Question Answering
Transformers
PyTorch
TensorBoard
t5
Generated from Trainer
text-generation-inference
Instructions to use TARUNBHATT/flan-t5-small-finetuned-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TARUNBHATT/flan-t5-small-finetuned-squad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="TARUNBHATT/flan-t5-small-finetuned-squad")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("TARUNBHATT/flan-t5-small-finetuned-squad") model = AutoModelForQuestionAnswering.from_pretrained("TARUNBHATT/flan-t5-small-finetuned-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: flan-t5-small-finetuned-squad
results: []
flan-t5-small-finetuned-squad
This model is a fine-tuned version of google/flan-t5-small on the squad_v2 dataset. It achieves the following results on the evaluation set:
- Loss: 1.4937
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.6998 | 1.0 | 8321 | 1.4937 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3