metadata
tags:
- generated_from_trainer
model-index:
- name: persian-clip
results: []
persian-clip
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7629
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: 3e-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
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.4072 | 0.12 | 100 | 2.1627 |
| 1.7146 | 0.25 | 200 | 1.6432 |
| 1.5058 | 0.37 | 300 | 1.4523 |
| 1.3836 | 0.49 | 400 | 1.4799 |
| 1.4946 | 0.62 | 500 | 1.3101 |
| 1.2544 | 0.74 | 600 | 1.2073 |
| 1.1984 | 0.86 | 700 | 1.1801 |
| 1.3243 | 0.99 | 800 | 1.1652 |
| 0.8373 | 1.11 | 900 | 1.0860 |
| 0.8625 | 1.23 | 1000 | 1.0731 |
| 0.791 | 1.36 | 1100 | 1.0427 |
| 0.8975 | 1.48 | 1200 | 1.0786 |
| 0.7767 | 1.6 | 1300 | 1.0248 |
| 0.9041 | 1.73 | 1400 | 1.0311 |
| 0.8474 | 1.85 | 1500 | 0.9649 |
| 0.7435 | 1.98 | 1600 | 0.9552 |
| 0.5126 | 2.1 | 1700 | 0.9909 |
| 0.4871 | 2.22 | 1800 | 0.9188 |
| 0.48 | 2.35 | 1900 | 0.9151 |
| 0.4715 | 2.47 | 2000 | 0.9056 |
| 0.408 | 2.59 | 2100 | 0.8885 |
| 0.4999 | 2.72 | 2200 | 0.8911 |
| 0.5169 | 2.84 | 2300 | 0.8727 |
| 0.3574 | 2.96 | 2400 | 0.8477 |
| 0.2749 | 3.09 | 2500 | 0.8666 |
| 0.2719 | 3.21 | 2600 | 0.8520 |
| 0.2779 | 3.33 | 2700 | 0.8379 |
| 0.3407 | 3.46 | 2800 | 0.8386 |
| 0.223 | 3.58 | 2900 | 0.8245 |
| 0.2649 | 3.7 | 3000 | 0.8149 |
| 0.2698 | 3.83 | 3100 | 0.7983 |
| 0.1863 | 3.95 | 3200 | 0.7959 |
| 0.1831 | 4.07 | 3300 | 0.7957 |
| 0.172 | 4.2 | 3400 | 0.7963 |
| 0.1457 | 4.32 | 3500 | 0.7879 |
| 0.1503 | 4.44 | 3600 | 0.7794 |
| 0.1783 | 4.57 | 3700 | 0.7788 |
| 0.166 | 4.69 | 3800 | 0.7753 |
| 0.1598 | 4.81 | 3900 | 0.7673 |
| 0.1618 | 4.94 | 4000 | 0.7629 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2+cu121
- Datasets 2.10.1
- Tokenizers 0.15.0
How to use?
# Both models generate vectors with 768 dimensions.
from transformers import CLIPVisionModel, RobertaModel, AutoTokenizer, CLIPFeatureExtractor
# download pre-trained models
vision_encoder = CLIPVisionModel.from_pretrained('SeyedAli/Persian-CLIP')
preprocessor = CLIPFeatureExtractor.from_pretrained('SeyedAli/Persian-CLIP')
text_encoder = RobertaModel.from_pretrained('SeyedAli/Persian-CLIP')
tokenizer = AutoTokenizer.from_pretrained('SeyedAli/Persian-CLIP')
# define input image and input text
text = 'something'
image = PIL.Image.open('my_favorite_image.jpg')
# compute embeddings
text_embedding = text_encoder(**tokenizer(text,
return_tensors='pt')).pooler_output
image_embedding = vision_encoder(**preprocessor(image,
return_tensors='pt')).pooler_output
zero-shot-Image-Classification:
The followings are just some use cases of Persian-CLIP on 25K Unsplash images
- use pip install -q git+https://github.com/sajjjadayobi/clipfa.git
from clipfa import CLIPDemo
import torch
# Both models generate vectors with 768 dimensions.
from transformers import CLIPVisionModel, RobertaModel, AutoTokenizer, CLIPFeatureExtractor
# download pre-trained models
vision_encoder = CLIPVisionModel.from_pretrained('SeyedAli/Persian-CLIP')
preprocessor = CLIPFeatureExtractor.from_pretrained('SeyedAli/Persian-CLIP')
text_encoder = RobertaModel.from_pretrained('SeyedAli/Persian-CLIP')
tokenizer = AutoTokenizer.from_pretrained('SeyedAli/Persian-CLIP')
demo = CLIPDemo(vision_encoder, text_encoder, tokenizer)
demo.compute_text_embeddings(['متن 3' ,'متن 2' ,'متن 1'])
demo.compute_image_embeddings(['my_favorite_image.jpg'])
demo.zero_shot(image_path='my_favorite_image.jpg')