This is a sentence-transformers model finetuned from 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 tokens
Similarity Function: Cosine Similarity

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': 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:

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]

Evaluation

Metrics

Information Retrieval

Evaluated with 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

Training Details

Training Dataset

Unnamed Dataset

Size: 723 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 5 tokens
mean: 11.8 tokens
max: 26 tokens
min: 24 tokens
mean: 84.41 tokens
max: 194 tokens

Samples:

sentence_0	sentence_1
`what is my portfolio 3 year cagr?`	`[{"get_portfolio(None)": "portfolio"}, {"get_attribute('portfolio',['gains'],'[object Object]`
`what is my 1 year rate of return`	`[{"get_portfolio(None)": "portfolio"}, {"get_attribute('portfolio',['gains'],'[object Object]`
`show backtest of my performance this year?`	`[{"get_portfolio(None)": "portfolio"}, {"get_attribute('portfolio',['gains'],'[object Object]`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

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

Click to expand

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

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