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: 0.4
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.5666666666666667
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6666666666666666
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.4
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.16666666666666663
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.11333333333333336
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.06666666666666668
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.4
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.5
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.5666666666666667
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.6666666666666666
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.520075424207518
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.4745238095238095
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.4911198244479979
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.36666666666666664
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.6333333333333333
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.7
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.36666666666666664
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.16666666666666663
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.12666666666666668
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.07000000000000002
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.36666666666666664
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.5
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.6333333333333333
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.7
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5276816602931818
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.4730555555555555
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.48591804976272246
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.43333333333333335
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5666666666666667
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.5666666666666667
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6666666666666666
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.43333333333333335
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.18888888888888886
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.11333333333333336
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.06666666666666668
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.43333333333333335
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.5666666666666667
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.5666666666666667
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.6666666666666666
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5384601202448717
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.4984126984126984
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.5093647325589986
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.3
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.6
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.3
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.16666666666666663
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.12000000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.06000000000000001
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.3
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.5
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.6
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.6
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.4445103975371312
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.3938888888888889
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.40394887488531556
            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: 0.43333333333333335
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.5
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.5333333333333333
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.26666666666666666
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.14444444444444443
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.05333333333333334
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.26666666666666666
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.43333333333333335
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.5
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5333333333333333
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.40728018212553163
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.36587301587301585
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.384350058982412
            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-willy3")
# 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	0.4
cosine_accuracy@3	0.5
cosine_accuracy@5	0.5667
cosine_accuracy@10	0.6667
cosine_precision@1	0.4
cosine_precision@3	0.1667
cosine_precision@5	0.1133
cosine_precision@10	0.0667
cosine_recall@1	0.4
cosine_recall@3	0.5
cosine_recall@5	0.5667
cosine_recall@10	0.6667
cosine_ndcg@10	0.5201
cosine_mrr@10	0.4745
cosine_map@100	0.4911

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.3667
cosine_accuracy@3	0.5
cosine_accuracy@5	0.6333
cosine_accuracy@10	0.7
cosine_precision@1	0.3667
cosine_precision@3	0.1667
cosine_precision@5	0.1267
cosine_precision@10	0.07
cosine_recall@1	0.3667
cosine_recall@3	0.5
cosine_recall@5	0.6333
cosine_recall@10	0.7
cosine_ndcg@10	0.5277
cosine_mrr@10	0.4731
cosine_map@100	0.4859

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.4333
cosine_accuracy@3	0.5667
cosine_accuracy@5	0.5667
cosine_accuracy@10	0.6667
cosine_precision@1	0.4333
cosine_precision@3	0.1889
cosine_precision@5	0.1133
cosine_precision@10	0.0667
cosine_recall@1	0.4333
cosine_recall@3	0.5667
cosine_recall@5	0.5667
cosine_recall@10	0.6667
cosine_ndcg@10	0.5385
cosine_mrr@10	0.4984
cosine_map@100	0.5094

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.3
cosine_accuracy@3	0.5
cosine_accuracy@5	0.6
cosine_accuracy@10	0.6
cosine_precision@1	0.3
cosine_precision@3	0.1667
cosine_precision@5	0.12
cosine_precision@10	0.06
cosine_recall@1	0.3
cosine_recall@3	0.5
cosine_recall@5	0.6
cosine_recall@10	0.6
cosine_ndcg@10	0.4445
cosine_mrr@10	0.3939
cosine_map@100	0.4039

Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.2667
cosine_accuracy@3	0.4333
cosine_accuracy@5	0.5
cosine_accuracy@10	0.5333
cosine_precision@1	0.2667
cosine_precision@3	0.1444
cosine_precision@5	0.1
cosine_precision@10	0.0533
cosine_recall@1	0.2667
cosine_recall@3	0.4333
cosine_recall@5	0.5
cosine_recall@10	0.5333
cosine_ndcg@10	0.4073
cosine_mrr@10	0.3659
cosine_map@100	0.3844

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: 50
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: 50
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	Training Loss	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
1.0	1	-	0.3742	0.3666	0.3584	0.3495	0.3504
2.0	2	-	0.3939	0.3767	0.3611	0.3626	0.3302
3.0	3	-	0.3844	0.3896	0.3873	0.3589	0.3388
4.0	4	-	0.3885	0.4011	0.3989	0.3581	0.3515
5.0	5	-	0.3790	0.4024	0.3984	0.3658	0.3563
6.0	6	-	0.3815	0.4047	0.3998	0.3904	0.3573
7.0	7	-	0.3868	0.4164	0.4038	0.4024	0.3512
8.0	8	-	0.3971	0.4224	0.4047	0.3933	0.3518
9.0	9	-	0.3971	0.4224	0.4062	0.3933	0.3536
10.0	10	69.6696	0.3971	0.4353	0.4062	0.4056	0.3579
1.0	1	-	0.3971	0.4353	0.4062	0.4056	0.3579
2.0	2	-	0.3885	0.4454	0.4181	0.3947	0.3558
3.0	3	-	0.4141	0.4482	0.4424	0.4020	0.3584
4.0	4	-	0.4215	0.4502	0.4443	0.3854	0.3571
5.0	5	-	0.4402	0.4596	0.4366	0.3874	0.3671
6.0	6	-	0.4402	0.4500	0.4372	0.3901	0.3645
7.0	7	-	0.4551	0.4557	0.4390	0.4045	0.3489
8.0	8	-	0.4658	0.4637	0.4390	0.3865	0.3487
9.0	9	-	0.4658	0.4741	0.4586	0.3827	0.3587
10.0	10	41.2785	0.4590	0.4741	0.4510	0.3941	0.3600
11.0	11	-	0.4590	0.4741	0.4515	0.3841	0.3604
12.0	12	-	0.4605	0.4802	0.4556	0.3864	0.3945
13.0	13	-	0.4590	0.4792	0.4433	0.3864	0.3768
14.0	14	-	0.4595	0.4844	0.4447	0.3964	0.3812
15.0	15	-	0.4605	0.4945	0.4447	0.3974	0.3869
16.0	16	-	0.4611	0.4951	0.4465	0.3974	0.3861
17.0	17	-	0.4605	0.4951	0.4465	0.3974	0.3869
18.0	18	-	0.4605	0.4955	0.4588	0.4097	0.3905
19.0	19	-	0.4605	0.4951	0.4479	0.3974	0.3869
20.0	20	24.2435	0.4605	0.4951	0.4479	0.3964	0.3869
1.0	1	-	0.4605	0.4955	0.4588	0.4097	0.3905
2.0	2	-	0.4613	0.4951	0.4472	0.3969	0.3779
3.0	3	-	0.4736	0.4955	0.4697	0.3979	0.3844
4.0	4	-	0.4736	0.4958	0.4705	0.3985	0.3844
5.0	5	-	0.4739	0.4993	0.4724	0.3965	0.3873
6.0	6	-	0.4828	0.4916	0.4756	0.4146	0.3773
7.0	7	-	0.4832	0.5012	0.5002	0.4023	0.3817
8.0	8	-	0.4928	0.5012	0.5057	0.4061	0.3802
9.0	9	-	0.4947	0.5005	0.5192	0.4184	0.4012
10.0	10	14.397	0.4951	0.5105	0.5174	0.4151	0.3935
11.0	11	-	0.4968	0.5114	0.5218	0.4151	0.3935
12.0	12	-	0.4973	0.5225	0.5151	0.4328	0.3983
13.0	13	-	0.4979	0.5214	0.5147	0.4318	0.3926
14.0	14	-	0.5004	0.5229	0.5147	0.4151	0.4023
15.0	15	-	0.4989	0.5258	0.5167	0.4318	0.4066
16.0	16	-	0.5033	0.5269	0.5167	0.4336	0.4091
17.0	17	-	0.5048	0.5282	0.5167	0.4336	0.3995
18.0	18	-	0.5048	0.5308	0.5175	0.4336	0.4091
19.0	19	-	0.5033	0.5207	0.5175	0.4325	0.4091
20.0	20	8.6526	0.5033	0.5207	0.5160	0.4325	0.4203
21.0	21	-	0.5156	0.5207	0.5214	0.4336	0.4202
22.0	22	-	0.5156	0.5218	0.5229	0.4306	0.4202
23.0	23	-	0.5171	0.5222	0.5175	0.4354	0.4100
24.0	24	-	0.5156	0.5207	0.5175	0.4354	0.4096
25.0	25	-	0.5252	0.5222	0.5204	0.4311	0.4082
26.0	26	-	0.5267	0.5222	0.5160	0.4355	0.4316
27.0	27	-	0.5267	0.5218	0.5160	0.4362	0.4316
28.0	28	-	0.5171	0.5218	0.5175	0.4372	0.4329
29.0	29	-	0.5171	0.5177	0.5175	0.4372	0.4358
30.0	30	5.2469	0.5171	0.5207	0.5242	0.4420	0.4252
31.0	31	-	0.5062	0.5191	0.5365	0.4474	0.4262
32.0	32	-	0.5062	0.5238	0.5365	0.4474	0.4343
33.0	33	-	0.5048	0.5200	0.5279	0.4430	0.4292
34.0	34	-	0.5048	0.5200	0.5269	0.4430	0.4466
35.0	35	-	0.5062	0.5214	0.5269	0.4430	0.4292
36.0	36	-	0.5085	0.5223	0.5269	0.4430	0.4389
37.0	37	-	0.5085	0.5223	0.5262	0.4430	0.4196
38.0	38	-	0.5201	0.5281	0.5385	0.4474	0.4239
39.0	39	-	0.5201	0.5238	0.5385	0.4474	0.4239
40.0	40	4.274	0.5201	0.5267	0.5256	0.4445	0.4196
41.0	41	-	0.5201	0.5223	0.5262	0.4474	0.4239
42.0	42	-	0.5078	0.5277	0.5379	0.4445	0.4073
43.0	43	-	0.5215	0.5281	0.5379	0.4445	0.4196
44.0	44	-	0.5215	0.5291	0.5256	0.4489	0.4196
45.0	45	-	0.5201	0.5277	0.5256	0.4445	0.4196
46.0	46	-	0.5078	0.5277	0.5262	0.4445	0.4196
47.0	47	-	0.5201	0.5277	0.5379	0.4445	0.4073
48.0	48	-	0.5215	0.5291	0.5262	0.4445	0.4073
49.0	49	-	0.5215	0.5291	0.5385	0.4489	0.4116
50.0	50	3.6753	0.5201	0.5277	0.5385	0.4445	0.4073

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}
}