Instructions to use herMaster/devang-qna-model-5epoch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use herMaster/devang-qna-model-5epoch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="herMaster/devang-qna-model-5epoch")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("herMaster/devang-qna-model-5epoch") model = AutoModelForQuestionAnswering.from_pretrained("herMaster/devang-qna-model-5epoch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: devang-qna-model-5epoch
results: []
devang-qna-model-5epoch
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset. It achieves the following results on the evaluation set:
- Loss: 1.5994
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 250 | 2.2048 |
| 2.6636 | 2.0 | 500 | 1.5864 |
| 2.6636 | 3.0 | 750 | 1.5463 |
| 1.0762 | 4.0 | 1000 | 1.5754 |
| 1.0762 | 5.0 | 1250 | 1.5994 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1