Instructions to use bilbo991/clip-mixer-300k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bilbo991/clip-mixer-300k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bilbo991/clip-mixer-300k")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("bilbo991/clip-mixer-300k") model = AutoModel.from_pretrained("bilbo991/clip-mixer-300k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| base_model: clip-mixer-300k | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: clip-mixer-300k | |
| results: [] | |
| <!-- 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. --> | |
| # clip-mixer-300k | |
| This model is a fine-tuned version of [clip-mixer-300k](https://huggingface.co/clip-mixer-300k) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.4179 | |
| ## 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 | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:-----:|:---------------:| | |
| | 1.2811 | 1.0 | 9375 | 2.5993 | | |
| | 0.8159 | 2.0 | 18750 | 2.4179 | | |
| | 0.374 | 3.0 | 28125 | 2.4597 | | |
| ### Framework versions | |
| - Transformers 4.32.0.dev0 | |
| - Pytorch 2.0.1+cu117 | |
| - Datasets 2.14.1 | |
| - Tokenizers 0.13.3 | |