Downloads · 30 days
8
2% of all-time downloads
finetuning-sentiment-model-1500-samples
tonyla25/finetuning-sentiment-model-1500-samples
finetuning-sentiment-model-1500-samples is a text classification model from tonyla25. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
351
Public
Repo size
1.1 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin268 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Oct 8, 2023
- Updated
- Oct 8, 2023
- SHA
- 0dd9d9c0
Try a prompt
Base models
Datasets
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. -->
finetuning-sentiment-model-1500-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset. It achieves the following results on the evaluation set:
- Loss: 0.3496
- Accuracy: 0.87
- F1: 0.8721
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: 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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 94 | 0.3214 | 0.8667 | 0.8649 |
| No log | 2.0 | 188 | 0.3496 | 0.87 | 0.8721 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.1.0
- Datasets 2.12.0
- Tokenizers 0.13.2
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Oct 8, 2023
- Updated
- Oct 8, 2023