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bge-small-en-v1.5-ft-orc-0806

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:723
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
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---
base_model: BAAI/bge-small-en-v1.5
datasets: []
language: []
library_name: sentence-transformers
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
- 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
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:723
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: how do different regions contribute to my returns
sentences:
- '[{"get_portfolio(None)": "portfolio"}, {"filter(''portfolio'',''ticker'',''=='',''<TICKER1>'')":
"portfolio"}, {"get_attribute(''portfolio'',[''losses''],''<DATES>'')": "portfolio"}]'
- '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''region'',None,''returns'')":
"portfolio"}]'
- '[{"get_portfolio(None)": "portfolio"}, {"get_attribute(''portfolio'',[''gains''],''<DATES>'')":
"portfolio"}, {"sort(''portfolio'',''gains'',''desc'')": "portfolio"}, {"get_attribute([''<TICKER1>''],[''returns''],''<DATES>'')":
"<TICKER1>_performance_data"}]'
- source_sentence: how have I done in US equity this year?
sentences:
- '[{"get_portfolio([''weight''])": "portfolio"}, {"get_attribute(''portfolio'',[''dividend
yield''],''<DATES>'')": "portfolio"}, {"calculate(''portfolio'',[''dividend yield'',''weight''],''multiply'',''weighted_yield'')":
"portfolio"}, {"aggregate(''portfolio'',''ticker'',''weighted_yield'',''sum'',None)":
"portfolio_yield"}]'
- '[{"get_portfolio(None)": "portfolio"}, {"get_attribute(''portfolio'',[''dividend
yield''],''<DATES>'')": "portfolio"}, {"calculate(''portfolio'',[''dividend yield'',
''marketValue''],''multiply'',''div_income'')": "portfolio"}, {"sort(''portfolio'',''div_income'',''desc'')":
"portfolio"}]'
- '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''asset_class'',''us
equity'',''returns'')": "portfolio"}]'
- source_sentence: What is the total value of my cash?
sentences:
- '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''sector'',''sector
utilities'',''portfolio'')": "portfolio"}]'
- '[{"get_portfolio([''type'', ''marketValue''])": "portfolio"}, {"filter(''portfolio'',''type'',''=='',''CASH'')":
"portfolio"}, {"aggregate(''portfolio'',''ticker'',''marketValue'',''sum'',None)":
"buying_power"}]'
- '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''sector'',''sector
information technology'',''returns'')": "portfolio"}]'
- source_sentence: What is the exposure of my account to Chinese market?
sentences:
- '[{"get_portfolio([''marketValue''])": "portfolio"}, {"sort(''portfolio'',''marketValue'',''asc'')":
"portfolio"}]'
- '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''region'',''china'',''portfolio'')":
"portfolio"}]'
- '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''volatility'',None,''portfolio'')":
"portfolio"}]'
- source_sentence: Which of my investments are projected to generate the most return?
sentences:
- '[{"get_portfolio([''marketValue''])": "portfolio"}, {"get_attribute(''portfolio'',[''<TICKER1>''],''<DATES>'')":
"portfolio"}, {"calculate(''portfolio'',[''marketValue'', ''<TICKER1>''],''multiply'',''expo_<TICKER1>'')":
"portfolio"}, {"sort(''portfolio'',''expo_<TICKER1>'',''desc'')": "portfolio"},
{"aggregate(''portfolio'',''ticker'',''expo_<TICKER1>'',''sum'',None)": "port_expo_<TICKER1>"}]'
- '[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(''portfolio'',''<DATES>'',''asset_class'',''us
equity'',''returns'')": "portfolio"}]'
- '[{"get_portfolio(None)": "portfolio"}, {"get_expected_attribute(''portfolio'',[''returns''])":
"portfolio"}, {"sort(''portfolio'',''returns'',''desc'')": "portfolio"}]'
model-index:
- name: SentenceTransformer based on BAAI/bge-small-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.6643835616438356
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8287671232876712
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.863013698630137
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9178082191780822
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6643835616438356
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27625570776255703
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17260273972602735
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0917808219178082
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.018455098934550992
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.023021308980213092
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.02397260273972603
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.02549467275494673
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.1736543171752474
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7480294629267232
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.020863027954722068
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.6643835616438356
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.8287671232876712
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.863013698630137
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9178082191780822
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.6643835616438356
name: Dot Precision@1
- type: dot_precision@3
value: 0.27625570776255703
name: Dot Precision@3
- type: dot_precision@5
value: 0.17260273972602735
name: Dot Precision@5
- type: dot_precision@10
value: 0.0917808219178082
name: Dot Precision@10
- type: dot_recall@1
value: 0.018455098934550992
name: Dot Recall@1
- type: dot_recall@3
value: 0.023021308980213092
name: Dot Recall@3
- type: dot_recall@5
value: 0.02397260273972603
name: Dot Recall@5
- type: dot_recall@10
value: 0.02549467275494673
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.1736543171752474
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7480294629267232
name: Dot Mrr@10
- type: dot_map@100
value: 0.020863027954722068
name: Dot Map@100
---
# SentenceTransformer based on BAAI/bge-small-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). It maps sentences & paragraphs to a 384-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-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'Which of my investments are projected to generate the most return?',
'[{"get_portfolio(None)": "portfolio"}, {"get_expected_attribute(\'portfolio\',[\'returns\'])": "portfolio"}, {"sort(\'portfolio\',\'returns\',\'desc\')": "portfolio"}]',
'[{"get_portfolio(None)": "portfolio"}, {"factor_contribution(\'portfolio\',\'<DATES>\',\'asset_class\',\'us equity\',\'returns\')": "portfolio"}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### 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.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.6644 |
| cosine_accuracy@3 | 0.8288 |
| cosine_accuracy@5 | 0.863 |
| cosine_accuracy@10 | 0.9178 |
| cosine_precision@1 | 0.6644 |
| cosine_precision@3 | 0.2763 |
| cosine_precision@5 | 0.1726 |
| cosine_precision@10 | 0.0918 |
| cosine_recall@1 | 0.0185 |
| cosine_recall@3 | 0.023 |
| cosine_recall@5 | 0.024 |
| cosine_recall@10 | 0.0255 |
| cosine_ndcg@10 | 0.1737 |
| cosine_mrr@10 | 0.748 |
| **cosine_map@100** | **0.0209** |
| dot_accuracy@1 | 0.6644 |
| dot_accuracy@3 | 0.8288 |
| dot_accuracy@5 | 0.863 |
| dot_accuracy@10 | 0.9178 |
| dot_precision@1 | 0.6644 |
| dot_precision@3 | 0.2763 |
| dot_precision@5 | 0.1726 |
| dot_precision@10 | 0.0918 |
| dot_recall@1 | 0.0185 |
| dot_recall@3 | 0.023 |
| dot_recall@5 | 0.024 |
| dot_recall@10 | 0.0255 |
| dot_ndcg@10 | 0.1737 |
| dot_mrr@10 | 0.748 |
| dot_map@100 | 0.0209 |
<!--
## 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: 723 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 11.8 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 84.41 tokens</li><li>max: 194 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:--------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what is my portfolio 3 year cagr?</code> | <code>[{"get_portfolio(None)": "portfolio"}, {"get_attribute('portfolio',['gains'],'<DATES>')": "portfolio"}, {"sort('portfolio','gains','desc')": "portfolio"}]</code> |
| <code>what is my 1 year rate of return</code> | <code>[{"get_portfolio(None)": "portfolio"}, {"get_attribute('portfolio',['gains'],'<DATES>')": "portfolio"}, {"sort('portfolio','gains','desc')": "portfolio"}]</code> |
| <code>show backtest of my performance this year?</code> | <code>[{"get_portfolio(None)": "portfolio"}, {"get_attribute('portfolio',['gains'],'<DATES>')": "portfolio"}, {"sort('portfolio','gains','desc')": "portfolio"}]</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `num_train_epochs`: 6
- `multi_dataset_batch_sampler`: round_robin
#### 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`: 10
- `per_device_eval_batch_size`: 10
- `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
- `num_train_epochs`: 6
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: 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`: 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
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | cosine_map@100 |
|:------:|:----:|:--------------:|
| 0.0274 | 2 | 0.0136 |
| 0.0548 | 4 | 0.0137 |
| 0.0822 | 6 | 0.0139 |
| 0.1096 | 8 | 0.0142 |
| 0.1370 | 10 | 0.0145 |
| 0.1644 | 12 | 0.0144 |
| 0.1918 | 14 | 0.0147 |
| 0.2192 | 16 | 0.0151 |
| 0.2466 | 18 | 0.0153 |
| 0.2740 | 20 | 0.0158 |
| 0.3014 | 22 | 0.0165 |
| 0.3288 | 24 | 0.0163 |
| 0.3562 | 26 | 0.0167 |
| 0.3836 | 28 | 0.0171 |
| 0.4110 | 30 | 0.0175 |
| 0.4384 | 32 | 0.0177 |
| 0.4658 | 34 | 0.0180 |
| 0.4932 | 36 | 0.0183 |
| 0.5205 | 38 | 0.0185 |
| 0.5479 | 40 | 0.0186 |
| 0.5753 | 42 | 0.0186 |
| 0.6027 | 44 | 0.0186 |
| 0.6301 | 46 | 0.0186 |
| 0.6575 | 48 | 0.0187 |
| 0.6849 | 50 | 0.0189 |
| 0.7123 | 52 | 0.0190 |
| 0.7397 | 54 | 0.0189 |
| 0.7671 | 56 | 0.0188 |
| 0.7945 | 58 | 0.0189 |
| 0.8219 | 60 | 0.0192 |
| 0.8493 | 62 | 0.0193 |
| 0.8767 | 64 | 0.0194 |
| 0.9041 | 66 | 0.0194 |
| 0.9315 | 68 | 0.0197 |
| 0.9589 | 70 | 0.0200 |
| 0.9863 | 72 | 0.0201 |
| 1.0 | 73 | 0.0202 |
| 1.0137 | 74 | 0.0203 |
| 1.0411 | 76 | 0.0202 |
| 1.0685 | 78 | 0.0203 |
| 1.0959 | 80 | 0.0205 |
| 1.1233 | 82 | 0.0207 |
| 1.1507 | 84 | 0.0207 |
| 1.1781 | 86 | 0.0206 |
| 1.2055 | 88 | 0.0205 |
| 1.2329 | 90 | 0.0205 |
| 1.2603 | 92 | 0.0205 |
| 1.2877 | 94 | 0.0204 |
| 1.3151 | 96 | 0.0204 |
| 1.3425 | 98 | 0.0205 |
| 1.3699 | 100 | 0.0205 |
| 1.3973 | 102 | 0.0205 |
| 1.4247 | 104 | 0.0205 |
| 1.4521 | 106 | 0.0204 |
| 1.4795 | 108 | 0.0205 |
| 1.5068 | 110 | 0.0208 |
| 1.5342 | 112 | 0.0206 |
| 1.5616 | 114 | 0.0205 |
| 1.5890 | 116 | 0.0206 |
| 1.6164 | 118 | 0.0205 |
| 1.6438 | 120 | 0.0205 |
| 1.6712 | 122 | 0.0205 |
| 1.6986 | 124 | 0.0207 |
| 1.7260 | 126 | 0.0207 |
| 1.7534 | 128 | 0.0207 |
| 1.7808 | 130 | 0.0205 |
| 1.8082 | 132 | 0.0206 |
| 1.8356 | 134 | 0.0208 |
| 1.8630 | 136 | 0.0206 |
| 1.8904 | 138 | 0.0206 |
| 1.9178 | 140 | 0.0206 |
| 1.9452 | 142 | 0.0205 |
| 1.9726 | 144 | 0.0206 |
| 2.0 | 146 | 0.0207 |
| 2.0274 | 148 | 0.0209 |
### Framework Versions
- Python: 3.10.9
- Sentence Transformers: 3.0.1
- Transformers: 4.44.0
- PyTorch: 2.4.0+cu121
- Accelerate: 0.33.0
- Datasets: 2.20.0
- Tokenizers: 0.19.1
## 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",
}
```
#### MultipleNegativesRankingLoss
```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}
}
```
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*Clearly define terms in order to be accessible across audiences.*
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## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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## Model Card Contact
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