epoch: 2
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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 87288, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: mixed_float16
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 0.8647 | 0.6401 | 0 |
| 0.6569 | 0.6209 | 1 |
| 0.5545 | 0.6041 | 2 |
Framework versions
- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.9.0
- Tokenizers 0.13.2
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