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--- |
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language: |
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- multilingual |
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license: apache-2.0 |
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tags: |
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- sentence-transformers |
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- cross-encoder |
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- generated_from_trainer |
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- dataset_size:9632 |
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- loss:BinaryCrossEntropyLoss |
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base_model: FacebookAI/xlm-roberta-base |
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datasets: |
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- MercuraTech/reranker_10k |
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pipeline_tag: text-ranking |
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library_name: sentence-transformers |
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--- |
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# xlm-roberta-base fine-tuned on custom cross‑encoder dataset |
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This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the [reranker_10k](https://huggingface.co/datasets/MercuraTech/reranker_10k) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search. |
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## Model Details |
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### Model Description |
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- **Model Type:** Cross Encoder |
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- **Base model:** [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) <!-- at revision e73636d4f797dec63c3081bb6ed5c7b0bb3f2089 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Number of Output Labels:** 1 label |
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- **Training Dataset:** |
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- [reranker_10k](https://huggingface.co/datasets/MercuraTech/reranker_10k) |
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- **Language:** multilingual |
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- **License:** apache-2.0 |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder) |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import CrossEncoder |
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# Download from the 🤗 Hub |
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model = CrossEncoder("egerber1/xlm-roberta-crossencoder") |
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# Get scores for pairs of texts |
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pairs = [ |
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['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'], |
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['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'], |
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['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'], |
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['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'], |
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['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'], |
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] |
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scores = model.predict(pairs) |
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print(scores.shape) |
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# (5,) |
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# Or rank different texts based on similarity to a single text |
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ranks = model.rank( |
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'Elektroschweißband für Fixpunkt DN 70 Elektroschweißband für Fixpunkt - Zur Fixpunktbefestigung von Rohren in Verbindung mit Rohrschellen - Einteilig - Nennweite: DN 70 liefern und montieren', |
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[ |
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'Geberit PE Elektroschweißband für Fixpunkt DN70', |
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'Geberit Rohrschelle gedämmt Gewindemuffe M8/10 DN70', |
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'Geberit PE Steckmuffe mit Lippendichtung DN70', |
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'Geberit PE Elektroschweißband für Fixpunkt DN56', |
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'Geberit PE Elektroschweißband für Fixpunkt DN90', |
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] |
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) |
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# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### reranker_10k |
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* Dataset: [reranker_10k](https://huggingface.co/datasets/MercuraTech/reranker_10k) at [28cd3fd](https://huggingface.co/datasets/MercuraTech/reranker_10k/tree/28cd3fd3fae12373465efc6bdb89d3d39c9fdc1c) |
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* Size: 9,632 training samples |
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* Columns: <code>query</code>, <code>passage</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | query | passage | label | |
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|:--------|:--------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------| |
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| type | string | string | int | |
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| 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> | |
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* Samples: |
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| query | passage | label | |
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|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------|:---------------| |
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| <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> | |
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| <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> | |
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| <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> | |
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* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters: |
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```json |
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{ |
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"activation_fn": "torch.nn.modules.linear.Identity", |
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"pos_weight": 9.561403274536133 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 64 |
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- `learning_rate`: 2e-05 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `dataloader_num_workers`: 8 |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 64 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 3 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 8 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `tp_size`: 0 |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: proportional |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | |
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|:------:|:----:|:-------------:| |
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| 0.0033 | 1 | 0.8052 | |
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| 1.6611 | 500 | 1.2767 | |
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### Framework Versions |
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- Python: 3.9.5 |
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- Sentence Transformers: 4.1.0 |
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- Transformers: 4.51.3 |
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- PyTorch: 2.6.0+cu124 |
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- Accelerate: 1.6.0 |
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- Datasets: 3.5.0 |
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- Tokenizers: 0.21.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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<!-- |
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## Glossary |
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*Clearly define terms in order to be accessible across audiences.* |
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