metadata

base_model: BAAI/bge-base-en-v1.5
datasets: []
language:
  - en
library_name: sentence-transformers
license: apache-2.0
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:6300
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      We expect ME&T’s capital expenditures in 2024 to be around $2.0 billion to
      $2.5 billion.
    sentences:
      - What was the amount gained from the disposal of assets in 2022?
      - What is the expected capital expenditure for ME&T in 2024?
      - >-
        What is the expected total cost HP will incur from its Fiscal 2023 Plan,
        and how is it primarily divided?
  - source_sentence: >-
      Average invested capital is calculated as the sum of (i) the average of
      our total assets, (ii) the average LIFO reserve and (iii) the average
      accumulated depreciation and amortization; minus (i) the average taxes
      receivable, (ii) the average trade accounts payable, (iii) the average
      accrued salaries and wages and (iv) the average other current liabilities,
      excluding accrued income taxes.
    sentences:
      - >-
        What are the components and the effective tax rates for the year 2023 as
        reported in the financial statements?
      - How is average invested capital calculated for ROIC?
      - >-
        How did the interest income change in fiscal year 2023 compared to the
        previous year?
  - source_sentence: >-
      Return on Invested Capital ('ROIC') as of May 31, 2023 was 31.5% compared
      to 46.5% as of May 31, 2022.
    sentences:
      - >-
        How is NIKE's return on invested capital (ROIC) calculated, and what was
        its value as of May 31, 2023?
      - >-
        What role do medical directors play at outpatient dialysis centers, and
        what are their general qualifications?
      - What item number discusses legal proceedings in the report?
  - source_sentence: >-
      Net cash used in financing activities was $506.5 million in the year ended
      December 31, 2022, and increased to $656.5 million in the year ended
      December 31, 2023.
    sentences:
      - >-
        How has the change in foreign exchange rates affected cash and cash
        equivalents in 2023 and 2021?
      - >-
        What kind of financial documents are included in Part IV, Item 15(a)(1)
        of the Annual Report on Form 10-K?
      - >-
        How did the net cash used in financing activities in 2023 compare to
        2022?
  - source_sentence: >-
      Alternative Payments Providers: These providers, such as closed commerce
      ecosystems, BNPL solutions and cryptocurrency platforms, often have a
      primary focus of enabling payments through ecommerce and mobile channels;
      however, they are expanding or may expand their offerings to the physical
      point of sale. These companies may process payments using in-house account
      transfers between parties, electronic funds transfer networks like the
      ACH, global or local networks like Visa, or some combination of the
      foregoing.
    sentences:
      - >-
        What are some examples of alternative payments providers and how do they
        compete with Visa?
      - >-
        How much did the company's currently payable U.S. taxes amount to in
        2023?
      - What considerations are involved in recording an uncertain tax position?
model-index:
  - name: BGE base Financial Matryoshka
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.6885714285714286
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8328571428571429
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8742857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9142857142857143
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6885714285714286
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2776190476190476
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17485714285714282
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09142857142857141
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6885714285714286
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8328571428571429
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8742857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9142857142857143
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8044897381040067
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7690017006802718
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.772240177124622
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.6971428571428572
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8342857142857143
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8742857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9071428571428571
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6971428571428572
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27809523809523806
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17485714285714282
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09071428571428569
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6971428571428572
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8342857142857143
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8742857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9071428571428571
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8044496489287004
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7712602040816322
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7750129601859859
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.6914285714285714
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8257142857142857
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8714285714285714
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.91
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6914285714285714
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2752380952380953
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17428571428571427
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09099999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6914285714285714
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8257142857142857
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8714285714285714
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.91
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8034440275222344
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7690856009070293
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7724648546606009
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.6742857142857143
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.81
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8542857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6742857142857143
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17085714285714282
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6742857142857143
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.81
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8542857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7881399973034273
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7522210884353742
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7560032496112399
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.6385714285714286
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7671428571428571
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8242857142857143
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.87
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6385714285714286
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2557142857142857
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16485714285714284
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.087
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6385714285714286
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7671428571428571
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8242857142857143
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.87
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7528845651704559
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7154948979591831
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7205565552029373
            name: Cosine Map@100

BGE base Financial Matryoshka

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: BAAI/bge-base-en-v1.5
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("kperkins411/bge-base-financial-matryoshka")
# Run inference
sentences = [
    'Alternative Payments Providers: These providers, such as closed commerce ecosystems, BNPL solutions and cryptocurrency platforms, often have a primary focus of enabling payments through ecommerce and mobile channels; however, they are expanding or may expand their offerings to the physical point of sale. These companies may process payments using in-house account transfers between parties, electronic funds transfer networks like the ACH, global or local networks like Visa, or some combination of the foregoing.',
    'What are some examples of alternative payments providers and how do they compete with Visa?',
    "How much did the company's currently payable U.S. taxes amount to in 2023?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6886
cosine_accuracy@3	0.8329
cosine_accuracy@5	0.8743
cosine_accuracy@10	0.9143
cosine_precision@1	0.6886
cosine_precision@3	0.2776
cosine_precision@5	0.1749
cosine_precision@10	0.0914
cosine_recall@1	0.6886
cosine_recall@3	0.8329
cosine_recall@5	0.8743
cosine_recall@10	0.9143
cosine_ndcg@10	0.8045
cosine_mrr@10	0.769
cosine_map@100	0.7722

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6971
cosine_accuracy@3	0.8343
cosine_accuracy@5	0.8743
cosine_accuracy@10	0.9071
cosine_precision@1	0.6971
cosine_precision@3	0.2781
cosine_precision@5	0.1749
cosine_precision@10	0.0907
cosine_recall@1	0.6971
cosine_recall@3	0.8343
cosine_recall@5	0.8743
cosine_recall@10	0.9071
cosine_ndcg@10	0.8044
cosine_mrr@10	0.7713
cosine_map@100	0.775

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6914
cosine_accuracy@3	0.8257
cosine_accuracy@5	0.8714
cosine_accuracy@10	0.91
cosine_precision@1	0.6914
cosine_precision@3	0.2752
cosine_precision@5	0.1743
cosine_precision@10	0.091
cosine_recall@1	0.6914
cosine_recall@3	0.8257
cosine_recall@5	0.8714
cosine_recall@10	0.91
cosine_ndcg@10	0.8034
cosine_mrr@10	0.7691
cosine_map@100	0.7725

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6743
cosine_accuracy@3	0.81
cosine_accuracy@5	0.8543
cosine_accuracy@10	0.9
cosine_precision@1	0.6743
cosine_precision@3	0.27
cosine_precision@5	0.1709
cosine_precision@10	0.09
cosine_recall@1	0.6743
cosine_recall@3	0.81
cosine_recall@5	0.8543
cosine_recall@10	0.9
cosine_ndcg@10	0.7881
cosine_mrr@10	0.7522
cosine_map@100	0.756

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6386
cosine_accuracy@3	0.7671
cosine_accuracy@5	0.8243
cosine_accuracy@10	0.87
cosine_precision@1	0.6386
cosine_precision@3	0.2557
cosine_precision@5	0.1649
cosine_precision@10	0.087
cosine_recall@1	0.6386
cosine_recall@3	0.7671
cosine_recall@5	0.8243
cosine_recall@10	0.87
cosine_ndcg@10	0.7529
cosine_mrr@10	0.7155
cosine_map@100	0.7206

Training Details

Training Dataset

Unnamed Dataset

Size: 6,300 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 6 tokens
mean: 45.51 tokens
max: 371 tokens

min: 10 tokens
mean: 20.83 tokens
max: 45 tokens

	positive	anchor
type	string	string
details	min: 6 tokens mean: 45.51 tokens max: 371 tokens	min: 10 tokens mean: 20.83 tokens max: 45 tokens

Samples:

positive	anchor
`Activities related to sales before 2023 experienced adjustments due to changes in estimates, impacting the rebates and chargebacks accounts, and led to an ending balance of $4,493 million for the year 2023.`	`What adjustments were made to the rebates and chargebacks balances for previous years' sales and how did they affect the end of year balance in 2023?`
`We’re focused on making hosting just as popular as traveling on Airbnb. We will continue to invest in growing the size and quality of our Host community. We plan to attract more Hosts globally by expanding use cases and supporting all different types of Hosts, including those who host occasionally.`	`What is Airbnb's long-term corporate strategy regarding hosting?`
`Due to protectionist measures in various regions, Nike has experienced increased product costs. The company responds by monitoring trends, engaging in processes to mitigate restrictions, and advocating for trade liberalization in trade agreements.`	`What challenges related to trade protectionism has Nike faced, and what measures has the company taken in response?`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
tf32: True
load_best_model_at_end: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
eval_accumulation_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: 4
max_steps: -1
lr_scheduler_type: cosine
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: True
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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_128_cosine_map@100	dim_256_cosine_map@100	dim_512_cosine_map@100	dim_64_cosine_map@100	dim_768_cosine_map@100
0.96	3	-	0.7116	0.7341	0.7448	0.6550	0.7455
1.92	6	-	0.7317	0.7520	0.7586	0.6975	0.7591
2.88	9	-	0.7334	0.7553	0.7631	0.7039	0.7630
3.2	10	3.3636	-	-	-	-	-
3.84	12	-	0.7368	0.759	0.7634	0.7054	0.7638
0.96	3	-	0.7415	0.7601	0.7672	0.7102	0.7661
1.92	6	-	0.7486	0.7683	0.7720	0.7205	0.7718
2.88	9	-	0.7556	0.7718	0.7750	0.7215	0.7717
3.2	10	1.66	-	-	-	-	-
3.84	12	-	0.756	0.7725	0.775	0.7206	0.7722

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.9
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.1.2+cu121
Accelerate: 0.31.0
Datasets: 2.19.1
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@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}
}