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egerber1/xlm-roberta-crossencoder

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face

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Model Card

xlm-roberta-base fine-tuned on custom cross‑encoder dataset

This is a Cross Encoder model finetuned from FacebookAI/xlm-roberta-base on the reranker_10k dataset 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: FacebookAI/xlm-roberta-base
—Maximum Sequence Length: 512 tokens
—Number of Output Labels: 1 label
—Training Dataset:
—reranker_10k
—Language: multilingual
—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:

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("egerber1/xlm-roberta-crossencoder")
# Get scores for pairs of texts
pairs = [
    ['Elektroschweißband für Fixpunkt DN 70 Elektroschweißband für Fixpunkt - Zur Fixpunktbefestigung von Rohren in Verbindung mit Rohrschellen - Einteilig - Nennweite: DN 70 liefern und montieren', 'Geberit PE Elektroschweißband für Fixpunkt DN70'],
    ['Elektroschweißband für Fixpunkt DN 70 Elektroschweißband für Fixpunkt - Zur Fixpunktbefestigung von Rohren in Verbindung mit Rohrschellen - Einteilig - Nennweite: DN 70 liefern und montieren', 'Geberit Rohrschelle gedämmt Gewindemuffe M8/10 DN70'],
    ['Elektroschweißband für Fixpunkt DN 70 Elektroschweißband für Fixpunkt - Zur Fixpunktbefestigung von Rohren in Verbindung mit Rohrschellen - Einteilig - Nennweite: DN 70 liefern und montieren', 'Geberit PE Steckmuffe mit Lippendichtung DN70'],
    ['Elektroschweißband für Fixpunkt DN 70 Elektroschweißband für Fixpunkt - Zur Fixpunktbefestigung von Rohren in Verbindung mit Rohrschellen - Einteilig - Nennweite: DN 70 liefern und montieren', 'Geberit PE Elektroschweißband für Fixpunkt DN56'],
    ['Elektroschweißband für Fixpunkt DN 70 Elektroschweißband für Fixpunkt - Zur Fixpunktbefestigung von Rohren in Verbindung mit Rohrschellen - Einteilig - Nennweite: DN 70 liefern und montieren', 'Geberit PE Elektroschweißband für Fixpunkt DN90'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'Elektroschweißband für Fixpunkt DN 70 Elektroschweißband für Fixpunkt - Zur Fixpunktbefestigung von Rohren in Verbindung mit Rohrschellen - Einteilig - Nennweite: DN 70 liefern und montieren',
    [
        'Geberit PE Elektroschweißband für Fixpunkt DN70',
        'Geberit Rohrschelle gedämmt Gewindemuffe M8/10 DN70',
        'Geberit PE Steckmuffe mit Lippendichtung DN70',
        'Geberit PE Elektroschweißband für Fixpunkt DN56',
        'Geberit PE Elektroschweißband für Fixpunkt DN90',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

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Downstream Usage (Sentence Transformers)

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Training Details

Training Dataset

reranker_10k

—Dataset: reranker_10k at 28cd3fd
—Size: 9,632 training samples
—Columns: <code>query</code>, <code>passage</code>, and <code>label</code>
—Approximate statistics based on the first 1000 samples: | | query | passage | label | |:--------|:--------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------| | type | string | string | int | | details | <ul><li>min: 23 characters</li><li>mean: 326.49 characters</li><li>max: 1733 characters</li></ul> | <ul><li>min: 21 characters</li><li>mean: 58.05 characters</li><li>max: 81 characters</li></ul> | <ul><li>0: ~90.40%</li><li>1: ~9.60%</li></ul> |
—Samples: | query | passage | label | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------|:---------------| | <code>Elektroschweißband für Fixpunkt DN 70 Elektroschweißband für Fixpunkt - Zur Fixpunktbefestigung von Rohren in Verbindung mit Rohrschellen - Einteilig - Nennweite: DN 70 liefern und montieren</code> | <code>Geberit PE Elektroschweißband für Fixpunkt DN70</code> | <code>1</code> | | <code>Elektroschweißband für Fixpunkt DN 70 Elektroschweißband für Fixpunkt - Zur Fixpunktbefestigung von Rohren in Verbindung mit Rohrschellen - Einteilig - Nennweite: DN 70 liefern und montieren</code> | <code>Geberit Rohrschelle gedämmt Gewindemuffe M8/10 DN70</code> | <code>0</code> | | <code>Elektroschweißband für Fixpunkt DN 70 Elektroschweißband für Fixpunkt - Zur Fixpunktbefestigung von Rohren in Verbindung mit Rohrschellen - Einteilig - Nennweite: DN 70 liefern und montieren</code> | <code>Geberit PE Steckmuffe mit Lippendichtung DN70</code> | <code>0</code> |
—Loss: <code>BinaryCrossEntropyLoss</code> with these parameters:

json

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": 9.561403274536133
  }

Training Hyperparameters

Non-Default Hyperparameters

—per_device_train_batch_size: 32
—per_device_eval_batch_size: 64
—learning_rate: 2e-05
—warmup_ratio: 0.1
—fp16: True
—dataloader_num_workers: 8

All Hyperparameters

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

—overwrite_output_dir: False
—do_predict: False
—eval_strategy: no
—prediction_loss_only: True
—per_device_train_batch_size: 32
—per_device_eval_batch_size: 64
—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: 3
—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: 42
—data_seed: None
—jit_mode_eval: False
—use_ipex: False
—bf16: False
—fp16: True
—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: 8
—dataloader_prefetch_factor: None
—past_index: -1
—disable_tqdm: False
—remove_unused_columns: True
—label_names: None
—load_best_model_at_end: False
—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

Epoch	Step	Training Loss
0.0033	1	0.8052
1.6611	500	1.2767

Framework Versions

—Python: 3.9.5
—Sentence Transformers: 4.1.0
—Transformers: 4.51.3
—PyTorch: 2.6.0+cu124
—Accelerate: 1.6.0
—Datasets: 3.5.0
—Tokenizers: 0.21.1

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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