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': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'image': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'video': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'message': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'message_format': 'auto', 'processing_kwargs': {'chat_template': {'add_generation_prompt': True}}, 'architecture': 'Qwen3_5Model'})
  (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'lasttoken', 'include_prompt': True})
  (2): Dense({'in_features': 1024, 'out_features': 1, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'module_input_name': 'sentence_embedding', 'module_output_name': 'scores'})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("tomaarsen/reranker-Qwen3.5-0.8B-doodles-image-text-to-text-pooling-dense")
# Get scores for pairs of inputs
pairs = [
    ['https://huggingface.co/tomaarsen/reranker-Qwen3.5-0.8B-doodles-image-text-to-text-pooling-dense/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/tomaarsen/reranker-Qwen3.5-0.8B-doodles-image-text-to-text-pooling-dense/resolve/main/assets/image_0.jpg', 'a grumpy character with a green head and pink hair wearing a light blue puffer, iridescent background'],
    ['https://huggingface.co/tomaarsen/reranker-Qwen3.5-0.8B-doodles-image-text-to-text-pooling-dense/resolve/main/assets/image_0.jpg', 'a surprised character with a pale head and green mullet hair wearing a blue backpack, purple background'],
    ['https://huggingface.co/tomaarsen/reranker-Qwen3.5-0.8B-doodles-image-text-to-text-pooling-dense/resolve/main/assets/image_0.jpg', 'a default character with a med head and green brushcut hair wearing a holographic sweater, gradient 4 background'],
    ['https://huggingface.co/tomaarsen/reranker-Qwen3.5-0.8B-doodles-image-text-to-text-pooling-dense/resolve/main/assets/image_0.jpg', 'a sunglasses character with a orange head and pink hair wearing a green hoodie, grey background'],
]
scores = model.predict(pairs)
print(scores)
# [0.7607 0.4866 0.5265 0.5412 0.4776]

Evaluation

Metrics

Cross Encoder Reranking

• Datasets: doodles-image-to-text-eval and doodles-text-to-image-eval
• Evaluated with CrossEncoderRerankingEvaluator with these parameters:

  {
      "at_k": 10
  }
  

Training Details

Training Datasets

image_to_text

• Dataset: image_to_text at a575ac6
• Size: 4,500 training samples
• Columns: image, text, and label
• Approximate statistics based on the first 1000 samples:

  	image	text	label
  type	image	string	int
  details	min: 128x128 px mean: 128x128 px max: 128x128 px	min: 29 tokens mean: 33.45 tokens max: 40 tokens	0: ~80.00% 1: ~20.00%

• Samples:

  image	text	label
  	a cobain glasses character with a gradient 2 head and purple puffballs hair wearing a white sweater, gradient 4 background	1
  	a content character with a orange head and purple long hair wearing a striped sweater, yellow background	0
  	a neutral note character with a orange head and green puffballs hair wearing a combo 2 puffer, light blue background	0

• Loss: BinaryCrossEntropyLoss with these parameters:

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

text_to_image

• Dataset: text_to_image at a575ac6
• Size: 4,500 training samples
• Columns: text, image, and label
• Approximate statistics based on the first 1000 samples:

  	text	image	label
  type	string	image	int
  details	min: 29 tokens mean: 33.51 tokens max: 40 tokens	min: 128x128 px mean: 128x128 px max: 128x128 px	0: ~80.00% 1: ~20.00%

• Samples:

  text	image	label
  a cobain glasses character with a gradient 2 head and purple puffballs hair wearing a white sweater, gradient 4 background		1
  a cobain glasses character with a gradient 2 head and purple puffballs hair wearing a white sweater, gradient 4 background		0
  a cobain glasses character with a gradient 2 head and purple puffballs hair wearing a white sweater, gradient 4 background		0

• Loss: BinaryCrossEntropyLoss with these parameters:

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

Evaluation Datasets

image_to_text

• Dataset: image_to_text at a575ac6
• Size: 500 evaluation samples
• Columns: image, text, and label
• Approximate statistics based on the first 500 samples:

  	image	text	label
  type	image	string	int
  details	min: 128x128 px mean: 128x128 px max: 128x128 px	min: 27 tokens mean: 33.05 tokens max: 38 tokens	0: ~80.00% 1: ~20.00%

• Samples:

  image	text	label
  	a content character with a tan head and purple puffballs hair wearing a blue fleece, green background	1
  	a grumpy character with a green head and pink hair wearing a light blue puffer, iridescent background	0
  	a surprised character with a pale head and green mullet hair wearing a blue backpack, purple background	0

• Loss: BinaryCrossEntropyLoss with these parameters:

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

text_to_image

• Dataset: text_to_image at a575ac6
• Size: 500 evaluation samples
• Columns: text, image, and label
• Approximate statistics based on the first 500 samples:

  	text	image	label
  type	string	image	int
  details	min: 27 tokens mean: 33.21 tokens max: 38 tokens	min: 128x128 px mean: 128x128 px max: 128x128 px	0: ~80.00% 1: ~20.00%

• Samples:

  text	image	label
  a content character with a tan head and purple puffballs hair wearing a blue fleece, green background		1
  a content character with a tan head and purple puffballs hair wearing a blue fleece, green background		0
  a content character with a tan head and purple puffballs hair wearing a blue fleece, green background		0

• Loss: BinaryCrossEntropyLoss with these parameters:

  {
      "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
• gradient_accumulation_steps: 4
• bf16: True
• eval_strategy: steps
• per_device_eval_batch_size: 32
• prompts: {'image_to_text': "Given the image, judge whether the text matches it. Respond with 1 if they match, 0 if they don't.", 'text_to_image': "Given the text, judge whether the image matches it. Respond with 1 if they match, 0 if they don't."}

All Hyperparameters

Click to expand

• 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: adamw_torch_fused
• 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: 4
• average_tokens_across_devices: True
• max_grad_norm: 1.0
• label_smoothing_factor: 0.0
• bf16: True
• fp16: False
• 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: trackio
• eval_strategy: steps
• per_device_eval_batch_size: 32
• 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: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': 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_backend: None
• ddp_timeout: 1800
• fsdp: []
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• deepspeed: None
• debug: []
• skip_memory_metrics: True
• do_predict: False
• resume_from_checkpoint: None
• warmup_ratio: None
• local_rank: -1
• prompts: {'image_to_text': "Given the image, judge whether the text matches it. Respond with 1 if they match, 0 if they don't.", 'text_to_image': "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: {}

Training Logs

Environmental Impact

Carbon emissions were measured using CodeCarbon.

• Energy Consumed: 0.290 kWh
• Carbon Emitted: 0.078 kg of CO2
• Hours Used: 1.025 hours

Training Hardware

• On Cloud: No
• GPU Model: 1 x NVIDIA GeForce RTX 3090
• CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
• RAM Size: 31.78 GB

Framework Versions

• Python: 3.11.6
• Sentence Transformers: 5.4.0.dev0
• Transformers: 5.3.0
• PyTorch: 2.10.0+cu128
• Accelerate: 1.13.0.dev0
• Datasets: 4.3.0
• Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers

@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",
}