Question Answering
Transformers
TensorBoard
Safetensors
t5
Generated from Trainer
text-generation-inference
Instructions to use vijaynaykodi/flan_t5_small_qa_vijay with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vijaynaykodi/flan_t5_small_qa_vijay with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="vijaynaykodi/flan_t5_small_qa_vijay")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("vijaynaykodi/flan_t5_small_qa_vijay") model = AutoModelForQuestionAnswering.from_pretrained("vijaynaykodi/flan_t5_small_qa_vijay", device_map="auto") - Notebooks
- Google Colab
- Kaggle
flan_t5_small_qa_vijay
This model is a fine-tuned version of google/flan-t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4820
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: 0.003
- train_batch_size: 14
- eval_batch_size: 14
- 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 |
|---|---|---|---|
| 2.0484 | 1.0 | 1786 | 1.8813 |
| 1.3625 | 2.0 | 3572 | 1.5579 |
| 0.8756 | 3.0 | 5358 | 1.4820 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
- Downloads last month
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Model tree for vijaynaykodi/flan_t5_small_qa_vijay
Base model
google/flan-t5-small