File size: 5,583 Bytes
05e2184 ff7fe7e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 |
---
license: mit
tags:
- multimodal
- embeddings
datasets:
- ituperceptron/image-captioning-turkish
- dogukanvzr/ml-paraphrase-tr
library_name: pytorch
language:
- tr
base_model:
- newmindai/modernbert-base-tr-uncased-allnli-stsb
- facebook/dinov2-base
---
# Turkish Multimodal Embedding Model
This repository contains a **contrastively trained Turkish multimodal embedding model**, combining a text encoder and a vision encoder with projection heads.
The model is trained entirely on **Turkish datasets** (image–caption and paraphrase), making it specifically tailored for Turkish multimodal applications.
## Model Summary
- **Text encoder**: `newmindai/modernbert-base-tr-uncased-allnli-stsb`
- **Vision encoder**: `facebook/dinov2-base`
- **Dimensions**: `text_dim=768`, `image_dim=768`, `embed_dim=768`
- **Projection dropout**: fixed at `0.4` (inside `ProjectionHead`)
- **Pooling**: mean pooling over tokens (`use_mean_pooling_for_text=True`)
- **Normalize outputs**: `{normalize}`
- **Encoders frozen during training?**: `{frozen}` (this release was trained with encoders **NOT frozen**)
- **Language focus**: Turkish (both text and image–caption pairs are fully in Turkish)
## Training Strategy (inspired by JINA-CLIP-v2 style)
- The model was trained jointly with **image–text** and **text–text** pairs using a **bidirectional contrastive loss** (InfoNCE/CLIP-style).
- For **image–text**, standard CLIP-style training with **in-batch negatives** was applied.
- For **text–text**, only **positive paraphrase pairs (label=1)** were used, with in-batch negatives coming from other samples.
- This follows the general training philosophy often seen in Jina’s multimodal work, but in a **simplified single-stage setup** (without the 3-stage curriculum).
## Datasets
- **Image–Text**: [`ituperceptron/image-captioning-turkish`](https://huggingface.co/datasets/ituperceptron/image-captioning-turkish)
- **Text–Text (Paraphrase)**: [`dogukanvzr/ml-paraphrase-tr`](https://huggingface.co/datasets/dogukanvzr/ml-paraphrase-tr)
> Both datasets are in Turkish, aligning the model’s embedding space around Turkish multimodal signals.
> Please check each dataset’s license and terms before downstream use.
## Files
- `pytorch_model.bin` — PyTorch `state_dict`
- `config.json` — metadata (encoder IDs, dimensions, flags)
- `model.py` — custom model classes (required to load)
- (This README is the model card.)
## Evaluation Results
**Dataset:** Test split created from [`ituperceptron/image-captioning-turkish`](https://huggingface.co/datasets/ituperceptron/image-captioning-turkish)
### Image-Text
**Average cosine similarity:** 0.7934
**Recall@K**
<table>
<tr><th>Direction</th><th>R@1</th><th>R@5</th><th>R@10</th></tr>
<tr><td>Text → Image</td><td>0.9365</td><td>0.9913</td><td>0.9971</td></tr>
<tr><td>Image → Text</td><td>0.9356</td><td>0.9927</td><td>0.9958</td></tr>
</table>
<details>
<summary>Raw metrics (JSON)</summary>
```json
{
"avg_cosine_sim": 0.7934404611587524,
"recall_text_to_image": {
"R@1": 0.936458564763386,
"R@5": 0.9913352588313709,
"R@10": 0.9971117529437903
},
"recall_image_to_text": {
"R@1": 0.9355698733614752,
"R@5": 0.9926682959342369,
"R@10": 0.9957787158409243
}
}
```
</details>
### Text-Text
**Average cosine similarity:** 0.7599
**Recall@K**
<table>
<tr><th>Direction</th><th>R@1</th><th>R@5</th><th>R@10</th></tr>
<tr><td>Text → Text</td><td>0.7198</td><td>0.9453</td><td>0.9824</td></tr>
</table>
<details>
<summary>Raw metrics (JSON)</summary>
```json
{
"avg_cosine_sim": 0.7599335312843323,
"recall_text_to_text": {
"R@1": 0.719875500222321,
"R@5": 0.9453090262338817,
"R@10": 0.9824366385060027
}
}
```
</details>
## Loading & Usage
```python
import torch
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer, AutoImageProcessor
from PIL import Image
device = "cuda" if torch.cuda.is_available() else "cpu"
model_name = "utkubascakir/MultiEmbedTR"
model = AutoModel.from_pretrained(model_name, trust_remote_code=True).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
image_processor = AutoImageProcessor.from_pretrained(model_name, trust_remote_code=True)
model.eval()
# Text Embedding
texts = ["yeşil arka planlı bir kedi", "kumsalda bir köpek"]
text_inputs = tokenizer(
texts,
padding=True,
truncation=True,
return_tensors="pt"
).to(device)
with torch.no_grad():
text_embeds = model.encode_text(
input_ids=text_inputs["input_ids"],
attention_mask=text_inputs["attention_mask"]
)
print("Text embeddings shape:", text_embeds.shape)
# Image Embedding
img = Image.open("kedi.jpg").convert("RGB")
image_inputs = image_processor(
images=img,
return_tensors="pt"
).to(device)
with torch.no_grad():
image_embeds = model.encode_image(
pixel_values=image_inputs["pixel_values"]
)
print("Image embeddings shape:", image_embeds.shape)
similarity = F.cosine_similarity(text_embeds, image_embeds)
print("Cosine similarity:", similarity)
```
## Limitations & Intended Use
This release provides a **Turkish multimodal embedding model**, trained to produce aligned vector representations for text and images.
It has not been tested for specific downstream tasks (e.g., retrieval, classification).
No guarantees for bias/toxicity; please evaluate on your own target domain.
## Citation
If you use this model, please cite this repository. |