metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:15002
  - loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-large-en-v1.5
widget:
  - source_sentence: >-
      what kind of oil and how much do i need for my toyota tacoma truck and how
      do i do it
    sentences:
      - >-
        Requests to change the system or application's language settings. Users
        may ask to switch to a specific language, such as English, or adjust the
        language preferences to enhance usability.
      - >-
        Requests for step-by-step instructions or guidance on how to change the
        oil in a car. Users seek detailed procedures, tools needed, and tips for
        performing this maintenance task.
      - >-
        Requests to make a reservation at a specific restaurant for a specified
        number of people, time, and under a provided name. Users expect
        confirmation of the booking details.
  - source_sentence: please double check my reservations for six at mani
    sentences:
      - >-
        Requests to verify or confirm existing reservations, typically for
        dining or events. Users provide details about the reservation and ask
        for confirmation that it is correctly recorded.
      - >-
        Requests for details about an insurance policy, including coverage,
        benefits, and exclusions. Users may inquire about specific aspects like
        health benefits or policy terms.
      - >-
        Requests to create, manage, or customize timers for various tasks or
        activities. Users can define the duration, purpose, or type of the timer
        and receive notifications or alerts when the timer reaches its set time.
  - source_sentence: what are some good ethiopian restaurants in queens
    sentences:
      - >-
        Requests for the meaning or definition of words. Users may inquire about
        the definitions of uncommon, complex, or unfamiliar terms, aiming to
        gain a clear understanding or contextual usage of the word in question.
      - >-
        Requests to assist with paying bills, such as utilities, credit cards,
        or other services. Users may specify the bill type, amount, and source
        account for the payment.
      - >-
        Requests for recommendations or suggestions for dining options. Users
        may ask for specific cuisine types, locations, or general ideas on where
        to eat.
  - source_sentence: are there any expected delays for flight dl123
    sentences:
      - >-
        Requests for travel time or distance to a specific location. Users
        typically seek estimates based on current traffic, routes, or modes of
        transportation to determine the time needed to reach their destination.
      - >-
        Requests for information about flight details, such as boarding times,
        delays, or schedules. Users typically inquire to ensure they are updated
        about their flight's status.
      - >-
        Requests for advice or strategies to improve credit scores. Users may
        seek a detailed plan, tips, or insights into financial habits that can
        lead to a better credit rating.
  - source_sentence: how do i ask about the weather in chinese
    sentences:
      - >-
        Requests related to translating words, phrases, or sentences from one
        language to another. The user may specify the source and target
        languages, and the goal is to provide an accurate and
        context-appropriate translation.
      - >-
        Requests for information about a vehicle's miles per gallon (MPG)
        rating, either in specific conditions like city driving or as an overall
        performance metric. Users may seek guidance on fuel efficiency for their
        car.
      - >-
        Requests for information about a vehicle's miles per gallon (MPG)
        rating, either in specific conditions like city driving or as an overall
        performance metric. Users may seek guidance on fuel efficiency for their
        car.
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 BAAI/bge-large-en-v1.5
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: cosine_accuracy@1
            value: 0.9706666666666667
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9886666666666667
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.992
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9956666666666667
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.9706666666666667
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3295555555555556
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19840000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09956666666666668
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.9706666666666667
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9886666666666667
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.992
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9956666666666667
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9841961906084298
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9804173280423282
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9806052445247627
            name: Cosine Map@100

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

This is a sentence-transformers model finetuned from BAAI/bge-large-en-v1.5. It maps sentences & paragraphs to a 1024-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-large-en-v1.5
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 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': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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("chinchilla04/bge-finetuned-train")
# Run inference
sentences = [
    'how do i ask about the weather in chinese',
    'Requests related to translating words, phrases, or sentences from one language to another. The user may specify the source and target languages, and the goal is to provide an accurate and context-appropriate translation.',
    "Requests for information about a vehicle's miles per gallon (MPG) rating, either in specific conditions like city driving or as an overall performance metric. Users may seek guidance on fuel efficiency for their car.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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	0.9707
cosine_accuracy@3	0.9887
cosine_accuracy@5	0.992
cosine_accuracy@10	0.9957
cosine_precision@1	0.9707
cosine_precision@3	0.3296
cosine_precision@5	0.1984
cosine_precision@10	0.0996
cosine_recall@1	0.9707
cosine_recall@3	0.9887
cosine_recall@5	0.992
cosine_recall@10	0.9957
cosine_ndcg@10	0.9842
cosine_mrr@10	0.9804
cosine_map@100	0.9806

Training Details

Training Dataset

Unnamed Dataset

Size: 15,002 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 4 tokens mean: 10.66 tokens max: 28 tokens	min: 25 tokens mean: 42.6 tokens max: 58 tokens	min: 29 tokens mean: 41.95 tokens max: 58 tokens

Samples:

anchor	positive	negative
`what expression would i use to say i love you if i were an italian`	`Requests related to translating words, phrases, or sentences from one language to another. The user may specify the source and target languages, and the goal is to provide an accurate and context-appropriate translation.`	`Requests involving financial operations, such as transferring money between bank accounts, credit cards, or other financial instruments. Users typically specify the amount, the source account, and the target account, ensuring that the transfer is executed correctly and securely.`
`can you tell me how to say 'i do not speak much spanish', in spanish`	`Requests related to translating words, phrases, or sentences from one language to another. The user may specify the source and target languages, and the goal is to provide an accurate and context-appropriate translation.`	`Requests involving financial operations, such as transferring money between bank accounts, credit cards, or other financial instruments. Users typically specify the amount, the source account, and the target account, ensuring that the transfer is executed correctly and securely.`
`what is the equivalent of, 'life is good' in french`	`Requests related to translating words, phrases, or sentences from one language to another. The user may specify the source and target languages, and the goal is to provide an accurate and context-appropriate translation.`	`Requests involving financial operations, such as transferring money between bank accounts, credit cards, or other financial instruments. Users typically specify the amount, the source account, and the target account, ensuring that the transfer is executed correctly and securely.`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

Size: 3,000 evaluation samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 3 tokens
mean: 11.06 tokens
max: 29 tokens

min: 26 tokens
mean: 36.16 tokens
max: 58 tokens

	anchor	positive
type	string	string
details	min: 3 tokens mean: 11.06 tokens max: 29 tokens	min: 26 tokens mean: 36.16 tokens max: 58 tokens

Samples:

anchor	positive
`in spanish, meet me tomorrow is said how`	`Requests related to translating words, phrases, or sentences from one language to another. The user may specify the source and target languages, and the goal is to provide an accurate and context-appropriate translation.`
`in french, how do i say, see you later`	`Requests related to translating words, phrases, or sentences from one language to another. The user may specify the source and target languages, and the goal is to provide an accurate and context-appropriate translation.`
`how do you say hello in japanese`	`Requests related to translating words, phrases, or sentences from one language to another. The user may specify the source and target languages, and the goal is to provide an accurate and context-appropriate translation.`

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: 32
learning_rate: 1e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.2
load_best_model_at_end: True
optim: adamw_torch_fused

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: 8
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: 1e-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: 0.2
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: 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: False
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
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
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	cosine_ndcg@10
None	0	-	0.2730	0.9055
0.3198	150	-	0.0698	0.9633
0.6397	300	-	0.0642	0.9683
0.9595	450	-	0.0603	0.9763
1.0661	500	1.0338	-	-
1.2793	600	-	0.0612	0.9762
1.5991	750	-	0.0602	0.9802
1.9190	900	-	0.0571	0.9820
2.1322	1000	0.787	-	-
2.2388	1050	-	0.0585	0.9819
2.5586	1200	-	0.0565	0.9842
2.8785	1350	-	0.0578	0.9837
3.1983	1500	0.6768	0.0570	0.9844
3.5181	1650	-	0.0587	0.9837
3.8380	1800	-	0.0584	0.9837
None	0	-	0.0565	0.9842

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.14
Sentence Transformers: 3.3.1
Transformers: 4.46.3
PyTorch: 2.4.0
Accelerate: 1.1.1
Datasets: 3.1.0
Tokenizers: 0.20.3

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}
}