Downloads · 30 days
0
distilbert-base-uncased
nainakader/distilbert-base-uncased
distilbert-base-uncased is a machine learning model from nainakader. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
Access
Public
Updated Oct 22, 2023
Repo size
2.5 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin2.5 MB · 73%
At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Oct 22, 2023
- Updated
- Oct 22, 2023
- SHA
- e7e27387
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
distilbert-base-uncased
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: 1.9069
- Accuracy: {'accuracy': 0.871}
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: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 250 | 1.7205 | {'accuracy': 0.868} |
| 0.1563 | 2.0 | 500 | 1.5937 | {'accuracy': 0.865} |
| 0.1563 | 3.0 | 750 | 1.6187 | {'accuracy': 0.861} |
| 0.1939 | 4.0 | 1000 | 1.2535 | {'accuracy': 0.861} |
| 0.1939 | 5.0 | 1250 | 1.5725 | {'accuracy': 0.87} |
| 0.0701 | 6.0 | 1500 | 1.7691 | {'accuracy': 0.874} |
| 0.0701 | 7.0 | 1750 | 1.8419 | {'accuracy': 0.864} |
| 0.0224 | 8.0 | 2000 | 1.8159 | {'accuracy': 0.876} |
| 0.0224 | 9.0 | 2250 | 1.9088 | {'accuracy': 0.871} |
| 0.0129 | 10.0 | 2500 | 1.9069 | {'accuracy': 0.871} |
Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
- License
- apache-2.0
- Created
- Oct 22, 2023
- Updated
- Oct 22, 2023