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
metadata
base_model: clip-mixer-300k
tags:
- generated_from_trainer
model-index:
- name: clip-mixer-300k
results: []
clip-mixer-300k
This model is a fine-tuned version of 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