Instructions to use wesleyaag/data2vec-squad-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wesleyaag/data2vec-squad-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="wesleyaag/data2vec-squad-test")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("wesleyaag/data2vec-squad-test") model = AutoModelForQuestionAnswering.from_pretrained("wesleyaag/data2vec-squad-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- en
datasets:
- squad
model:
- facebook/data2vec-text-base
data2vec squad
This is a testing fine tuned data2vec model in the squad dataset, any improvements and suggestions are welcome!Intended use
Question AnsweringTraining results
| Epoch | Training Loss | Validation Loss |
|---|---|---|
| 1 | 1.015800 |
0.997690 |
| 2 | 0.804400 | 0.950322 |
Hyperparameters
- evaluation_strategy="epoch"
- learning_rate=2e-5
- per_device_train_batch_size=15
- per_device_eval_batch_size=15
- num_train_epochs=2
- weight_decay=0.01
Frameworks and libraries used:
- transformers
- datasets
- evaluate