metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:264
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
  - source_sentence: OpenAI models
    sentences:
      - >-
        What is the name of the checkpoint obtained after fine-tuning and
        additional RL process?
      - >-
        Which models showed performance gains even after memorization
        performance had saturated?
      - >-
        What are the four techniques used for inducing knowledge acquisition in
        fine-tuned models?
  - source_sentence: Section 2.3 is about TRAINING.
    sentences:
      - What is the section 2.3 about?
      - >-
        In which publication and volume was the paper "Retrieval-augmented
        generation for knowledge-intensive nlp tasks" published?
      - >-
        Who are the authors of the paper "Removing rlhf protections in gpt-4 via
        fine-tuning"?
  - source_sentence: >-
      The main advantage of the mentioned approach over exact string matching is
      that it is significantly better at reducing false negatives (missing
      correct responses).
    sentences:
      - >-
        Who is the performance on the generalization task (Vignettes) compared
        to the Medical dataset questions?
      - >-
        Q:Where was the paper "Training language models to follow instructions
        with human feedback" published?
      - >-
        What is the main advantage of the mentioned approach over exact string
        matching?
  - source_sentence: Chunting Zhou, Pengfei Liu, Puxin Xu.
    sentences:
      - >-
        What type of questions are generated from each Python file in
        Scikit-Learn’s repository?
      - >-
        Who are the first three authors of the paper "Lima: Less is more for
        alignment"?
      - >-
        What model is used to generate QA pairs and rephrase them as short
        clinical vignettes?
  - source_sentence: >-
      The topic of the first paper is "Fine-tuning gpt-3 for russian text
      summarization."
    sentences:
      - >-
        What is the model's response when the date is changed to September 8,
        2030?
      - What is the topic of the first paper mentioned in the text?
      - >-
        What is the prompt used in the masking example for the given fact
        statement?
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: 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: 0.26666666666666666
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.3333333333333333
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.36666666666666664
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.5666666666666667
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.26666666666666666
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.11111111111111112
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.07333333333333333
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.05666666666666668
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.26666666666666666
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.3333333333333333
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.36666666666666664
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5666666666666667
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.3838336301118898
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.3309259259259259
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.34343436271505323
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.26666666666666666
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.3333333333333333
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.36666666666666664
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.5
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.26666666666666666
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.11111111111111112
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.07333333333333333
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.05000000000000002
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.26666666666666666
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.3333333333333333
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.36666666666666664
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.36661904860357913
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.32652116402116405
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.3453772498337715
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.23333333333333334
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.3333333333333333
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.36666666666666664
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.5333333333333333
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.23333333333333334
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.11111111111111112
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.07333333333333333
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.053333333333333344
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.23333333333333334
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.3333333333333333
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.36666666666666664
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5333333333333333
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.35800954456310863
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.3059259259259258
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.31868615950830104
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.2
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.36666666666666664
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.43333333333333335
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.5333333333333333
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.2
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.12222222222222222
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.08666666666666668
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.053333333333333344
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.2
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.36666666666666664
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.43333333333333335
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5333333333333333
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.3480374940126532
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.2912037037037037
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.2984413665207766
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.26666666666666666
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.3333333333333333
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.4
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.4666666666666667
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.26666666666666666
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.11111111111111112
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.08000000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.046666666666666676
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.26666666666666666
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.3333333333333333
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.4
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.4666666666666667
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.3487600459577948
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.31277777777777777
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.3249634443232781
            name: Cosine Map@100

BGE base Financial Matryoshka

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5 on the json dataset. 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 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- json
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("willy-arison/bge-base-financial-willy")
# Run inference
sentences = [
    'The topic of the first paper is "Fine-tuning gpt-3 for russian text summarization."',
    'What is the topic of the first paper mentioned in the text?',
    "What is the model's response when the date is changed to September 8, 2030?",
]
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 with these parameters:
```
{
    "truncate_dim": 768
}
```

Metric	Value
cosine_accuracy@1	0.2667
cosine_accuracy@3	0.3333
cosine_accuracy@5	0.3667
cosine_accuracy@10	0.5667
cosine_precision@1	0.2667
cosine_precision@3	0.1111
cosine_precision@5	0.0733
cosine_precision@10	0.0567
cosine_recall@1	0.2667
cosine_recall@3	0.3333
cosine_recall@5	0.3667
cosine_recall@10	0.5667
cosine_ndcg@10	0.3838
cosine_mrr@10	0.3309
cosine_map@100	0.3434

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 512
}
```

Metric	Value
cosine_accuracy@1	0.2667
cosine_accuracy@3	0.3333
cosine_accuracy@5	0.3667
cosine_accuracy@10	0.5
cosine_precision@1	0.2667
cosine_precision@3	0.1111
cosine_precision@5	0.0733
cosine_precision@10	0.05
cosine_recall@1	0.2667
cosine_recall@3	0.3333
cosine_recall@5	0.3667
cosine_recall@10	0.5
cosine_ndcg@10	0.3666
cosine_mrr@10	0.3265
cosine_map@100	0.3454

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 256
}
```

Metric	Value
cosine_accuracy@1	0.2333
cosine_accuracy@3	0.3333
cosine_accuracy@5	0.3667
cosine_accuracy@10	0.5333
cosine_precision@1	0.2333
cosine_precision@3	0.1111
cosine_precision@5	0.0733
cosine_precision@10	0.0533
cosine_recall@1	0.2333
cosine_recall@3	0.3333
cosine_recall@5	0.3667
cosine_recall@10	0.5333
cosine_ndcg@10	0.358
cosine_mrr@10	0.3059
cosine_map@100	0.3187

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 128
}
```

Metric	Value
cosine_accuracy@1	0.2
cosine_accuracy@3	0.3667
cosine_accuracy@5	0.4333
cosine_accuracy@10	0.5333
cosine_precision@1	0.2
cosine_precision@3	0.1222
cosine_precision@5	0.0867
cosine_precision@10	0.0533
cosine_recall@1	0.2
cosine_recall@3	0.3667
cosine_recall@5	0.4333
cosine_recall@10	0.5333
cosine_ndcg@10	0.348
cosine_mrr@10	0.2912
cosine_map@100	0.2984

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator with these parameters:
```
{
    "truncate_dim": 64
}
```

Metric	Value
cosine_accuracy@1	0.2667
cosine_accuracy@3	0.3333
cosine_accuracy@5	0.4
cosine_accuracy@10	0.4667
cosine_precision@1	0.2667
cosine_precision@3	0.1111
cosine_precision@5	0.08
cosine_precision@10	0.0467
cosine_recall@1	0.2667
cosine_recall@3	0.3333
cosine_recall@5	0.4
cosine_recall@10	0.4667
cosine_ndcg@10	0.3488
cosine_mrr@10	0.3128
cosine_map@100	0.325

Training Details

Training Dataset

json

Dataset: json
Size: 264 training samples
Columns: positive and anchor
Approximate statistics based on the first 264 samples:
positive anchor
type string string
details
min: 3 tokens
mean: 15.37 tokens
max: 61 tokens

min: 9 tokens
mean: 18.22 tokens
max: 37 tokens

	positive	anchor
type	string	string
details	min: 3 tokens mean: 15.37 tokens max: 61 tokens	min: 9 tokens mean: 18.22 tokens max: 37 tokens

Samples:

positive	anchor
`Hospital systems may wish to update an LLM with their current medical guidelines.`	`Give an example of a specific domain or industry that might want to update a language model with their own knowledge.`
`The Gemini models struggle to learn a significant proportion of the data even after 20 or 30 epochs.`	`How do the Gemini models perform in learning the training data compared to the OpenAI models?`
`Anthropic, Google, OpenAI`	`Which companies have contributed to the rapid iteration and evolution of Large Language Models?`

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
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
torch_empty_cache_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: 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: 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: 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: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
1.0	1	0.3407	0.3622	0.3385	0.3376	0.3703
2.0	2	0.3739	0.3652	0.3634	0.3429	0.3613
3.0	3	0.3742	0.3666	0.3584	0.3495	0.3504
4.0	4	0.3838	0.3666	0.3580	0.3480	0.3488

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.13
Sentence Transformers: 4.1.0
Transformers: 4.52.4
PyTorch: 2.6.0+cu124
Accelerate: 1.8.1
Datasets: 3.6.0
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",
}

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}
}