metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:2620
  - loss:MultipleNegativesRankingLoss
  - loss:CosineSimilarityLoss
base_model: jinaai/jina-embedding-b-en-v1
widget:
  - source_sentence: What sector am I most heavily invested in?
    sentences:
      - |
        Show me how to switch my stock portfolio to mutual funds
      - What percentage of my portfolio is in X
      - Which sector do I invest most in?
  - source_sentence: Can you tell me how my portfolio ranks among others?
    sentences:
      - What is my AMC wise split ?
      - In which funds am I paying highest fees
      - Compare my portfolio with others?
  - source_sentence: Which of my funds has the highest risk level?
    sentences:
      - Give me python code to find best funds in my portfolio
      - Show my stocks ranked by performance
      - Show my riskiest mutual funds
  - source_sentence: What's going right with my portfolio?
    sentences:
      - Is my portfolio linked?
      - My portfolio returns over all the years
      - What's going well in my portfolio
  - source_sentence: I'd like to know the percentage of large cap in my investments.
    sentences:
      - Show my riskiest holdings
      - Can you show what percentage of my portfolio consists of large cap
      - What is the expected return of my portfolio?
pipeline_tag: sentence-similarity
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
model-index:
  - name: SentenceTransformer based on jinaai/jina-embedding-b-en-v1
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: test eval
          type: test-eval
        metrics:
          - type: cosine_accuracy@1
            value: 0.8625954198473282
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9961832061068703
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8625954198473282
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.33206106870229
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19999999999999998
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09999999999999999
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8625954198473282
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9961832061068703
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9460250731496836
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9271628498727736
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9271628498727736
            name: Cosine Map@100

SentenceTransformer based on jinaai/jina-embedding-b-en-v1

This is a sentence-transformers model finetuned from jinaai/jina-embedding-b-en-v1. 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: jinaai/jina-embedding-b-en-v1
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
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': False}) with Transformer model: T5EncoderModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

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 = [
    "I'd like to know the percentage of large cap in my investments.",
    'Can you show what percentage of my portfolio consists of large cap',
    'Show my riskiest holdings',
]
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: test-eval
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8626
cosine_accuracy@3	0.9962
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.8626
cosine_precision@3	0.3321
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.8626
cosine_recall@3	0.9962
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.946
cosine_mrr@10	0.9272
cosine_map@100	0.9272

Training Details

Training Datasets

Unnamed Dataset

Size: 1,310 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 4 tokens
mean: 10.62 tokens
max: 22 tokens

min: 4 tokens
mean: 9.06 tokens
max: 17 tokens

min: 1.0
mean: 1.0
max: 1.0

	sentence_0	sentence_1	label
type	string	string	float
details	min: 4 tokens mean: 10.62 tokens max: 22 tokens	min: 4 tokens mean: 9.06 tokens max: 17 tokens	min: 1.0 mean: 1.0 max: 1.0

Samples:

sentence_0	sentence_1	label
`are there any of my funds that are lagging behind`	`do I hold any funds that haven't been performing well`	`1.0`
`Which sectors are performing the best in my portfolio?`	`What are my best performing sectors?`	`1.0`
`List some of my top holdings`	`Show some of my best performing holdings`	`1.0`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Unnamed Dataset

Size: 1,310 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 4 tokens
mean: 10.68 tokens
max: 22 tokens

min: 4 tokens
mean: 9.13 tokens
max: 17 tokens

min: 1.0
mean: 1.0
max: 1.0

	sentence_0	sentence_1	label
type	string	string	float
details	min: 4 tokens mean: 10.68 tokens max: 22 tokens	min: 4 tokens mean: 9.13 tokens max: 17 tokens	min: 1.0 mean: 1.0 max: 1.0

Samples:

sentence_0	sentence_1	label
`I need my portfolio to hit 1000% returns by next month`	`make my portfolio return 1000% by next month`	`1.0`
`What are my stocks?`	`Show my stocks`	`1.0`
`I'd like to know my sector distribution.`	`What is my sector allocation?`	`1.0`

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
num_train_epochs: 15
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: 32
per_device_eval_batch_size: 32
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: 15
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}
tp_size: 0
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
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
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss	test-eval_cosine_ndcg@10
1.0	82	-	0.8929
2.0	164	-	0.9007
3.0	246	-	0.9112
4.0	328	-	0.9188
5.0	410	-	0.9285
6.0	492	-	0.9286
6.0976	500	0.2352	0.9291
7.0	574	-	0.9356
8.0	656	-	0.9404
9.0	738	-	0.9406
10.0	820	-	0.9434
11.0	902	-	0.9424
12.0	984	-	0.9455
12.1951	1000	0.164	0.9460

Framework Versions

Python: 3.10.16
Sentence Transformers: 4.1.0
Transformers: 4.51.3
PyTorch: 2.7.0
Accelerate: 1.6.0
Datasets: 3.5.0
Tokenizers: 0.21.1

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}
}