Feature Extraction
Transformers
TensorBoard
Safetensors
vision-text-dual-encoder
Generated from Trainer
# Load model directly
from transformers import AutoProcessor, AutoModel
processor = AutoProcessor.from_pretrained("pavement/clip-roberta-finetuned")
model = AutoModel.from_pretrained("pavement/clip-roberta-finetuned")Quick Links
clip-roberta-finetuned
This model was trained from scratch on the ydshieh/coco_dataset_script 2017 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0
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: 1
- eval_batch_size: 1
- 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 4.42.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="pavement/clip-roberta-finetuned")