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kwondw/reranker-Qwen3.5-0.8B-doodles-any-to-any

sourceHugging Faceupdated 1mo agoView on Hugging Face

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Model Card

CrossEncoder based on Qwen/Qwen3.5-0.8B

This is a Cross Encoder model finetuned from Qwen/Qwen3.5-0.8B on the image_to_text and text_to_image datasets using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

Model Details

Model Description

—Model Type: Cross Encoder
—Base model: Qwen/Qwen3.5-0.8B
—Maximum Sequence Length: 262144 tokens
—Number of Output Labels: 1 label
—Supported Modalities: Text, Image, Video, Message
—Training Datasets:
—image_to_text
—text_to_image

Model Sources

—Documentation: Sentence Transformers Documentation
—Documentation: Cross Encoder Documentation
—Repository: Sentence Transformers on GitHub
—Hugging Face: Cross Encoders on Hugging Face

Full Model Architecture

CrossEncoder(
  (0): Transformer({'transformer_task': 'any-to-any', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}, 'image': {'method': 'forward', 'method_output_name': 'logits'}, 'video': {'method': 'forward', 'method_output_name': 'logits'}, 'message': {'method': 'forward', 'method_output_name': 'logits', 'format': 'structured'}}, 'module_output_name': 'causal_logits', 'processing_kwargs': {'chat_template': {'add_generation_prompt': True}}, 'architecture': 'Qwen3_5ForConditionalGeneration'})
  (1): LogitScore({'true_token_id': 16, 'false_token_id': 15, 'module_input_name': 'causal_logits'})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

bash

pip install -U sentence-transformers

Then you can load this model and run inference.

python

from sentence_transformers import CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("kwondw/reranker-Qwen3.5-0.8B-doodles-any-to-any")
# Get scores for pairs of inputs
pairs = [
    ['https://huggingface.co/kwondw/reranker-Qwen3.5-0.8B-doodles-any-to-any/resolve/main/assets/image_0.jpg', 'a content character with a tan head and purple puffballs hair wearing a blue fleece, green background'],
    ['https://huggingface.co/kwondw/reranker-Qwen3.5-0.8B-doodles-any-to-any/resolve/main/assets/image_0.jpg', 'a mustache character with a orange head and yellow headband hair wearing a white turtleneck, gradient 3 background'],
    ['https://huggingface.co/kwondw/reranker-Qwen3.5-0.8B-doodles-any-to-any/resolve/main/assets/image_0.jpg', 'a sunglasses character with a purple head and yellow bowlcut hair wearing a orange collar, gradient 4 background'],
    ['https://huggingface.co/kwondw/reranker-Qwen3.5-0.8B-doodles-any-to-any/resolve/main/assets/image_0.jpg', 'a mad note character with a orange head and pink toque hair wearing a orange puffer, grey background'],
    ['https://huggingface.co/kwondw/reranker-Qwen3.5-0.8B-doodles-any-to-any/resolve/main/assets/image_0.jpg', 'a pink beard character with a yellow head and green bowlcut hair wearing a striped sweater, light blue background'],
]
scores = model.predict(pairs)
print(scores)
# [0.8176 0.6298 0.6689 0.7432 0.7356]

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Cross Encoder Reranking

—Datasets: doodles-image-to-text-eval and doodles-text-to-image-eval
—Evaluated with <code>CrossEncoderRerankingEvaluator</code> with these parameters:

json

  {
      "at_k": 10
  }

Metric	doodles-image-to-text-eval	doodles-text-to-image-eval
map	0.9825	0.755
mrr@10	0.9825	0.755
ndcg@10	0.9869	0.8167

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Training Details

Training Datasets

imagetotext

—Dataset: image_to_text at a575ac6
—Size: 4,500 training samples
—Columns: <code>image</code>, <code>text</code>, and <code>label</code>
—Approximate statistics based on the first 100 samples: | | image | text | label | |:---------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------| | type | image | string | int | | modality | image | text | | | details | <ul><li>min: 128x128 px</li><li>mean: 128x128 px</li><li>max: 128x128 px</li></ul> | <ul><li>min: 30 tokens</li><li>mean: 33.48 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>0: ~79.81%</li><li>1: ~20.19%</li></ul> |
—Samples: | image | text | label | |:---------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------|:---------------| | <img src="https://huggingface.co/kwondw/reranker-Qwen3.5-0.8B-doodles-any-to-any/resolve/main/assets/exampleimage0.jpg" width="200"> | <code>a cobain glasses character with a gradient 2 head and purple puffballs hair wearing a white sweater, gradient 4 background</code> | <code>1</code> | | <img src="https://huggingface.co/kwondw/reranker-Qwen3.5-0.8B-doodles-any-to-any/resolve/main/assets/exampleimage0.jpg" width="200"> | <code>a content character with a orange head and purple long hair wearing a striped sweater, yellow background</code> | <code>0</code> | | <img src="https://huggingface.co/kwondw/reranker-Qwen3.5-0.8B-doodles-any-to-any/resolve/main/assets/exampleimage0.jpg" width="200"> | <code>a neutral note character with a orange head and green puffballs hair wearing a combo 2 puffer, light blue background</code> | <code>0</code> |
—Loss: <code>BinaryCrossEntropyLoss</code> with these parameters:

json

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": null
  }

texttoimage

—Dataset: text_to_image at a575ac6
—Size: 4,500 training samples
—Columns: <code>text</code>, <code>image</code>, and <code>label</code>
—Approximate statistics based on the first 100 samples: | | text | image | label | |:---------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | image | int | | modality | text | image | | | details | <ul><li>min: 30 tokens</li><li>mean: 33.13 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 128x128 px</li><li>mean: 128x128 px</li><li>max: 128x128 px</li></ul> | <ul><li>0: ~79.81%</li><li>1: ~20.19%</li></ul> |
—Samples: | text | image | label | |:----------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------|:---------------| | <code>a cobain glasses character with a gradient 2 head and purple puffballs hair wearing a white sweater, gradient 4 background</code> | <img src="https://huggingface.co/kwondw/reranker-Qwen3.5-0.8B-doodles-any-to-any/resolve/main/assets/exampleimage0.jpg" width="200"> | <code>1</code> | | <code>a cobain glasses character with a gradient 2 head and purple puffballs hair wearing a white sweater, gradient 4 background</code> | <img src="https://huggingface.co/kwondw/reranker-Qwen3.5-0.8B-doodles-any-to-any/resolve/main/assets/exampleimage1.jpg" width="200"> | <code>0</code> | | <code>a cobain glasses character with a gradient 2 head and purple puffballs hair wearing a white sweater, gradient 4 background</code> | <img src="https://huggingface.co/kwondw/reranker-Qwen3.5-0.8B-doodles-any-to-any/resolve/main/assets/exampleimage2.jpg" width="200"> | <code>0</code> |
—Loss: <code>BinaryCrossEntropyLoss</code> with these parameters:

json

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": null
  }

Evaluation Datasets

imagetotext

—Dataset: image_to_text at a575ac6
—Size: 500 evaluation samples
—Columns: <code>image</code>, <code>text</code>, and <code>label</code>
—Approximate statistics based on the first 100 samples: | | image | text | label | |:---------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------| | type | image | string | int | | modality | image | text | | | details | <ul><li>min: 128x128 px</li><li>mean: 128x128 px</li><li>max: 128x128 px</li></ul> | <ul><li>min: 27 tokens</li><li>mean: 32.94 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>0: ~79.81%</li><li>1: ~20.19%</li></ul> |
—Samples: | image | text | label | |:-------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------|:---------------| | <img src="https://huggingface.co/kwondw/reranker-Qwen3.5-0.8B-doodles-any-to-any/resolve/main/assets/image0.jpg" width="200"> | <code>a content character with a tan head and purple puffballs hair wearing a blue fleece, green background</code> | <code>1</code> | | <img src="https://huggingface.co/kwondw/reranker-Qwen3.5-0.8B-doodles-any-to-any/resolve/main/assets/image0.jpg" width="200"> | <code>a mustache character with a orange head and yellow headband hair wearing a white turtleneck, gradient 3 background</code> | <code>0</code> | | <img src="https://huggingface.co/kwondw/reranker-Qwen3.5-0.8B-doodles-any-to-any/resolve/main/assets/image_0.jpg" width="200"> | <code>a sunglasses character with a purple head and yellow bowlcut hair wearing a orange collar, gradient 4 background</code> | <code>0</code> |
—Loss: <code>BinaryCrossEntropyLoss</code> with these parameters:

json

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": null
  }

texttoimage

—Dataset: text_to_image at a575ac6
—Size: 500 evaluation samples
—Columns: <code>text</code>, <code>image</code>, and <code>label</code>
—Approximate statistics based on the first 100 samples: | | text | image | label | |:---------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | image | int | | modality | text | image | | | details | <ul><li>min: 29 tokens</li><li>mean: 32.73 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 128x128 px</li><li>mean: 128x128 px</li><li>max: 128x128 px</li></ul> | <ul><li>0: ~79.81%</li><li>1: ~20.19%</li></ul> |
—Samples: | text | image | label | |:-------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------|:---------------| | <code>a content character with a tan head and purple puffballs hair wearing a blue fleece, green background</code> | <img src="https://huggingface.co/kwondw/reranker-Qwen3.5-0.8B-doodles-any-to-any/resolve/main/assets/image0.jpg" width="200"> | <code>1</code> | | <code>a content character with a tan head and purple puffballs hair wearing a blue fleece, green background</code> | <img src="https://huggingface.co/kwondw/reranker-Qwen3.5-0.8B-doodles-any-to-any/resolve/main/assets/exampleimage3.jpg" width="200"> | <code>0</code> | | <code>a content character with a tan head and purple puffballs hair wearing a blue fleece, green background</code> | <img src="https://huggingface.co/kwondw/reranker-Qwen3.5-0.8B-doodles-any-to-any/resolve/main/assets/exampleimage_4.jpg" width="200"> | <code>0</code> |
—Loss: <code>BinaryCrossEntropyLoss</code> with these parameters:

json

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": null
  }

Training Hyperparameters

Non-Default Hyperparameters

—num_train_epochs: 1
—learning_rate: 5e-06
—warmup_steps: 0.1
—fp16: True
—prompts: {'imagetotext': "Given the image, judge whether the text matches it. Respond with 1 if they match, 0 if they don't.", 'texttoimage': "Given the text, judge whether the image matches it. Respond with 1 if they match, 0 if they don't."}

All Hyperparameters

<details><summary>Click to expand</summary>

—per_device_train_batch_size: 8
—num_train_epochs: 1
—max_steps: -1
—learning_rate: 5e-06
—lr_scheduler_type: linear
—lr_scheduler_kwargs: None
—warmup_steps: 0.1
—optim: adamwtorchfused
—optim_args: None
—weight_decay: 0.0
—adam_beta1: 0.9
—adam_beta2: 0.999
—adam_epsilon: 1e-08
—optim_target_modules: None
—gradient_accumulation_steps: 1
—average_tokens_across_devices: True
—max_grad_norm: 1.0
—label_smoothing_factor: 0.0
—bf16: False
—fp16: True
—bf16_full_eval: False
—fp16_full_eval: False
—tf32: None
—gradient_checkpointing: False
—gradient_checkpointing_kwargs: None
—torch_compile: False
—torch_compile_backend: None
—torch_compile_mode: None
—use_liger_kernel: False
—liger_kernel_config: None
—use_cache: False
—neftune_noise_alpha: None
—torch_empty_cache_steps: None
—auto_find_batch_size: False
—log_on_each_node: True
—logging_nan_inf_filter: True
—include_num_input_tokens_seen: no
—log_level: passive
—log_level_replica: warning
—disable_tqdm: False
—project: huggingface
—trackio_space_id: None
—trackio_bucket_id: None
—trackio_static_space_id: None
—per_device_eval_batch_size: 8
—prediction_loss_only: True
—eval_on_start: False
—eval_do_concat_batches: True
—eval_use_gather_object: False
—eval_accumulation_steps: None
—include_for_metrics: []
—batch_eval_metrics: False
—save_only_model: False
—save_on_each_node: False
—enable_jit_checkpoint: False
—push_to_hub: False
—hub_private_repo: None
—hub_model_id: None
—hub_strategy: every_save
—hub_always_push: False
—hub_revision: None
—load_best_model_at_end: False
—ignore_data_skip: False
—restore_callback_states_from_checkpoint: False
—full_determinism: False
—seed: 42
—data_seed: None
—use_cpu: False
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
—parallelism_config: None
—dataloader_drop_last: False
—dataloader_num_workers: 0
—dataloader_pin_memory: True
—dataloader_persistent_workers: False
—dataloader_prefetch_factor: None
—remove_unused_columns: True
—label_names: None
—train_sampling_strategy: random
—length_column_name: length
—ddp_find_unused_parameters: None
—ddp_bucket_cap_mb: None
—ddp_broadcast_buffers: False
—ddp_static_graph: None
—ddp_backend: None
—ddp_timeout: 1800
—fsdp: None
—fsdp_config: None
—deepspeed: None
—debug: []
—skip_memory_metrics: True
—do_predict: False
—resume_from_checkpoint: None
—warmup_ratio: None
—local_rank: -1
—prompts: {'imagetotext': "Given the image, judge whether the text matches it. Respond with 1 if they match, 0 if they don't.", 'texttoimage': "Given the text, judge whether the image matches it. Respond with 1 if they match, 0 if they don't."}
—batch_sampler: batch_sampler
—multi_dataset_batch_sampler: proportional
—router_mapping: {}
—learning_rate_mapping: {}

</details>

Training Logs

Epoch	Step	Training Loss	image to text loss	text to image loss	doodles-image-to-text-eval_ndcg@10	doodles-text-to-image-eval_ndcg@10
0.1004	113	0.3237	-	-	-	-
0.2007	226	0.3331	-	-	-	-
0.2504	282	-	0.2869	0.2543	0.9037	0.6832
0.3011	339	0.2455	-	-	-	-
0.4014	452	0.2192	-	-	-	-
0.5009	564	-	0.1758	0.2778	0.9704	0.7909
0.5018	565	0.1695	-	-	-	-
0.6021	678	0.1765	-	-	-	-
0.7025	791	0.2051	-	-	-	-
0.7513	846	-	0.1583	0.2085	0.9869	0.8108
0.8028	904	0.1758	-	-	-	-
0.9032	1017	0.2113	-	-	-	-
1.0	1126	-	0.1462	0.1767	0.9869	0.8167
-1	-1	-	-	-	0.9869	0.8167

Training Time

—Training: 2.0 hours

Framework Versions

—Python: 3.12.13
—Sentence Transformers: 5.6.0
—Transformers: 5.13.1
—PyTorch: 2.11.0+cu128
—Accelerate: 1.14.0
—Datasets: 4.0.0
—Tokenizers: 0.22.2

Additional Resources

—Training and Finetuning Reranker Models with Sentence Transformers: the end-to-end guide for training or finetuning Cross Encoder (reranker) models.
—Multimodal Embedding & Reranker Models with Sentence Transformers: use text, image, audio, and video reranker models through the same API.
—Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers: training multimodal Cross Encoders.

Citation

BibTeX

Sentence Transformers

bibtex

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

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