Feature Extraction
Transformers
TensorBoard
Safetensors
vision-text-dual-encoder
Generated from Trainer
Instructions to use sharkMeow/clip-roberta-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sharkMeow/clip-roberta-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sharkMeow/clip-roberta-finetuned")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("sharkMeow/clip-roberta-finetuned") model = AutoModel.from_pretrained("sharkMeow/clip-roberta-finetuned") - Notebooks
- Google Colab
- Kaggle
clip-roberta-finetuned
This model is a fine-tuned version of ckiplab/bert-base-chinese on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3715
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: 1e-05
- train_batch_size: 64
- eval_batch_size: 100
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.3102 | 10.0 | 390 | 2.7681 |
| 1.6079 | 20.0 | 780 | 1.5404 |
| 0.7749 | 30.0 | 1170 | 0.9966 |
| 0.4468 | 40.0 | 1560 | 0.7465 |
| 0.2965 | 50.0 | 1950 | 0.5970 |
| 0.2199 | 60.0 | 2340 | 0.5014 |
| 0.1751 | 70.0 | 2730 | 0.4469 |
| 0.1487 | 80.0 | 3120 | 0.4024 |
| 0.1317 | 90.0 | 3510 | 0.3746 |
| 0.1234 | 100.0 | 3900 | 0.3715 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for sharkMeow/clip-roberta-finetuned
Base model
ckiplab/bert-base-chinese