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---
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language:
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- en
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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:100000
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- loss:CrossEntropyLoss
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base_model: distilbert/distilroberta-base
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datasets:
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- sentence-transformers/all-nli
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pipeline_tag: text-classification
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library_name: sentence-transformers
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metrics:
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- f1_macro
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- f1_micro
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- f1_weighted
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co2_eq_emissions:
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emissions: 4.04344552694739
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energy_consumed: 0.010402430465877176
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source: codecarbon
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training_type: fine-tuning
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on_cloud: false
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cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
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ram_total_size: 31.777088165283203
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hours_used: 0.037
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hardware_used: 1 x NVIDIA GeForce RTX 3090
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model-index:
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- name: CrossEncoder based on distilbert/distilroberta-base
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results:
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- task:
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type: cross-encoder-classification
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name: Cross Encoder Classification
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dataset:
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name: AllNLI dev
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type: AllNLI-dev
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metrics:
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- type: f1_macro
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value: 0.8572064017289116
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name: F1 Macro
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- type: f1_micro
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value: 0.858
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name: F1 Micro
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- type: f1_weighted
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value: 0.8571688195967522
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name: F1 Weighted
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- task:
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type: cross-encoder-classification
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name: Cross Encoder Classification
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dataset:
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name: AllNLI test
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type: AllNLI-test
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metrics:
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- type: f1_macro
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value: 0.7750916004999927
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name: F1 Macro
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- type: f1_micro
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value: 0.7755392755392755
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name: F1 Micro
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- type: f1_weighted
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value: 0.7759677200472417
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name: F1 Weighted
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---
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# CrossEncoder based on distilbert/distilroberta-base
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This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text pair classification.
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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:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b -->
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- **Maximum Sequence Length:** 514 tokens
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- **Number of Output Labels:** 3 labels
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- **Training Dataset:**
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- [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
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- **Language:** en
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<!-- - **License:** Unknown -->
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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("tomaarsen/reranker-distilroberta-base-nli")
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# Get scores for pairs of texts
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pairs = [
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['Two women are embracing while holding to go packages.', 'The sisters are hugging goodbye while holding to go packages after just eating lunch.'],
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['Two women are embracing while holding to go packages.', 'Two woman are holding packages.'],
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['Two women are embracing while holding to go packages.', 'The men are fighting outside a deli.'],
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['Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.', 'Two kids in numbered jerseys wash their hands.'],
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['Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.', 'Two kids at a ballgame wash their hands.'],
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]
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scores = model.predict(pairs)
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print(scores.shape)
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# (5, 3)
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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 Classification
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* Datasets: `AllNLI-dev` and `AllNLI-test`
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* Evaluated with [<code>CrossEncoderClassificationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderClassificationEvaluator)
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| Metric | AllNLI-dev | AllNLI-test |
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|:-------------|:-----------|:------------|
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| **f1_macro** | **0.8572** | **0.7751** |
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| f1_micro | 0.858 | 0.7755 |
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| f1_weighted | 0.8572 | 0.776 |
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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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#### all-nli
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* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
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* Size: 100,000 training samples
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* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| | premise | hypothesis | 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: 69.54 characters</li><li>max: 227 characters</li></ul> | <ul><li>min: 11 characters</li><li>mean: 38.26 characters</li><li>max: 131 characters</li></ul> | <ul><li>0: ~33.40%</li><li>1: ~33.30%</li><li>2: ~33.30%</li></ul> |
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* Samples:
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| premise | hypothesis | label |
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|:--------------------------------------------------------------------|:---------------------------------------------------------------|:---------------|
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| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is training his horse for a competition.</code> | <code>1</code> |
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| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is at a diner, ordering an omelette.</code> | <code>2</code> |
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| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>0</code> |
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* Loss: [<code>CrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#crossentropyloss)
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### Evaluation Dataset
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#### all-nli
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* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
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* Size: 1,000 evaluation samples
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* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| | premise | hypothesis | label |
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|:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------|
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| type | string | string | int |
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| details | <ul><li>min: 16 characters</li><li>mean: 75.01 characters</li><li>max: 229 characters</li></ul> | <ul><li>min: 11 characters</li><li>mean: 37.66 characters</li><li>max: 116 characters</li></ul> | <ul><li>0: ~33.10%</li><li>1: ~33.30%</li><li>2: ~33.60%</li></ul> |
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* Samples:
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| premise | hypothesis | label |
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|:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------|
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| <code>Two women are embracing while holding to go packages.</code> | <code>The sisters are hugging goodbye while holding to go packages after just eating lunch.</code> | <code>1</code> |
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| <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>0</code> |
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| <code>Two women are embracing while holding to go packages.</code> | <code>The men are fighting outside a deli.</code> | <code>2</code> |
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* Loss: [<code>CrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#crossentropyloss)
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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`: 64
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- `per_device_eval_batch_size`: 64
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- `num_train_epochs`: 1
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- `warmup_ratio`: 0.1
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- `bf16`: 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`: 64
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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`: 5e-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`: 1
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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`: True
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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`: 0
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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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- `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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- `dispatch_batches`: None
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- `split_batches`: 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 | Validation Loss | AllNLI-dev_f1_macro | AllNLI-test_f1_macro |
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|:------:|:----:|:-------------:|:---------------:|:-------------------:|:--------------------:|
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| -1 | -1 | - | - | 0.1775 | - |
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| 0.0640 | 100 | 1.0464 | - | - | - |
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| 0.1280 | 200 | 0.702 | - | - | - |
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| 0.1919 | 300 | 0.6039 | - | - | - |
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| 0.2559 | 400 | 0.5658 | - | - | - |
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| 0.3199 | 500 | 0.5513 | 0.4792 | 0.7932 | - |
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| 0.3839 | 600 | 0.523 | - | - | - |
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| 0.4479 | 700 | 0.5261 | - | - | - |
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| 0.5118 | 800 | 0.5074 | - | - | - |
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| 0.5758 | 900 | 0.4871 | - | - | - |
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| 0.6398 | 1000 | 0.5078 | 0.3934 | 0.8407 | - |
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| 0.7038 | 1100 | 0.4706 | - | - | - |
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| 0.7678 | 1200 | 0.4725 | - | - | - |
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| 0.8317 | 1300 | 0.4362 | - | - | - |
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| 0.8957 | 1400 | 0.4577 | - | - | - |
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| 0.9597 | 1500 | 0.4415 | 0.3599 | 0.8572 | - |
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| -1 | -1 | - | - | - | 0.7751 |
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### Environmental Impact
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Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
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- **Energy Consumed**: 0.010 kWh
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- **Carbon Emitted**: 0.004 kg of CO2
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- **Hours Used**: 0.037 hours
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### Training Hardware
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- **On Cloud**: No
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- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
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- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
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- **RAM Size**: 31.78 GB
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### Framework Versions
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- Python: 3.11.6
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- Sentence Transformers: 3.5.0.dev0
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- Transformers: 4.49.0
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- PyTorch: 2.6.0+cu124
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- Accelerate: 1.5.1
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- Datasets: 3.3.2
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- Tokenizers: 0.21.0
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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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-->
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## Model Card Authors
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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## Model Card Contact
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