---
language:
- en
tags:
- sentence-transformers
- cross-encoder
- generated_from_trainer
- dataset_size:100000
- loss:CrossEntropyLoss
base_model: distilbert/distilroberta-base
datasets:
- sentence-transformers/all-nli
pipeline_tag: text-classification
library_name: sentence-transformers
metrics:
- f1_macro
- f1_micro
- f1_weighted
co2_eq_emissions:
emissions: 4.04344552694739
energy_consumed: 0.010402430465877176
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.037
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: CrossEncoder based on distilbert/distilroberta-base
results:
- task:
type: cross-encoder-classification
name: Cross Encoder Classification
dataset:
name: AllNLI dev
type: AllNLI-dev
metrics:
- type: f1_macro
value: 0.8572064017289116
name: F1 Macro
- type: f1_micro
value: 0.858
name: F1 Micro
- type: f1_weighted
value: 0.8571688195967522
name: F1 Weighted
- task:
type: cross-encoder-classification
name: Cross Encoder Classification
dataset:
name: AllNLI test
type: AllNLI-test
metrics:
- type: f1_macro
value: 0.7750916004999927
name: F1 Macro
- type: f1_micro
value: 0.7755392755392755
name: F1 Micro
- type: f1_weighted
value: 0.7759677200472417
name: F1 Weighted
---
# CrossEncoder based on distilbert/distilroberta-base
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.
## Model Details
### Model Description
- **Model Type:** Cross Encoder
- **Base model:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b -->
- **Maximum Sequence Length:** 514 tokens
- **Number of Output Labels:** 3 labels
- **Training Dataset:**
- [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import CrossEncoder
# Download from the 🤗 Hub
model = CrossEncoder("tomaarsen/reranker-distilroberta-base-nli")
# Get scores for pairs of texts
pairs = [
['Two women are embracing while holding to go packages.', 'The sisters are hugging goodbye while holding to go packages after just eating lunch.'],
['Two women are embracing while holding to go packages.', 'Two woman are holding packages.'],
['Two women are embracing while holding to go packages.', 'The men are fighting outside a deli.'],
['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.'],
['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.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5, 3)
```
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## Evaluation
### Metrics
#### Cross Encoder Classification
* Datasets: `AllNLI-dev` and `AllNLI-test`
* Evaluated with [<code>CrossEncoderClassificationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderClassificationEvaluator)
| Metric | AllNLI-dev | AllNLI-test |
|:-------------|:-----------|:------------|
| **f1_macro** | **0.8572** | **0.7751** |
| f1_micro | 0.858 | 0.7755 |
| f1_weighted | 0.8572 | 0.776 |
<!--
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## Training Details
### Training Dataset
#### all-nli
* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 100,000 training samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | premise | hypothesis | label |
|:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------|
| type | string | string | int |
| 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> |
* Samples:
| premise | hypothesis | label |
|:--------------------------------------------------------------------|:---------------------------------------------------------------|:---------------|
| <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> |
| <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> |
| <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> |
* Loss: [<code>CrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#crossentropyloss)
### Evaluation Dataset
#### all-nli
* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 1,000 evaluation samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | premise | hypothesis | label |
|:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------|
| type | string | string | int |
| 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> |
* Samples:
| premise | hypothesis | label |
|:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------|
| <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> |
| <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>0</code> |
| <code>Two women are embracing while holding to go packages.</code> | <code>The men are fighting outside a deli.</code> | <code>2</code> |
* Loss: [<code>CrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#crossentropyloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `bf16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | AllNLI-dev_f1_macro | AllNLI-test_f1_macro |
|:------:|:----:|:-------------:|:---------------:|:-------------------:|:--------------------:|
| -1 | -1 | - | - | 0.1775 | - |
| 0.0640 | 100 | 1.0464 | - | - | - |
| 0.1280 | 200 | 0.702 | - | - | - |
| 0.1919 | 300 | 0.6039 | - | - | - |
| 0.2559 | 400 | 0.5658 | - | - | - |
| 0.3199 | 500 | 0.5513 | 0.4792 | 0.7932 | - |
| 0.3839 | 600 | 0.523 | - | - | - |
| 0.4479 | 700 | 0.5261 | - | - | - |
| 0.5118 | 800 | 0.5074 | - | - | - |
| 0.5758 | 900 | 0.4871 | - | - | - |
| 0.6398 | 1000 | 0.5078 | 0.3934 | 0.8407 | - |
| 0.7038 | 1100 | 0.4706 | - | - | - |
| 0.7678 | 1200 | 0.4725 | - | - | - |
| 0.8317 | 1300 | 0.4362 | - | - | - |
| 0.8957 | 1400 | 0.4577 | - | - | - |
| 0.9597 | 1500 | 0.4415 | 0.3599 | 0.8572 | - |
| -1 | -1 | - | - | - | 0.7751 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.010 kWh
- **Carbon Emitted**: 0.004 kg of CO2
- **Hours Used**: 0.037 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.5.0.dev0
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.1
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
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