|
|
--- |
|
|
tags: |
|
|
- sentence-transformers |
|
|
- sparse-encoder |
|
|
- sparse |
|
|
- splade |
|
|
- generated_from_trainer |
|
|
- dataset_size:1000000 |
|
|
- loss:SpladeLoss |
|
|
- loss:SparseMarginMSELoss |
|
|
- loss:FlopsLoss |
|
|
base_model: |
|
|
- prajjwal1/bert-mini |
|
|
widget: |
|
|
- text: >- |
|
|
Caffeine is a central nervous system stimulant. It works by stimulating the |
|
|
brain. Caffeine is found naturally in foods and beverages such as coffee, |
|
|
tea, colas, energy and chocolate. Botanical sources of caffeine include kola |
|
|
nuts, guarana, and yerba mate. |
|
|
- text: >- |
|
|
Tim Hardaway, Jr. Compared To My 5ft 10in (177cm) Height. Tim Hardaway, |
|
|
Jr.'s height is 6ft 6in or 198cm while I am 5ft 10in or 177cm. I am shorter |
|
|
compared to him. To find out how much shorter I am, we would have to |
|
|
subtract my height from Tim Hardaway, Jr.'s height. Therefore I am shorter |
|
|
to him for about 21cm. |
|
|
- text: benefits of honey and lemon |
|
|
- text: >- |
|
|
How To Cook Corn on the Cob in the Microwave What You Need. Ingredients 1 or |
|
|
more ears fresh, un-shucked sweet corn Equipment Microwave Cooling rack or |
|
|
cutting board Instructions. Place 1 to 4 ears of corn in the microwave: |
|
|
Arrange 1 to 4 ears of corn, un-shucked, in the microwave. If you prefer, |
|
|
you can set them on a microwaveable plate or tray. If you need to cook more |
|
|
than 4 ears of corn, cook them in batches. Microwave for 3 to 5 minutes: For |
|
|
just 1 or 2 ears of corn, microwave for 3 minutes. For 3 or 4 ears, |
|
|
microwave for 4 minutes. If you like softer corn or if your ears are |
|
|
particularly large, microwave for an additional minute. |
|
|
- text: >- |
|
|
The law recognizes two basic kinds of warrantiesimplied warranties and |
|
|
express warranties. Implied Warranties. Implied warranties are unspoken, |
|
|
unwritten promises, created by state law, that go from you, as a seller or |
|
|
merchant, to your customers. |
|
|
pipeline_tag: feature-extraction |
|
|
library_name: sentence-transformers |
|
|
metrics: |
|
|
- dot_accuracy@1 |
|
|
- dot_accuracy@3 |
|
|
- dot_accuracy@5 |
|
|
- dot_accuracy@10 |
|
|
- dot_precision@1 |
|
|
- dot_precision@3 |
|
|
- dot_precision@5 |
|
|
- dot_precision@10 |
|
|
- dot_recall@1 |
|
|
- dot_recall@3 |
|
|
- dot_recall@5 |
|
|
- dot_recall@10 |
|
|
- dot_ndcg@10 |
|
|
- dot_mrr@10 |
|
|
- dot_map@100 |
|
|
- query_active_dims |
|
|
- query_sparsity_ratio |
|
|
- corpus_active_dims |
|
|
- corpus_sparsity_ratio |
|
|
model-index: |
|
|
- name: SPLADE Sparse Encoder |
|
|
results: |
|
|
- task: |
|
|
type: sparse-information-retrieval |
|
|
name: Sparse Information Retrieval |
|
|
dataset: |
|
|
name: Unknown |
|
|
type: unknown |
|
|
metrics: |
|
|
- type: dot_accuracy@1 |
|
|
value: 0.5018 |
|
|
name: Dot Accuracy@1 |
|
|
- type: dot_accuracy@3 |
|
|
value: 0.8286 |
|
|
name: Dot Accuracy@3 |
|
|
- type: dot_accuracy@5 |
|
|
value: 0.9194 |
|
|
name: Dot Accuracy@5 |
|
|
- type: dot_accuracy@10 |
|
|
value: 0.9746 |
|
|
name: Dot Accuracy@10 |
|
|
- type: dot_precision@1 |
|
|
value: 0.5018 |
|
|
name: Dot Precision@1 |
|
|
- type: dot_precision@3 |
|
|
value: 0.2839333333333333 |
|
|
name: Dot Precision@3 |
|
|
- type: dot_precision@5 |
|
|
value: 0.19103999999999996 |
|
|
name: Dot Precision@5 |
|
|
- type: dot_precision@10 |
|
|
value: 0.10255999999999998 |
|
|
name: Dot Precision@10 |
|
|
- type: dot_recall@1 |
|
|
value: 0.4867666666666667 |
|
|
name: Dot Recall@1 |
|
|
- type: dot_recall@3 |
|
|
value: 0.81485 |
|
|
name: Dot Recall@3 |
|
|
- type: dot_recall@5 |
|
|
value: 0.9096166666666667 |
|
|
name: Dot Recall@5 |
|
|
- type: dot_recall@10 |
|
|
value: 0.9709333333333334 |
|
|
name: Dot Recall@10 |
|
|
- type: dot_ndcg@10 |
|
|
value: 0.7457042059559617 |
|
|
name: Dot Ndcg@10 |
|
|
- type: dot_mrr@10 |
|
|
value: 0.6749323809523842 |
|
|
name: Dot Mrr@10 |
|
|
- type: dot_map@100 |
|
|
value: 0.670785161566693 |
|
|
name: Dot Map@100 |
|
|
- type: query_active_dims |
|
|
value: 22.584999084472656 |
|
|
name: Query Active Dims |
|
|
- type: query_sparsity_ratio |
|
|
value: 0.9992600419669592 |
|
|
name: Query Sparsity Ratio |
|
|
- type: corpus_active_dims |
|
|
value: 174.85202722777373 |
|
|
name: Corpus Active Dims |
|
|
- type: corpus_sparsity_ratio |
|
|
value: 0.9942712788405814 |
|
|
name: Corpus Sparsity Ratio |
|
|
license: mit |
|
|
datasets: |
|
|
- microsoft/ms_marco |
|
|
language: |
|
|
- en |
|
|
--- |
|
|
|
|
|
|
|
|
# SPLADE-BERT-Mini-Distil |
|
|
|
|
|
This is a SPLADE sparse retrieval model based on BERT-Mini (11M) that was trained by distilling a Cross-Encoder on the MSMARCO dataset. The cross-encoder used was [ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2). |
|
|
|
|
|
This tiny SPLADE model is `6x` smaller than Naver's official `splade-v3-distilbert` while having `85%` of it's performance on the MSMARCO benchmark. This model is small enough to be used without a GPU on a dataset of a few thousand documents. |
|
|
|
|
|
- `Collection:` https://huggingface.co/collections/rasyosef/splade-tiny-msmarco-687c548c0691d95babf65b70 |
|
|
- `Distillation Dataset:` https://huggingface.co/datasets/yosefw/msmarco-train-distil-v2 |
|
|
- `Code:` https://github.com/rasyosef/splade-tiny-msmarco |
|
|
|
|
|
## Performance |
|
|
|
|
|
The splade models were evaluated on 55 thousand queries and 8.84 million documents from the [MSMARCO](https://huggingface.co/datasets/microsoft/ms_marco) dataset. |
|
|
|
|
|
||Size (# Params)|MRR@10 (MS MARCO dev)| |
|
|
|:---|:----|:-------------------| |
|
|
|`BM25`|-|18.0|-|-| |
|
|
|`rasyosef/splade-tiny`|4.4M|30.9| |
|
|
|`rasyosef/splade-mini`|11.2M|34.1| |
|
|
|`naver/splade-v3-distilbert`|67.0M|38.7| |
|
|
|
|
|
## 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 SparseEncoder |
|
|
|
|
|
# Download from the 🤗 Hub |
|
|
model = SparseEncoder("yosefw/SPLADE-BERT-Mini-BS256-distil") |
|
|
# Run inference |
|
|
queries = [ |
|
|
"common law implied warranty", |
|
|
] |
|
|
documents = [ |
|
|
'The law recognizes two basic kinds of warrantiesimplied warranties and express warranties. Implied Warranties. Implied warranties are unspoken, unwritten promises, created by state law, that go from you, as a seller or merchant, to your customers.', |
|
|
'An implied warranty is a contract law term for certain assurances that are presumed in the sale of products or real property.', |
|
|
'The implied warranty of fitness for a particular purpose is a promise that the law says you, as a seller, make when your customer relies on your advice that a product can be used for some specific purpose.', |
|
|
] |
|
|
query_embeddings = model.encode_query(queries) |
|
|
document_embeddings = model.encode_document(documents) |
|
|
print(query_embeddings.shape, document_embeddings.shape) |
|
|
# [1, 30522] [3, 30522] |
|
|
|
|
|
# Get the similarity scores for the embeddings |
|
|
similarities = model.similarity(query_embeddings, document_embeddings) |
|
|
print(similarities) |
|
|
# tensor([[22.4364, 22.7160, 21.7330]]) |
|
|
``` |
|
|
|
|
|
<!-- |
|
|
### Direct Usage (Transformers) |
|
|
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
|
|
|
|
|
</details> |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
### Downstream Usage (Sentence Transformers) |
|
|
|
|
|
You can finetune this model on your own dataset. |
|
|
|
|
|
<details><summary>Click to expand</summary> |
|
|
|
|
|
</details> |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
### Out-of-Scope Use |
|
|
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
|
--> |
|
|
|
|
|
## Model Details |
|
|
|
|
|
### Model Description |
|
|
- **Model Type:** SPLADE Sparse Encoder |
|
|
- **Base model:** [prajjwal1/bert-mini](https://huggingface.co/prajjwal1/bert-mini) |
|
|
- **Maximum Sequence Length:** 512 tokens |
|
|
- **Output Dimensionality:** 30522 dimensions |
|
|
- **Similarity Function:** Dot Product |
|
|
<!-- - **Training Dataset:** Unknown --> |
|
|
<!-- - **Language:** Unknown --> |
|
|
<!-- - **License:** Unknown --> |
|
|
|
|
|
### Model Sources |
|
|
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
|
- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) |
|
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
|
- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder) |
|
|
|
|
|
### Full Model Architecture |
|
|
|
|
|
``` |
|
|
SparseEncoder( |
|
|
(0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertForMaskedLM'}) |
|
|
(1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522}) |
|
|
) |
|
|
``` |
|
|
|
|
|
## More |
|
|
<details><summary>Click to expand</summary> |
|
|
|
|
|
## Evaluation |
|
|
|
|
|
### Metrics |
|
|
|
|
|
#### Sparse Information Retrieval |
|
|
|
|
|
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) |
|
|
|
|
|
| Metric | Value | |
|
|
|:----------------------|:-----------| |
|
|
| dot_accuracy@1 | 0.5018 | |
|
|
| dot_accuracy@3 | 0.8286 | |
|
|
| dot_accuracy@5 | 0.9194 | |
|
|
| dot_accuracy@10 | 0.9746 | |
|
|
| dot_precision@1 | 0.5018 | |
|
|
| dot_precision@3 | 0.2839 | |
|
|
| dot_precision@5 | 0.191 | |
|
|
| dot_precision@10 | 0.1026 | |
|
|
| dot_recall@1 | 0.4868 | |
|
|
| dot_recall@3 | 0.8148 | |
|
|
| dot_recall@5 | 0.9096 | |
|
|
| dot_recall@10 | 0.9709 | |
|
|
| **dot_ndcg@10** | **0.7457** | |
|
|
| dot_mrr@10 | 0.6749 | |
|
|
| dot_map@100 | 0.6708 | |
|
|
| query_active_dims | 22.585 | |
|
|
| query_sparsity_ratio | 0.9993 | |
|
|
| corpus_active_dims | 174.852 | |
|
|
| corpus_sparsity_ratio | 0.9943 | |
|
|
|
|
|
<!-- |
|
|
## Bias, Risks and Limitations |
|
|
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
### Recommendations |
|
|
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
|
--> |
|
|
|
|
|
## Training Details |
|
|
|
|
|
### Training Dataset |
|
|
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 1,000,000 training samples |
|
|
* Columns: <code>query</code>, <code>positive</code>, <code>negative_1</code>, <code>negative_2</code>, and <code>label</code> |
|
|
* Approximate statistics based on the first 1000 samples: |
|
|
| | query | positive | negative_1 | negative_2 | label | |
|
|
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------| |
|
|
| type | string | string | string | string | list | |
|
|
| details | <ul><li>min: 4 tokens</li><li>mean: 9.01 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 80.48 tokens</li><li>max: 247 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 79.27 tokens</li><li>max: 213 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 75.56 tokens</li><li>max: 190 tokens</li></ul> | <ul><li>size: 2 elements</li></ul> | |
|
|
* Samples: |
|
|
| query | positive | negative_1 | negative_2 | label | |
|
|
|:-----------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------| |
|
|
| <code>friendly home health care</code> | <code>Medicare Evaluation of the Quality of Care. The quality of care given at Friendly Care Home Health Services is periodically evaluated by Medicare. The results of the most recent evaluation period are listed below to help you compare home care agencies in your area. More Info.</code> | <code>Every participant took the same survey so it is a useful way to compare Friendly Care Home Health Services to other home care agencies.</code> | <code>It covers a wide range of services and can often delay the need for long-term nursing home care. More specifically, home health care may include occupational and physical therapy, speech therapy, and even skilled nursing.</code> | <code>[1.2647171020507812, 9.144136428833008]</code> | |
|
|
| <code>how much does the xbox elite controller weigh</code> | <code>How much does an Xbox 360 weigh? A: The weight of an Xbox 360 depends on the different model purchased, with an original Xbox 360 or Xbox 360 Elite weighing 7.7 pounds with a hard drive and a newer Xbox 360 Slim weighing 6.3 pounds. An Xbox 360 without a hard drive weighs 7 pounds.</code> | <code>How much does 6 xbox 360 games/cases weigh? How much does an xbox 360 elite weigh (in the box)? How much does an xbox 360 weigh? im going to fedex one? I am considering purchasing an Xbox 360, or a Playstation 3...</code> | <code>1 You can only upload videos smaller than 600 MB. 2 You can only upload a photo (png, jpg, jpeg) or video (3gp, 3gpp, mp4, mov, avi, mpg, mpeg, rm). 3 You can only upload a photo or video. Video should be smaller than <b>600 MB/5 minutes</b>.</code> | <code>[4.903870582580566, 18.162578582763672]</code> | |
|
|
| <code>what county is norfolk, ct in</code> | <code>Norfolk, Connecticut. Norfolk (local /ˈnɔːrfɔːrk/) is a town in Litchfield County, Connecticut, United States. The population was 1,787 at the 2010 census.</code> | <code>Norfolk Historic District. The Norfolk Historic District was listed on the National Register of Historic Places in 1979. Portions of the content on this web page were adapted from a copy of the original nomination document. [†] Adaptation copyright © 2010, The Gombach Group. Description.</code> | <code>Terms begin the first day of the month. Grand Juries, 1st and 3rd Wednesday of each month. Civil cases set by agreement of counsel and consent of the court; scheduling orders are mandatory in most cases. Civil and Criminal trials begin at 9:30 a.m.</code> | <code>[12.4237699508667, 21.46290397644043]</code> | |
|
|
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"loss": "SparseMarginMSELoss", |
|
|
"document_regularizer_weight": 0.12, |
|
|
"query_regularizer_weight": 0.2 |
|
|
} |
|
|
``` |
|
|
|
|
|
### Training Hyperparameters |
|
|
#### Non-Default Hyperparameters |
|
|
|
|
|
- `eval_strategy`: epoch |
|
|
- `per_device_train_batch_size`: 64 |
|
|
- `per_device_eval_batch_size`: 64 |
|
|
- `learning_rate`: 4e-05 |
|
|
- `num_train_epochs`: 4 |
|
|
- `lr_scheduler_type`: cosine |
|
|
- `warmup_ratio`: 0.025 |
|
|
- `fp16`: True |
|
|
- `load_best_model_at_end`: True |
|
|
- `optim`: adamw_torch_fused |
|
|
|
|
|
#### All Hyperparameters |
|
|
<details><summary>Click to expand</summary> |
|
|
|
|
|
- `overwrite_output_dir`: False |
|
|
- `do_predict`: False |
|
|
- `eval_strategy`: epoch |
|
|
- `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`: 4e-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`: 4 |
|
|
- `max_steps`: -1 |
|
|
- `lr_scheduler_type`: cosine |
|
|
- `lr_scheduler_kwargs`: {} |
|
|
- `warmup_ratio`: 0.025 |
|
|
- `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} |
|
|
- `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`: 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 |
|
|
- `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`: {} |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
| Epoch | Step | Training Loss | dot_ndcg@10 | |
|
|
|:-----:|:-----:|:-------------:|:-----------:| |
|
|
| 1.0 | 15625 | 9.3147 | 0.7353 | |
|
|
| 2.0 | 31250 | 7.5267 | 0.7429 | |
|
|
| 3.0 | 46875 | 6.3289 | 0.7457 | |
|
|
|
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.11.13 |
|
|
- Sentence Transformers: 5.0.0 |
|
|
- Transformers: 4.53.3 |
|
|
- PyTorch: 2.6.0+cu124 |
|
|
- Accelerate: 1.9.0 |
|
|
- Datasets: 4.0.0 |
|
|
- Tokenizers: 0.21.2 |
|
|
|
|
|
## 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", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### SpladeLoss |
|
|
```bibtex |
|
|
@misc{formal2022distillationhardnegativesampling, |
|
|
title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective}, |
|
|
author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant}, |
|
|
year={2022}, |
|
|
eprint={2205.04733}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.IR}, |
|
|
url={https://arxiv.org/abs/2205.04733}, |
|
|
} |
|
|
``` |
|
|
|
|
|
#### SparseMarginMSELoss |
|
|
```bibtex |
|
|
@misc{hofstätter2021improving, |
|
|
title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation}, |
|
|
author={Sebastian Hofstätter and Sophia Althammer and Michael Schröder and Mete Sertkan and Allan Hanbury}, |
|
|
year={2021}, |
|
|
eprint={2010.02666}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.IR} |
|
|
} |
|
|
``` |
|
|
|
|
|
#### FlopsLoss |
|
|
```bibtex |
|
|
@article{paria2020minimizing, |
|
|
title={Minimizing flops to learn efficient sparse representations}, |
|
|
author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s}, |
|
|
journal={arXiv preprint arXiv:2004.05665}, |
|
|
year={2020} |
|
|
} |
|
|
``` |
|
|
|
|
|
<!-- |
|
|
## Glossary |
|
|
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
## Model Card Authors |
|
|
|
|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
## Model Card Contact |
|
|
|
|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
|
|
--> |
|
|
</details> |
