## Acknowledgement
Google supported this work by providing Google Cloud credit. Thank you Google for supporting the open source! 🎉

## What is this?
This model is a finetuned version of [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) to be used as a text encoder in Turkish with [CLIP](https://github.com/openai/CLIP)'s `ViT-B/32` image encoder. It should be used with `clip_head.h5` from [my accompanying repo on GitHub]. Go to that repo for fully working example, and a simple usage example is as follows:

```python
from transformers import AutoTokenizer, TFAutoModel
import tensorflow as tf
import numpy as np
from PIL import Image
import torch
import clip

model_name = "mys/distilbert-base-turkish-cased-clip"
base_model = TFAutoModel.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
head_model = tf.keras.models.load_model("./clip_head.h5")

def encode_text(base_model, tokenizer, head_model, texts):
    tokens = tokenizer(texts, padding=True, return_tensors='tf')
    embs = base_model(**tokens)[0]

    attention_masks = tf.cast(tokens['attention_mask'], tf.float32)
    sample_length = tf.reduce_sum(attention_masks, axis=-1, keepdims=True)
    masked_embs = embs * tf.expand_dims(attention_masks, axis=-1)
    base_embs = tf.reduce_sum(masked_embs, axis=1) / tf.cast(sample_length, tf.float32)
    clip_embs = head_model(base_embs)
    clip_embs /= tf.norm(clip_embs, axis=-1, keepdims=True)
    return clip_embs



demo_images = {
    "bilgisayarda çalışan bir insan": "myspc.jpeg",
    "sahilde bir insan ve bir heykel": "mysdk.jpeg"
    }

clip_model, preprocess = clip.load("ViT-B/32")
images = {key: Image.open(f"images/{value}") for key, value in demo_images.items()}
img_inputs = torch.stack([preprocess(image).to('cpu') for image in images.values()])

with torch.no_grad():
    image_embs = clip_model.encode_image(img_inputs).float().to('cpu')

image_embs /= image_embs.norm(dim=-1, keepdim=True)
image_embs = image_embs.detach().numpy()
text_embs = encode_text(base_model, tokenizer, head_model, list(images.keys())).numpy()
similarities = image_embs @ text_embs.T
logits = tf.nn.softmax(tf.convert_to_tensor(similarities)).numpy()
idxs = np.argmax(logits, axis=-1).tolist()
for i, (key, value) in enumerate(demo_images.items()):
    print("path: ", value, "true label: ", key, "prediction: ", list(demo_images.keys())[idxs[i]], "score: ", logits[i, idxs[i]])
```

Sample images referred in the code snippet above can be found under `images` directory in the gitHub repo.

## How it works
`encode_text()` function agregates per-token hidden states outputted by the Distilbert model to produce a single vector per sequence. Then, `clip_head.h5` model projects this vector onto the same vector space as CLIP's text encoder with a single dense layer. First, all the Distilbert layers were frozen an and the head dense layer was trained for a few epochs. Then, freezing was removed and the dense layer was trained with the Distilbert layers for a few more epochs. I created the dataset by machine-translating COCO captions into Turkish. During training, vector representations of English captions outputted by the original CLIP text encoder was used as target values, and MSE between these vectors and `clip_head.h5` outputs were minimized.

## Dataset
The dataset and the training notebook will be released soon.
{}
transformers tf distilbert feature-extraction endpoints_compatible region:us
TAGS
#transformers #tf #distilbert #feature-extraction #endpoints_compatible #region-us 

## Acknowledgement
Google supported this work by providing Google Cloud credit. Thank you Google for supporting the open source! 

## What is this?
This model is a finetuned version of dbmdz/distilbert-base-turkish-cased to be used as a text encoder in Turkish with CLIP's 'ViT-B/32' image encoder. It should be used with 'clip_head.h5' from [my accompanying repo on GitHub]. Go to that repo for fully working example, and a simple usage example is as follows:



Sample images referred in the code snippet above can be found under 'images' directory in the gitHub repo.

## How it works
'encode_text()' function agregates per-token hidden states outputted by the Distilbert model to produce a single vector per sequence. Then, 'clip_head.h5' model projects this vector onto the same vector space as CLIP's text encoder with a single dense layer. First, all the Distilbert layers were frozen an and the head dense layer was trained for a few epochs. Then, freezing was removed and the dense layer was trained with the Distilbert layers for a few more epochs. I created the dataset by machine-translating COCO captions into Turkish. During training, vector representations of English captions outputted by the original CLIP text encoder was used as target values, and MSE between these vectors and 'clip_head.h5' outputs were minimized.

## Dataset
The dataset and the training notebook will be released soon.
## Acknowledgement
Google supported this work by providing Google Cloud credit. Thank you Google for supporting the open source! ## What is this?
This model is a finetuned version of dbmdz/distilbert-base-turkish-cased to be used as a text encoder in Turkish with CLIP's 'ViT-B/32' image encoder. It should be used with 'clip_head.h5' from [my accompanying repo on GitHub]. Go to that repo for fully working example, and a simple usage example is as follows:



Sample images referred in the code snippet above can be found under 'images' directory in the gitHub repo. ## How it works
'encode_text()' function agregates per-token hidden states outputted by the Distilbert model to produce a single vector per sequence. Then, 'clip_head.h5' model projects this vector onto the same vector space as CLIP's text encoder with a single dense layer. First, all the Distilbert layers were frozen an and the head dense layer was trained for a few epochs. Then, freezing was removed and the dense layer was trained with the Distilbert layers for a few more epochs. I created the dataset by machine-translating COCO captions into Turkish. During training, vector representations of English captions outputted by the original CLIP text encoder was used as target values, and MSE between these vectors and 'clip_head.h5' outputs were minimized. ## Dataset
The dataset and the training notebook will be released soon.
TAGS
#transformers #tf #distilbert #feature-extraction #endpoints_compatible #region-us 
 ## Acknowledgement
Google supported this work by providing Google Cloud credit. Thank you Google for supporting the open source! ## What is this?
This model is a finetuned version of dbmdz/distilbert-base-turkish-cased to be used as a text encoder in Turkish with CLIP's 'ViT-B/32' image encoder. It should be used with 'clip_head.h5' from [my accompanying repo on GitHub]. Go to that repo for fully working example, and a simple usage example is as follows:



Sample images referred in the code snippet above can be found under 'images' directory in the gitHub repo. ## How it works
'encode_text()' function agregates per-token hidden states outputted by the Distilbert model to produce a single vector per sequence. Then, 'clip_head.h5' model projects this vector onto the same vector space as CLIP's text encoder with a single dense layer. First, all the Distilbert layers were frozen an and the head dense layer was trained for a few epochs. Then, freezing was removed and the dense layer was trained with the Distilbert layers for a few more epochs. I created the dataset by machine-translating COCO captions into Turkish. During training, vector representations of English captions outputted by the original CLIP text encoder was used as target values, and MSE between these vectors and 'clip_head.h5' outputs were minimized. ## Dataset
The dataset and the training notebook will be released soon.
passage: TAGS
#transformers #tf #distilbert #feature-extraction #endpoints_compatible #region-us 
## Acknowledgement
Google supported this work by providing Google Cloud credit. Thank you Google for supporting the open source!## What is this?
This model is a finetuned version of dbmdz/distilbert-base-turkish-cased to be used as a text encoder in Turkish with CLIP's 'ViT-B/32' image encoder. It should be used with 'clip_head.h5' from [my accompanying repo on GitHub]. Go to that repo for fully working example, and a simple usage example is as follows:



Sample images referred in the code snippet above can be found under 'images' directory in the gitHub repo.## How it works
'encode_text()' function agregates per-token hidden states outputted by the Distilbert model to produce a single vector per sequence. Then, 'clip_head.h5' model projects this vector onto the same vector space as CLIP's text encoder with a single dense layer. First, all the Distilbert layers were frozen an and the head dense layer was trained for a few epochs. Then, freezing was removed and the dense layer was trained with the Distilbert layers for a few more epochs. I created the dataset by machine-translating COCO captions into Turkish. During training, vector representations of English captions outputted by the original CLIP text encoder was used as target values, and MSE between these vectors and 'clip_head.h5' outputs were minimized.## Dataset
The dataset and the training notebook will be released soon.