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deter3/
splade-distilbert-base-uncased-patentmatch-train-only

Name: splade-distilbert-base-uncased-patentmatch-train-only
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SPLADE model for patent matching

This is a SPLADE Sparse Encoder model finetuned from distilbert/distilbert-base-uncased on the json dataset using the sentence-transformers library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.

Model Details

Model Description

Model Type: SPLADE Sparse Encoder
Base model: distilbert/distilbert-base-uncased
Maximum Sequence Length: 256 tokens
Output Dimensionality: 30522 dimensions
Similarity Function: Dot Product
Training Dataset:
- json
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Documentation: Sparse Encoder Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sparse Encoders on Hugging Face

Full Model Architecture

SparseEncoder(
  (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'DistilBertForMaskedLM'})
  (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

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("deter3/splade-distilbert-base-uncased-patentmatch-train-only")
# Run inference
queries = [
    "The mobile device as claimed in claim 11 or claim 12 further comprising a display for displaying a list of applicationspecific places that only contains the places that have been selected by a specific application.",
]
documents = [
    'A client browser application for a user terminal the browser being capable of recognising top level domain name parts of a network resource identifier said browser being arranged to handle a network resource identifier in which a predetermined top level domain name part is recognised differently from a network resource identifier in which the top level domain name part is not recognised.',
    'Other variations in the components of the lid 1200 are possible in other embodiments of the invention.',
    'The cylinder servo motor according to the second embodiment is identical in operation with that described in connection with the first embodiment.By means of imparting a position instruction to the servo motor the ball screw shaft 50 corresponding to the ball screw shaft 5 is actuated to a predetermined position.',
]
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([[12.4191,  5.6239,  1.1307]])

Training Details

Training Dataset

json

Dataset: json
Size: 811,257 training samples
Columns: query and positive_passage
Approximate statistics based on the first 1000 samples:
query positive_passage
type string string
details
min: 14 tokens
mean: 72.25 tokens
max: 256 tokens
min: 4 tokens
mean: 111.83 tokens
max: 256 tokens

Samples:

query	positive_passage
`The method of claim 9 wherein the first area is located at an upper part of the screen and the second area is located at a lower part of the screen.`	`At this point the user can touch on a third icon 734 corresponding to a third application to select the third application.`
`The mobile terminal of claim 8 wherein the controller is further configured to receive information from the specific external device indicating that the allow device search feature is enabled in the specific external device via the wireless communication unit and to control the display unit to display information requesting a user research for the specific external device.`	Thereupon in this embodiment the communication device the information processing method thereof and the information processing program thereof constitute an address book DB in the data base part 18 and is constituted so that when receiving owner information from a communication terminal X namely a device of the other party by Bluetooth a Bluetooth device address ofthe communication terminal X obtained at the time of communication is recorded in the address book DB by using an owners name included in the owner information as a key.
`The container according to claim 1 characterized in that the angle formed by the first bar 1 and the second bar 2 in the gate closing position is an obtuse angle between 150 and 180 or an acute angle between 0 and 30.`	The second hydraulic actuator 25 is activated this actuator by means of the mechanical actuator 22 rotates the levers 17 in such a way that the relative small tooth 18 moves up under the edge of ring 4.The hydraulic actuator 20 is activated in such a way that the piston 21 rests on the head of the pin 6 and at the same timemoves the body 16 upwards in such a way as to eliminate the clearance existing between the small teeth 18 and the edge of ring 4 a necessary clearance to place the small teeth 18 under the edge of the ring 4 in the phase of coupling.

Loss: SpladeLoss with these parameters:
json
1{ 2 "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')", 3 "document_regularizer_weight": 0.001, 4 "query_regularizer_weight": 5e-05 5}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 24
gradient_accumulation_steps: 8
learning_rate: 2e-05
num_train_epochs: 2
warmup_ratio: 0.1
bf16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 24
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 8
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: 2
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
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: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.0237	100	3921.6512
0.0473	200	10.5268
0.0710	300	1.9553
0.0947	400	1.6494
0.1183	500	1.5371
0.1420	600	1.4252
0.1657	700	1.3429
0.1893	800	1.2958
0.2130	900	1.2307
0.2367	1000	1.1905
0.2603	1100	1.1774
0.2840	1200	1.1387
0.3077	1300	1.0852
0.3313	1400	1.0718
0.3550	1500	1.0435
0.3787	1600	1.0166
0.4023	1700	1.0151
0.4260	1800	0.9792
0.4497	1900	0.9725
0.4733	2000	0.9492
0.4970	2100	0.9253
0.5207	2200	0.9178
0.5443	2300	0.9252
0.5680	2400	0.912
0.5917	2500	0.8796
0.6153	2600	0.8631
0.6390	2700	0.8575
0.6627	2800	0.8361
0.6863	2900	0.8407
0.7100	3000	0.8296
0.7337	3100	0.818
0.7573	3200	0.813
0.7810	3300	0.8073
0.8047	3400	0.7929
0.8283	3500	0.7927
0.8520	3600	0.7718
0.8757	3700	0.7823
0.8993	3800	0.7607
0.9230	3900	0.7547
0.9467	4000	0.7547
0.9703	4100	0.7286
0.9940	4200	0.7269
1.0175	4300	0.7186
1.0412	4400	0.6988
1.0648	4500	0.6888
1.0885	4600	0.6971
1.1122	4700	0.6833
1.1358	4800	0.6854
1.1595	4900	0.6771
1.1832	5000	0.6798
1.2068	5100	0.6596
1.2305	5200	0.6344
1.2542	5300	0.6529
1.2778	5400	0.6569
1.3015	5500	0.6414
1.3252	5600	0.6386
1.3488	5700	0.6324
1.3725	5800	0.6478
1.3962	5900	0.6372
1.4198	6000	0.6292
1.4435	6100	0.6272
1.4672	6200	0.623
1.4908	6300	0.6215
1.5145	6400	0.6307
1.5382	6500	0.6256
1.5618	6600	0.6179
1.5855	6700	0.6089
1.6092	6800	0.6111
1.6328	6900	0.6036
1.6565	7000	0.6169
1.6802	7100	0.6152
1.7038	7200	0.6055
1.7275	7300	0.5846
1.7512	7400	0.5955
1.7748	7500	0.6083
1.7985	7600	0.6015
1.8222	7700	0.5871
1.8458	7800	0.5821
1.8695	7900	0.5941
1.8932	8000	0.5859
1.9168	8100	0.5974
1.9405	8200	0.5917
1.9642	8300	0.5787
1.9878	8400	0.5902

Framework Versions

Python: 3.11.10
Sentence Transformers: 5.0.0
Transformers: 4.53.0
PyTorch: 2.4.1+cu124
Accelerate: 1.8.1
Datasets: 3.6.0
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},
}

SparseMultipleNegativesRankingLoss

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

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}
}

	query	positive_passage
type	string	string
details	min: 14 tokens mean: 72.25 tokens max: 256 tokens	min: 4 tokens mean: 111.83 tokens max: 256 tokens