---
language:
- multilingual
- fr
- de
- zh
- ru
- pl
- es
- it
- ja
- ar
- hi
- pt
- nl
license: mit
tags:
- sentence-transformers
- cross-encoder
- reranker
- generated_from_trainer
- dataset_size:9358
- loss:LambdaLoss
base_model: jhu-clsp/mmBERT-small
datasets:
- Antix5/Product_Similarity_Dataset
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
- map
- mrr@10
- ndcg@10
model-index:
- name: mmBERT-small reranker (LambdaLoss NDCG2++)
results:
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: rerank
type: rerank
metrics:
- type: map
value: 0.9562
name: Map
- type: mrr@10
value: 0.9561
name: Mrr@10
- type: ndcg@10
value: 0.9656
name: Ndcg@10
---
# mmBERT-small reranker (LambdaLoss NDCG2++)
This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [jhu-clsp/mmBERT-small](https://huggingface.co/jhu-clsp/mmBERT-small) on the [product_similarity_dataset](https://huggingface.co/datasets/Antix5/Product_Similarity_Dataset) 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.
## Model Details
### Model Description
- **Model Type:** Cross Encoder
- **Base model:** [jhu-clsp/mmBERT-small](https://huggingface.co/jhu-clsp/mmBERT-small)
- **Maximum Sequence Length:** 256 tokens
- **Number of Output Labels:** 1 label
- **Training Dataset:**
- [product_similarity_dataset](https://huggingface.co/datasets/Antix5/Product_Similarity_Dataset)
- **Languages:** multilingual, fr, de, zh, ru, pl, es, it, ja, ar, hi, pt, nl
- **License:** mit
### 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)
**Warning : This model is just starting training, this is just a checkpoint**
## 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("Antix5/product-reranker-mmBERT-small")
# Get scores for pairs of texts
pairs = [
['Milk Belgian Chocolate', 'Milk Chocolate Flavor'],
]
scores = model.predict(pairs)
print(scores.shape)
# (3,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'70 % Cacao Dark Chocolate With Coconut',
[
'DRK CHCLT BAR, COCONUT',
'Coconut Cream Filled Dark Chocolate',
'Blueberry & Dark Chocolate With Chia',
]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
```
## Evaluation
### Metrics
#### Cross Encoder Reranking
* Dataset: `rerank`
* Evaluated with [CrossEncoderRerankingEvaluator](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
```json
{
"at_k": 10,
"always_rerank_positives": false
}
```
| Metric | Value |
|:------------|:---------------------|
| map | 0.9562 (-0.0359) |
| mrr@10 | 0.9561 (-0.0385) |
| **ndcg@10** | **0.9656 (-0.0291)** |
## Training Details
### Training Dataset
#### product_similarity_dataset
* Dataset: [product_similarity_dataset](https://huggingface.co/datasets/Antix5/Product_Similarity_Dataset) at [7aba3ef](https://huggingface.co/datasets/Antix5/Product_Similarity_Dataset/tree/7aba3ef7a1d4bb067b26c4cafb376f7447850314)
* Size: 9,358 training samples
* Columns: query, documents, and scores
* Approximate statistics based on the first 1000 samples:
| | query | documents | scores |
|:--------|:-----------------------------------------------------------------------------------------------|:------------------------------------|:------------------------------------|
| type | string | list | list |
| details | - min: 6 characters
- mean: 57.18 characters
- max: 197 characters
| | |
* Samples:
| query | documents | scores |
|:--------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------|
| Premier 26764 Car Spinner, Santa, 25 by 19-1/2-Inch | ['Premier 26764 Tourbillon pour voiture, Santa, 25 x 19-1/2 pouces', 'BNTS, ЧИПСЫ ИЗ ФАСОЛИ NV И МОРСКАЯ СОЛЬ', 'Beanitos, Чипс из фасоли navy, Сыр на чо', 'K2 स्केट व्हील (4 का पैक)', 'BLST BALL МЯЧ ДЛЯ КИКА (2 ШТ.)', ...] | [1.0, 0.0, 0.0, 0.0, 0.0, ...] |
| Juice Cocktail Blend From Concentrate, Apple Blueberry | ['Mélange de cocktail de jus à base de concentré, pomme myrtille', 'Orange Juice From Concentrate With Pulp', 'Tropical Juice Splash From Concentrate', 'BLUEBERRY JUICE DRNK', 'APPLE NECTAR JUICE DRINK FROM CNCNTRT', ...] | [1.0, 0.4, 0.35, 0.65, 0.55, ...] |
| Fruity Sour Strips Fruit-Flavored Chewy Candy | ['Fruity Sour Strips Fruit-Flavored Chewy Candy', 'SR CANDIES, FRUIT SOUR', 'Fruit Candy, Fruit', 'FRT SNCK TUTTI FRUITY', 'Fruit Strips, Peach Passion', ...] | [1.0, 0.95, 0.7, 0.55, 0.9, ...] |
* Loss: [LambdaLoss](https://sbert.net/docs/package_reference/cross_encoder/losses.html#lambdaloss) with these parameters:
```json
{
"weighting_scheme": "sentence_transformers.cross_encoder.losses.LambdaLoss.NDCGLoss2PPScheme",
"k": null,
"sigma": 1.0,
"eps": 1e-10,
"reduction_log": "binary",
"activation_fn": "torch.nn.modules.linear.Identity",
"mini_batch_size": null
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `gradient_checkpointing`: True
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 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`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `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}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `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
- `hub_revision`: None
- `gradient_checkpointing`: True
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}