damon6/reranker-cross-electra-ms-marco-german-uncased-shop_api_v3-bce

ModernBERT-base trained on GooAQ

This is a Cross Encoder model finetuned from svalabs/cross-electra-ms-marco-german-uncased 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: svalabs/cross-electra-ms-marco-german-uncased <!-- at revision eb0b2076c63e4adda8e550f16ea4fe347e761d81 -->
• —Maximum Sequence Length: 512 tokens
• —Number of Output Labels: 1 label <!-- - Training Dataset: Unknown -->
• —Language: de
• —License: apache-2.0

Model Sources

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

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("damon6/reranker-cross-electra-ms-marco-german-uncased-shop_api_v3-bce")
# Get scores for pairs of texts
pairs = [
    ['HPE ANW Networks Startup SVC U4832E Merkmal', 'HPE Care Pack Services sind leicht zu erwerben und zeichnen sich durch hohe Benutzerfreundlichkeit aus.'],
    ['HPE ANW Networks Startup SVC U4832E Merkmal', 'Dieses USB 2.0 Kabel von Delock dient zum Anschluss von verschiedenen USB Geräten, wie z. B. Drucker oder Scanner, an einen freien USB Port.'],
    ['HPE ANW Networks Startup SVC U4832E Merkmal', 'HPE ANW FC 1Y NBD EXCH 7220DC Contr SVC H3FQ1E.'],
    ['HPE ANW Networks Startup SVC U4832E Merkmal', 'Die SanDisk Extreme PRO Portable SSD ist eine robuste, zuverlässige Speicherlösung mit hoher SSD-Performance aus dem Hause SanDisk - der Marke, der professionelle Fotografen aus aller Welt vertrauen.'],
    ['HPE ANW Networks Startup SVC U4832E Merkmal', 'Farbe und schwarze Texte'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'HPE ANW Networks Startup SVC U4832E Merkmal',
    [
        'HPE Care Pack Services sind leicht zu erwerben und zeichnen sich durch hohe Benutzerfreundlichkeit aus.',
        'Dieses USB 2.0 Kabel von Delock dient zum Anschluss von verschiedenen USB Geräten, wie z. B. Drucker oder Scanner, an einen freien USB Port.',
        'HPE ANW FC 1Y NBD EXCH 7220DC Contr SVC H3FQ1E.',
        'Die SanDisk Extreme PRO Portable SSD ist eine robuste, zuverlässige Speicherlösung mit hoher SSD-Performance aus dem Hause SanDisk - der Marke, der professionelle Fotografen aus aller Welt vertrauen.',
        'Farbe und schwarze Texte',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

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Evaluation

Metrics

Cross Encoder Reranking

• —Dataset: shop_api_v3
• —Evaluated with <code>CrossEncoderRerankingEvaluator</code> with these parameters:

  {
      "at_k": 10,
      "always_rerank_positives": true
  }

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 38,267 training samples
• —Columns: <code>anchor</code>, <code>positive</code>, and <code>label</code>
• —Approximate statistics based on the first 1000 samples: | | anchor | positive | label | |:--------|:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | <ul><li>min: 27 characters</li><li>mean: 62.01 characters</li><li>max: 149 characters</li></ul> | <ul><li>min: 5 characters</li><li>mean: 123.87 characters</li><li>max: 2044 characters</li></ul> | <ul><li>0: ~83.00%</li><li>1: ~17.00%</li></ul> |
• —Samples: | anchor | positive | label | |:---------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | <code>HPE ANW Networks Startup SVC U4832E Merkmal</code> | <code>HPE Care Pack Services sind leicht zu erwerben und zeichnen sich durch hohe Benutzerfreundlichkeit aus.</code> | <code>1</code> | | <code>HPE ANW Networks Startup SVC U4832E Merkmal</code> | <code>Dieses USB 2.0 Kabel von Delock dient zum Anschluss von verschiedenen USB Geräten, wie z. B. Drucker oder Scanner, an einen freien USB Port.</code> | <code>0</code> | | <code>HPE ANW Networks Startup SVC U4832E Merkmal</code> | <code>HPE ANW FC 1Y NBD EXCH 7220DC Contr SVC H3FQ1E.</code> | <code>0</code> |
• —Loss: <code>BinaryCrossEntropyLoss</code> with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": 5
  }

Training Hyperparameters

Non-Default Hyperparameters

• —eval_strategy: steps
• —per_device_train_batch_size: 16
• —per_device_eval_batch_size: 16
• —learning_rate: 2e-05
• —num_train_epochs: 2
• —warmup_ratio: 0.1
• —seed: 12
• —bf16: True
• —dataloader_num_workers: 4
• —load_best_model_at_end: True

All Hyperparameters

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

• —overwrite_output_dir: False
• —do_predict: False
• —eval_strategy: steps
• —prediction_loss_only: True
• —per_device_train_batch_size: 16
• —per_device_eval_batch_size: 16
• —per_gpu_train_batch_size: None
• —per_gpu_eval_batch_size: None
• —gradient_accumulation_steps: 1
• —eval_accumulation_steps: None
• —torch_empty_cache_steps: None
• —learning_rate: 2e-05
• —weight_decay: 0.0
• —adam_beta1: 0.9
• —adam_beta2: 0.999
• —adam_epsilon: 1e-08
• —max_grad_norm: 1.0
• —num_train_epochs: 2
• —max_steps: -1
• —lr_scheduler_type: linear
• —lr_scheduler_kwargs: {}
• —warmup_ratio: 0.1
• —warmup_steps: 0
• —log_level: passive
• —log_level_replica: warning
• —log_on_each_node: True
• —logging_nan_inf_filter: True
• —save_safetensors: True
• —save_on_each_node: False
• —save_only_model: False
• —restore_callback_states_from_checkpoint: False
• —no_cuda: False
• —use_cpu: False
• —use_mps_device: False
• —seed: 12
• —data_seed: None
• —jit_mode_eval: False
• —use_ipex: False
• —bf16: True
• —fp16: False
• —fp16_opt_level: O1
• —half_precision_backend: auto
• —bf16_full_eval: False
• —fp16_full_eval: False
• —tf32: None
• —local_rank: 0
• —ddp_backend: None
• —tpu_num_cores: None
• —tpu_metrics_debug: False
• —debug: []
• —dataloader_drop_last: False
• —dataloader_num_workers: 4
• —dataloader_prefetch_factor: None
• —past_index: -1
• —disable_tqdm: False
• —remove_unused_columns: True
• —label_names: None
• —load_best_model_at_end: True
• —ignore_data_skip: False
• —fsdp: []
• —fsdp_min_num_params: 0
• —fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —tp_size: 0
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —deepspeed: None
• —label_smoothing_factor: 0.0
• —optim: adamw_torch
• —optim_args: None
• —adafactor: False
• —group_by_length: False
• —length_column_name: length
• —ddp_find_unused_parameters: None
• —ddp_bucket_cap_mb: None
• —ddp_broadcast_buffers: False
• —dataloader_pin_memory: True
• —dataloader_persistent_workers: False
• —skip_memory_metrics: True
• —use_legacy_prediction_loop: False
• —push_to_hub: False
• —resume_from_checkpoint: None
• —hub_model_id: None
• —hub_strategy: every_save
• —hub_private_repo: None
• —hub_always_push: False
• —gradient_checkpointing: False
• —gradient_checkpointing_kwargs: None
• —include_inputs_for_metrics: False
• —include_for_metrics: []
• —eval_do_concat_batches: True
• —fp16_backend: auto
• —push_to_hub_model_id: None
• —push_to_hub_organization: None
• —mp_parameters:
• —auto_find_batch_size: False
• —full_determinism: False
• —torchdynamo: None
• —ray_scope: last
• —ddp_timeout: 1800
• —torch_compile: False
• —torch_compile_backend: None
• —torch_compile_mode: None
• —include_tokens_per_second: False
• —include_num_input_tokens_seen: False
• —neftune_noise_alpha: None
• —optim_target_modules: None
• —batch_eval_metrics: False
• —eval_on_start: False
• —use_liger_kernel: False
• —eval_use_gather_object: False
• —average_tokens_across_devices: False
• —prompts: None
• —batch_sampler: batch_sampler
• —multi_dataset_batch_sampler: proportional

</details>

Training Logs

• —The bold row denotes the saved checkpoint.

Framework Versions

• —Python: 3.10.16
• —Sentence Transformers: 4.1.0
• —Transformers: 4.51.3
• —PyTorch: 2.6.0+cu124
• —Accelerate: 1.7.0
• —Datasets: 3.6.0
• —Tokenizers: 0.21.1

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",
}

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