Instructions to use Chae/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chae/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Chae/results")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Chae/results") model = AutoModelForQuestionAnswering.from_pretrained("Chae/results") - Notebooks
- Google Colab
- Kaggle
results
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.5909
- Exact Match: 5.7224
- F1: 10.3127
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 | Exact Match | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 380 | 2.7182 | 3.5093 | 8.2470 |
| 2.8171 | 2.0 | 760 | 2.5752 | 5.3430 | 10.5520 |
| 2.3508 | 3.0 | 1140 | 2.5909 | 5.7224 | 10.3127 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
- Downloads last month
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Model tree for Chae/results
Base model
distilbert/distilbert-base-uncased