Instructions to use Rocketknight1/clip-roberta-coco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rocketknight1/clip-roberta-coco with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Rocketknight1/clip-roberta-coco")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("Rocketknight1/clip-roberta-coco") model = AutoModel.from_pretrained("Rocketknight1/clip-roberta-coco", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Rocketknight1/clip-roberta-coco: direct link, hf CLI and curl.
- Browser
- Download file 1.61 kB
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https://huggingface.co/Rocketknight1/clip-roberta-coco/resolve/main/README.md
- Command line
-
hf download hf://Rocketknight1/clip-roberta-coco/README.md
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curl -L -o README.md https://huggingface.co/Rocketknight1/clip-roberta-coco/resolve/main/README.md
1.61 kB
metadata
tags:
- generated_from_keras_callback
datasets:
- ydshieh/coco_dataset_script
model-index:
- name: Rocketknight1/clip-roberta-coco
results: []
Rocketknight1/clip-roberta-coco
This model is a fine-tuned version of on the ydshieh/coco_dataset_script 2017 dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.1749
- Validation Loss: 1.6969
- Epoch: 2
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:
- optimizer: {'name': 'AdamWeightDecay', 'global_clipnorm': 1.0, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 27738, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.1}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 0.6558 | 1.8089 | 0 |
| 0.3065 | 1.7544 | 1 |
| 0.1749 | 1.6969 | 2 |
Framework versions
- Transformers 4.27.0.dev0
- TensorFlow 2.11.0
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2