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BGE base ArgillaSDK Matryoshka

Name: bge-base-argilla-sdk-matryoshka
Availability: InStock

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5. 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 tokens
Similarity Function: Cosine Similarity

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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("plaguss/bge-base-argilla-sdk-matryoshka")
# Run inference
sentences = [
    'hide: footer\n\nrg.Argilla\n\nTo interact with the Argilla server from python you can use the Argilla class. The Argilla client is used to create, get, update, and delete all Argilla resources, such as workspaces, users, datasets, and records.\n\nUsage Examples\n\nConnecting to an Argilla server\n\nTo connect to an Argilla server, instantiate the Argilla class and pass the api_url of the server and the api_key to authenticate.\n\n```python\nimport argilla_sdk as rg',
    'Can the Argilla class be employed to streamline dataset administration tasks in my Argilla server setup?',
    'The Argilla flowers were blooming beautifully in the garden.',
]
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

Metric	Value
cosine_accuracy@1	0.1327
cosine_accuracy@3	0.2857
cosine_accuracy@5	0.3878
cosine_accuracy@10	0.5204
cosine_precision@1	0.1327
cosine_precision@3	0.0952
cosine_precision@5	0.0776
cosine_precision@10	0.052
cosine_recall@1	0.1327
cosine_recall@3	0.2857
cosine_recall@5	0.3878
cosine_recall@10	0.5204
cosine_ndcg@10	0.3086
cosine_mrr@10	0.2432
cosine_map@100	0.2604

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.102
cosine_accuracy@3	0.2755
cosine_accuracy@5	0.3878
cosine_accuracy@10	0.5102
cosine_precision@1	0.102
cosine_precision@3	0.0918
cosine_precision@5	0.0776
cosine_precision@10	0.051
cosine_recall@1	0.102
cosine_recall@3	0.2755
cosine_recall@5	0.3878
cosine_recall@10	0.5102
cosine_ndcg@10	0.2942
cosine_mrr@10	0.2264
cosine_map@100	0.2426

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.1224
cosine_accuracy@3	0.2755
cosine_accuracy@5	0.3878
cosine_accuracy@10	0.5
cosine_precision@1	0.1224
cosine_precision@3	0.0918
cosine_precision@5	0.0776
cosine_precision@10	0.05
cosine_recall@1	0.1224
cosine_recall@3	0.2755
cosine_recall@5	0.3878
cosine_recall@10	0.5
cosine_ndcg@10	0.2931
cosine_mrr@10	0.2291
cosine_map@100	0.2445

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0918
cosine_accuracy@3	0.2551
cosine_accuracy@5	0.3163
cosine_accuracy@10	0.4694
cosine_precision@1	0.0918
cosine_precision@3	0.085
cosine_precision@5	0.0633
cosine_precision@10	0.0469
cosine_recall@1	0.0918
cosine_recall@3	0.2551
cosine_recall@5	0.3163
cosine_recall@10	0.4694
cosine_ndcg@10	0.2629
cosine_mrr@10	0.1992
cosine_map@100	0.2165

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.0816
cosine_accuracy@3	0.2551
cosine_accuracy@5	0.3163
cosine_accuracy@10	0.4796
cosine_precision@1	0.0816
cosine_precision@3	0.085
cosine_precision@5	0.0633
cosine_precision@10	0.048
cosine_recall@1	0.0816
cosine_recall@3	0.2551
cosine_recall@5	0.3163
cosine_recall@10	0.4796
cosine_ndcg@10	0.2611
cosine_mrr@10	0.194
cosine_map@100	0.2059

Training Details

Training Dataset

Unnamed Dataset

Size: 882 training samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 6 tokens
mean: 90.85 tokens
max: 198 tokens
min: 8 tokens
mean: 25.44 tokens
max: 91 tokens
min: 10 tokens
mean: 22.33 tokens
max: 61 tokens

Samples:

anchor	positive	negative
``[object Object]!!! note "Update the metadata"[object Object] ThemetadataofRecordobject is a python dictionary. So to update the metadata of a record, you can iterate over the records and update the metadata by key or usingmetadata.update`. After that, you should update the records in the dataset.	`Can I use Argilla to annotate the metadata of Record objects and update them in the dataset?`	`The beautiful scenery of the Argilla valley in Italy is perfect for a relaxing summer vacation.`
`git checkout [branch-name][object Object]git rebase [default-branch][object Object][object Object]sh[object Object][object Object]Add the changes to the staging area[object Object][object Object]git add filename[object Object][object Object]Commit the changes by writing a proper message[object Object][object Object]git commit -m "commit-message"[object Object][object Object]Push the changes to your fork`	`Can I commit Argilla's annotation changes and push them to a forked project repository after rebasing from the default branch?`	`The beautiful beach in Argilla, Spain, is a popular spot for surfers to catch a wave and enjoy the sunny weather.`
Accessing Record Attributes[object Object][object Object]The Record object has suggestions, responses, metadata, and vectors attributes that can be accessed directly whilst iterating over records in a dataset.[object Object][object Object]python[object Object]for record in dataset.records([object Object] with_suggestions=True,[object Object] with_responses=True,[object Object] with_metadata=True,[object Object] with_vectors=True[object Object] ):[object Object] print(record.suggestions)[object Object] print(record.responses)[object Object] print(record.metadata)[object Object] print(record.vectors)	`Is it possible to retrieve the suggestions, responses, metadata, and vectors of a Record object at the same time when iterating over a dataset in Argilla?`	`The new hiking trail offered breathtaking suggestions for scenic views, responses to environmental concerns, and metadata about the surrounding ecosystem, but it lacked vectors for navigation.`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "TripletLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_eval_batch_size: 4
gradient_accumulation_steps: 4
learning_rate: 2e-05
lr_scheduler_type: cosine
warmup_ratio: 0.1
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 8
per_device_eval_batch_size: 4
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 4
eval_accumulation_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: 3
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: 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
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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_128_cosine_map@100	dim_256_cosine_map@100	dim_512_cosine_map@100	dim_64_cosine_map@100	dim_768_cosine_map@100
0.1802	5	21.701	-	-	-	-	-
0.3604	10	21.7449	-	-	-	-	-
0.5405	15	21.7453	-	-	-	-	-
0.7207	20	21.7168	-	-	-	-	-
0.9009	25	21.6945	-	-	-	-	-
0.973	27	-	0.2165	0.2445	0.2426	0.2059	0.2604
1.0811	30	21.7248	-	-	-	-	-
1.2613	35	21.7322	-	-	-	-	-
1.4414	40	21.7367	-	-	-	-	-
1.6216	45	21.6821	-	-	-	-	-
1.8018	50	21.8392	-	-	-	-	-
1.9820	55	21.6441	0.2165	0.2445	0.2426	0.2059	0.2604
2.1622	60	21.8154	-	-	-	-	-
2.3423	65	21.7098	-	-	-	-	-
2.5225	70	21.6447	-	-	-	-	-
2.7027	75	21.6033	-	-	-	-	-
2.8829	80	21.8271	-	-	-	-	-
2.9189	81	-	0.2165	0.2445	0.2426	0.2059	0.2604

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.8
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.1.2
Accelerate: 0.31.0
Datasets: 2.19.2
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

bibtex

@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

bibtex

@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}
}

TripletLoss

bibtex

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification}, 
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

	anchor	positive	negative
type	string	string	string
details	min: 6 tokens mean: 90.85 tokens max: 198 tokens	min: 8 tokens mean: 25.44 tokens max: 91 tokens	min: 10 tokens mean: 22.33 tokens max: 61 tokens