# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText
processor = AutoProcessor.from_pretrained("nide01/pokemon_captions")
model = AutoModelForImageTextToText.from_pretrained("nide01/pokemon_captions")Quick Links
pokemon_captions
This model is a fine-tuned version of microsoft/git-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 6.8345
- Wer Score: 49.8505
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Score |
|---|---|---|---|---|
| 9.5056 | 0.21 | 10 | 8.3847 | 75.6675 |
| 8.0875 | 0.43 | 20 | 7.6115 | 75.9652 |
| 7.4568 | 0.64 | 30 | 7.1151 | 69.8376 |
| 7.0645 | 0.85 | 40 | 6.8345 | 49.8505 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
- 10
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for nide01/pokemon_captions
Base model
microsoft/git-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="nide01/pokemon_captions")