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--- |
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language: |
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- hu |
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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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- reranker |
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- generated_from_trainer |
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- dataset_size:32113 |
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- loss:BinaryCrossEntropyLoss |
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- chemistry |
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base_model: GaborMadarasz/ModernBERT-base-hungarian |
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pipeline_tag: text-ranking |
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library_name: sentence-transformers |
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metrics: |
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- map |
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- mrr@10 |
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- ndcg@10 |
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model-index: |
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- name: ModernBERT-base trained on Chemistry |
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results: |
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- task: |
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type: cross-encoder-reranking |
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name: Cross Encoder Reranking |
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dataset: |
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name: chem dev |
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type: chem-dev |
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metrics: |
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- type: map |
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value: 0.4646 |
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name: Map |
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- type: mrr@10 |
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value: 0.4614 |
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name: Mrr@10 |
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- type: ndcg@10 |
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value: 0.4928 |
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name: Ndcg@10 |
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--- |
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# ModernBERT-base trained on Chemistry |
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This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [GaborMadarasz/ModernBERT-base-hungarian](https://huggingface.co/GaborMadarasz/ModernBERT-base-hungarian) 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:** [GaborMadarasz/ModernBERT-base-hungarian](https://huggingface.co/GaborMadarasz/ModernBERT-base-hungarian) <!-- at revision 32d70514a6587e31e23ff8ea3d0dc98bc61e42e4 --> |
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- **Maximum Sequence Length:** 8192 tokens |
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- **Number of Output Labels:** 1 label |
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<!-- - **Training Dataset:** Unknown --> |
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- **Language:** hu |
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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("GaborMadarasz/reranker-ModernBERT-base-hungarian") |
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# Get scores for pairs of texts |
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pairs = [ |
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['Milyen halmazállapotú a klór szobahőmérsékleten?', 'Gáz'], |
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['Milyen halmazállapotú a klór szobahőmérsékleten?', 'Gáz.'], |
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['Mi az izoméria fogalma?', 'Azonos összegképletű, de eltérő szerkezetű és tulajdonságú anyagok. '], |
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['Melyik elektronhéjon található a hidrogénatom egyetlen elektronja?', 'Az első héjon.'], |
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['Milyen felhasználási területei vannak a szilíciumnak?', 'Ötvözőelemként, tranzisztorok, integrált áramkörök, fényelemek előállítására.'], |
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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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'Milyen halmazállapotú a klór szobahőmérsékleten?', |
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[ |
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'Gáz', |
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'Gáz.', |
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'Azonos összegképletű, de eltérő szerkezetű és tulajdonságú anyagok. ', |
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'Az első héjon.', |
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'Ötvözőelemként, tranzisztorok, integrált áramkörök, fényelemek előállítására.', |
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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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## Evaluation |
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### Metrics |
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#### Cross Encoder Reranking |
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* Dataset: `chem-dev` |
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* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters: |
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```json |
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{ |
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"at_k": 10, |
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"always_rerank_positives": false |
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} |
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``` |
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| Metric | Value | |
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|:------------|:---------------------| |
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| map | 0.4646 (+0.0929) | |
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| mrr@10 | 0.4614 (+0.0966) | |
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| **ndcg@10** | **0.4928 (+0.0910)** | |
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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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<!-- |
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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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#### Unnamed Dataset |
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* Size: 32,113 training samples |
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* Columns: <code>query</code>, <code>answer</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | query | answer | label | |
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|:--------|:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:------------------------------------------------| |
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| type | string | string | int | |
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| details | <ul><li>min: 8 characters</li><li>mean: 52.3 characters</li><li>max: 159 characters</li></ul> | <ul><li>min: 1 characters</li><li>mean: 83.87 characters</li><li>max: 531 characters</li></ul> | <ul><li>0: ~69.80%</li><li>1: ~30.20%</li></ul> | |
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* Samples: |
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| query | answer | label | |
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|:--------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------| |
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| <code>Milyen halmazállapotú a klór szobahőmérsékleten?</code> | <code>Gáz</code> | <code>1</code> | |
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| <code>Milyen halmazállapotú a klór szobahőmérsékleten?</code> | <code>Gáz.</code> | <code>1</code> | |
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| <code>Mi az izoméria fogalma?</code> | <code>Azonos összegképletű, de eltérő szerkezetű és tulajdonságú anyagok. </code> | <code>1</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": 5 |
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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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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 2 |
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- `per_device_eval_batch_size`: 2 |
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- `gradient_accumulation_steps`: 8 |
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- `learning_rate`: 2e-05 |
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- `warmup_ratio`: 0.1 |
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- `seed`: 12 |
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- `dataloader_num_workers`: 2 |
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- `load_best_model_at_end`: True |
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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`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 2 |
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- `per_device_eval_batch_size`: 2 |
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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`: 8 |
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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`: 12 |
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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`: False |
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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`: 2 |
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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`: True |
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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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- `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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- `hub_revision`: None |
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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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- `liger_kernel_config`: None |
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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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- `router_mapping`: {} |
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- `learning_rate_mapping`: {} |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | chem-dev_ndcg@10 | |
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|:----------:|:--------:|:-------------:|:--------------------:| |
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| -1 | -1 | - | 0.1188 (-0.2831) | |
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| 0.0005 | 1 | 1.9222 | - | |
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| 0.0498 | 100 | 1.8084 | - | |
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| 0.0996 | 200 | 1.2947 | 0.2862 (-0.1157) | |
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| 0.1495 | 300 | 1.1573 | - | |
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| 0.1993 | 400 | 1.17 | 0.3567 (-0.0452) | |
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| 0.2491 | 500 | 1.0609 | - | |
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| 0.2989 | 600 | 1.01 | 0.3747 (-0.0272) | |
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| 0.3488 | 700 | 0.9806 | - | |
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| 0.3986 | 800 | 0.9208 | 0.3963 (-0.0056) | |
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| 0.4484 | 900 | 0.9022 | - | |
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| 0.4982 | 1000 | 0.8722 | 0.4106 (+0.0087) | |
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| 0.5480 | 1100 | 0.9325 | - | |
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| 0.5979 | 1200 | 0.768 | 0.4316 (+0.0298) | |
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| 0.6477 | 1300 | 0.8151 | - | |
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| 0.6975 | 1400 | 0.7569 | 0.4506 (+0.0487) | |
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| 0.7473 | 1500 | 0.7216 | - | |
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| 0.7972 | 1600 | 0.7571 | 0.4643 (+0.0625) | |
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| 0.8470 | 1700 | 0.6993 | - | |
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| 0.8968 | 1800 | 0.6709 | 0.4713 (+0.0694) | |
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| 0.9466 | 1900 | 0.7021 | - | |
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| 0.9965 | 2000 | 0.7693 | 0.4805 (+0.0787) | |
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| 1.0458 | 2100 | 0.5179 | - | |
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| 1.0957 | 2200 | 0.4932 | 0.4800 (+0.0781) | |
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| 1.1455 | 2300 | 0.5568 | - | |
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| 1.1953 | 2400 | 0.4191 | 0.4821 (+0.0803) | |
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| 1.2451 | 2500 | 0.4702 | - | |
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| 1.2949 | 2600 | 0.4126 | 0.4851 (+0.0833) | |
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| 1.3448 | 2700 | 0.4744 | - | |
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| 1.3946 | 2800 | 0.4404 | 0.4907 (+0.0888) | |
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| 1.4444 | 2900 | 0.4712 | - | |
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| 1.4942 | 3000 | 0.4382 | 0.4913 (+0.0894) | |
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| 1.5441 | 3100 | 0.5049 | - | |
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| 1.5939 | 3200 | 0.4714 | 0.4886 (+0.0868) | |
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| 1.6437 | 3300 | 0.3885 | - | |
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| 1.6935 | 3400 | 0.4361 | 0.4924 (+0.0906) | |
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| 1.7434 | 3500 | 0.4207 | - | |
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| **1.7932** | **3600** | **0.4384** | **0.4928 (+0.0910)** | |
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| 1.8430 | 3700 | 0.4187 | - | |
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| 1.8928 | 3800 | 0.4271 | 0.4937 (+0.0919) | |
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| 1.9426 | 3900 | 0.3581 | - | |
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| 1.9925 | 4000 | 0.3751 | 0.4910 (+0.0891) | |
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| 2.0419 | 4100 | 0.2494 | - | |
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| 2.0917 | 4200 | 0.2045 | 0.4869 (+0.0850) | |
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| 2.1415 | 4300 | 0.1532 | - | |
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| 2.1913 | 4400 | 0.1268 | 0.4838 (+0.0820) | |
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| 2.2411 | 4500 | 0.2108 | - | |
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| 2.2910 | 4600 | 0.2292 | 0.4889 (+0.0870) | |
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| 2.3408 | 4700 | 0.2154 | - | |
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| 2.3906 | 4800 | 0.1574 | 0.4921 (+0.0902) | |
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| 2.4404 | 4900 | 0.1677 | - | |
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| 2.4903 | 5000 | 0.1596 | 0.4826 (+0.0807) | |
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| 2.5401 | 5100 | 0.1456 | - | |
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| 2.5899 | 5200 | 0.2177 | 0.4867 (+0.0849) | |
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| 2.6397 | 5300 | 0.1227 | - | |
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| 2.6895 | 5400 | 0.1638 | 0.4880 (+0.0862) | |
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| 2.7394 | 5500 | 0.1192 | - | |
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| 2.7892 | 5600 | 0.2003 | 0.4848 (+0.0829) | |
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| 2.8390 | 5700 | 0.2717 | - | |
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| 2.8888 | 5800 | 0.1546 | 0.4841 (+0.0822) | |
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| 2.9387 | 5900 | 0.268 | - | |
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| 2.9885 | 6000 | 0.2253 | 0.4858 (+0.0840) | |
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| -1 | -1 | - | 0.4928 (+0.0910) | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 5.0.0 |
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- Transformers: 4.53.2 |
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- PyTorch: 2.7.0+cpu |
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- Accelerate: 1.6.0 |
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- Datasets: 3.2.0 |
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- Tokenizers: 0.21.2 |
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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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