Hugging Face's logo

magnifi
/
bge-small-en-v1.5-ft-orc-0813

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:491
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
bge-small-en-v1.5-ft-orc-0813 / README.md
fliou2's picture
fliou2
Upload folder using huggingface_hub
ef2aeb3 verified
preview code
|
raw
history blame contribute delete
21.4 kB
metadata
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:491
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: do I have money I vested through [TICKER]
    sentences:
      - >-
        [{"get_portfolio(['brokerName'])": "portfolio"},
        {"filter('portfolio','brokerName','==','Magnifi')": "portfolio"}]
      - >-
        [{"get_attribute(['<TICKER1>'],['returns'],'<DATES>')":
        "price_<TICKER1>"}]
      - >-
        [{"get_earnings_announcements(['<TICKER1>'],'<DATES>')":
        "<TICKER1>_earnings"}]
  - source_sentence: Knock Knock!
    sentences:
      - >-
        [{"get_portfolio(['weight'])": "portfolio"},
        {"factor_contribution('portfolio','<DATES>','sector','sector
        retailing','portfolio')": "portfolio"}]
      - >-
        [{"get_news_articles(['<TICKER1>'],None,None,None)":
        "news_data_<TICKER1>"}]
      - '[]'
  - source_sentence: what's the earnings per share of [TICKER]
    sentences:
      - >-
        [{"get_attribute(['<TICKER1>'],['returns'],'<DATES>')":
        "performance_data_<TICKER1>"}]
      - >-
        [{"get_attribute(['<TICKER1>'],['earnings per share'],'<DATES>')":
        "earnings_per_share_<TICKER1>"}]
      - >-
        [{"get_portfolio(['weight'])": "portfolio"},
        {"factor_contribution('portfolio','<DATES>','factor','momentum','portfolio')":
        "portfolio"}]
  - source_sentence: returns of [TICKER] since 2017
    sentences:
      - >-
        [{"get_portfolio(['weight'])": "portfolio"},
        {"factor_contribution('portfolio','<DATES>','factor','volatility','returns')":
        "portfolio"}]
      - >-
        [{"get_attribute(['<TICKER1>'],['returns'],'<DATES>')":
        "performance_data_<TICKER1>"}]
      - >-
        [{"get_dictionary_definition(['limit order', 'market order'])":
        "definitions"}]
  - source_sentence: how should I play [TICKER] futures contracts
    sentences:
      - '[]'
      - >-
        [{"get_attribute(['<TICKER1>'],['returns'],'<DATES>')":
        "live_price_<TICKER1>"}]
      - '[{"get_news_articles(None,None,None,None)": "latest_news_data"}]'
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: 0.7191780821917808
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9246575342465754
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.952054794520548
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9794520547945206
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7191780821917808
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3082191780821918
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19041095890410956
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09794520547945204
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.019977168949771692
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.02568493150684932
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.02644596651445967
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.02720700152207002
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.1886992031917713
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8171314416177428
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.02272901264767703
            name: Cosine Map@100
          - type: dot_accuracy@1
            value: 0.7191780821917808
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.9246575342465754
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.952054794520548
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.9794520547945206
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.7191780821917808
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.3082191780821918
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.19041095890410956
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.09794520547945204
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.019977168949771692
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.02568493150684932
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.02644596651445967
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.02720700152207002
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.1886992031917713
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.8171314416177428
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.02272901264767703
            name: Dot Map@100

SentenceTransformer based on BAAI/bge-small-en-v1.5

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

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'how should I play [TICKER] futures contracts',
    '[]',
    '[{"get_attribute([\'<TICKER1>\'],[\'returns\'],\'<DATES>\')": "live_price_<TICKER1>"}]',
]
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

Metric Value
cosine_accuracy@1 0.7192
cosine_accuracy@3 0.9247
cosine_accuracy@5 0.9521
cosine_accuracy@10 0.9795
cosine_precision@1 0.7192
cosine_precision@3 0.3082
cosine_precision@5 0.1904
cosine_precision@10 0.0979
cosine_recall@1 0.02
cosine_recall@3 0.0257
cosine_recall@5 0.0264
cosine_recall@10 0.0272
cosine_ndcg@10 0.1887
cosine_mrr@10 0.8171
cosine_map@100 0.0227
dot_accuracy@1 0.7192
dot_accuracy@3 0.9247
dot_accuracy@5 0.9521
dot_accuracy@10 0.9795
dot_precision@1 0.7192
dot_precision@3 0.3082
dot_precision@5 0.1904
dot_precision@10 0.0979
dot_recall@1 0.02
dot_recall@3 0.0257
dot_recall@5 0.0264
dot_recall@10 0.0272
dot_ndcg@10 0.1887
dot_mrr@10 0.8171
dot_map@100 0.0227

Training Details

Training Dataset

Unnamed Dataset

  • Size: 491 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: 4 tokens
    • mean: 11.9 tokens
    • max: 26 tokens
    • min: 4 tokens
    • mean: 67.55 tokens
    • max: 194 tokens
  • Samples:
    sentence_0 sentence_1
    Profitability of [TICKER] [{"get_attribute([''],['cash flow profitability'],'')": "profitability_"}]
    [TICKER] momentum [{"get_attribute([''],['momentum'],'')": "momentum_"}]
    what was the total return of [TICKER] for 2023 [{"get_attribute([''],['returns'],'')": "performance_data_"}]
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
    "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

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

Click to expand
Epoch Step cosine_map@100
0.04 2 0.0137
0.08 4 0.0137
0.12 6 0.0138
0.16 8 0.0142
0.2 10 0.0144
0.24 12 0.0147
0.28 14 0.0149
0.32 16 0.0151
0.36 18 0.0155
0.4 20 0.0166
0.44 22 0.0170
0.48 24 0.0174
0.52 26 0.0179
0.56 28 0.0181
0.6 30 0.0184
0.64 32 0.0186
0.68 34 0.0189
0.72 36 0.0191
0.76 38 0.0192
0.8 40 0.0195
0.84 42 0.0195
0.88 44 0.0195
0.92 46 0.0195
0.96 48 0.0196
1.0 50 0.0197
1.04 52 0.0196
1.08 54 0.0198
1.12 56 0.0200
1.16 58 0.0202
1.2 60 0.0202
1.24 62 0.0205
1.28 64 0.0206
1.32 66 0.0207
1.3600 68 0.0208
1.4 70 0.0208
1.44 72 0.0209
1.48 74 0.0210
1.52 76 0.0211
1.56 78 0.0211
1.6 80 0.0209
1.6400 82 0.0210
1.6800 84 0.0209
1.72 86 0.0209
1.76 88 0.0210
1.8 90 0.0211
1.8400 92 0.0211
1.88 94 0.0211
1.92 96 0.0214
1.96 98 0.0216
2.0 100 0.0218
2.04 102 0.0217
2.08 104 0.0217
2.12 106 0.0219
2.16 108 0.0221
2.2 110 0.0219
2.24 112 0.0217
2.2800 114 0.0217
2.32 116 0.0217
2.36 118 0.0218
2.4 120 0.0219
2.44 122 0.0219
2.48 124 0.0219
2.52 126 0.0222
2.56 128 0.0220
2.6 130 0.0221
2.64 132 0.0221
2.68 134 0.0221
2.7200 136 0.0221
2.76 138 0.0222
2.8 140 0.0222
2.84 142 0.0224
2.88 144 0.0224
2.92 146 0.0223
2.96 148 0.0224
3.0 150 0.0223
3.04 152 0.0223
3.08 154 0.0223
3.12 156 0.0223
3.16 158 0.0223
3.2 160 0.0223
3.24 162 0.0223
3.2800 164 0.0223
3.32 166 0.0223
3.36 168 0.0223
3.4 170 0.0223
3.44 172 0.0224
3.48 174 0.0224
3.52 176 0.0225
3.56 178 0.0224
3.6 180 0.0224
3.64 182 0.0224
3.68 184 0.0225
3.7200 186 0.0225
3.76 188 0.0225
3.8 190 0.0225
3.84 192 0.0225
3.88 194 0.0225
3.92 196 0.0226
3.96 198 0.0226
4.0 200 0.0226
4.04 202 0.0226
4.08 204 0.0226
4.12 206 0.0226
4.16 208 0.0225
4.2 210 0.0225
4.24 212 0.0225
4.28 214 0.0225
4.32 216 0.0225
4.36 218 0.0226
4.4 220 0.0227

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 

@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 

@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}
}