Instructions to use wooseoko/clip-roberta-finetuned_GQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wooseoko/clip-roberta-finetuned_GQA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="wooseoko/clip-roberta-finetuned_GQA")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("wooseoko/clip-roberta-finetuned_GQA") model = AutoModel.from_pretrained("wooseoko/clip-roberta-finetuned_GQA") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Error:"datasets[0]" with value "./GQA_script.py" is not valid. If possible, use a dataset id from https://hf.co/datasets.
clip-roberta-finetuned_GQA
This model is a fine-tuned version of ./clip-roberta on the ./GQA_script.py relation dataset. It achieves the following results on the evaluation set:
- Loss: 2.3532
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: 3.0
Training results
Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2
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