SentenceTransformer based on intfloat/multilingual-e5-base

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-base. It maps inputs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: intfloat/multilingual-e5-base
Maximum Sequence Length: 96 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Supported Modality: Text

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True})
  (2): Normalize({'module_input_name': 'sentence_embedding', 'module_output_name': 'sentence_embedding'})
)

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("chirag1701/resolve72-biencoder-v1")
# Run inference
sentences = [
    'query: q 7 strategic developments | hurdsfield, nd, 235 3',
    'query: q developments strategic 7 | 235 3, hurdsfield, nd',
    'query: lyros boral | ',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.9363, -0.0104],
#         [ 0.9363,  1.0000,  0.0018],
#         [-0.0104,  0.0018,  1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 1,042,218 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 100 samples:

	anchor	positive	negative
type	string	string	string
modality	text	text	text
details	min: 19 tokens mean: 28.59 tokens max: 56 tokens	min: 10 tokens mean: 28.12 tokens max: 58 tokens	min: 9 tokens mean: 27.5 tokens max: 51 tokens

Samples:

anchor	positive	negative
`query: orelee s barbershop \| 1795 westchester drive, high point, nc`	`query: orelee s barbershop \| 1795 westchester drive, nc, high point`	`query: हाई क्रिएटिव सिस्टम्स प्रा लि \| 38/2, mumbai, mumbai city, महाराष्ट्र`
`query: b retail \| 1712 montebello avenue, phoenix, az`	`query: b retail \| phoenix, az, 1712 montebello ave`	`query: shivam it \| 7a/404, alica nagar lokhandwala complex, kandivali east, महाराष्ट्र`
`query: christ chapel \| 2100 cameron drive, unit apartment g, dundalk, md`	`query: christ chape1 \| 2100 cameron dr, dundalk, md`	`query: skyhigh consultancy \| maharashtra, b 102, pratik industrial estate, m g link road bhandup west, mumbai`

Loss: CachedMultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "mini_batch_size": 128,
    "mini_batch_num_tokens": null,
    "gather_across_devices": false,
    "directions": [
        "query_to_doc"
    ],
    "partition_mode": "joint",
    "hardness_mode": null,
    "hardness_strength": 0.0
}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 1024
num_train_epochs: 1
max_steps: 400
learning_rate: 3e-05
warmup_steps: 0.05
bf16: True
seed: 13
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

per_device_train_batch_size: 1024
num_train_epochs: 1
max_steps: 400
learning_rate: 3e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0.05
optim: adamw_torch
optim_args: None
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 1
average_tokens_across_devices: True
max_grad_norm: 1.0
label_smoothing_factor: 0.0
bf16: True
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
project: huggingface
trackio_space_id: None
trackio_bucket_id: None
trackio_static_space_id: None
per_device_eval_batch_size: 8
prediction_loss_only: True
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: None
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_on_each_node: False
enable_jit_checkpoint: False
push_to_hub: False
hub_private_repo: None
hub_model_id: None
hub_strategy: every_save
hub_always_push: False
hub_revision: None
load_best_model_at_end: False
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 13
data_seed: None
use_cpu: False
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
dataloader_multiprocessing_context: None
dataloader_in_order: True
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
ddp_static_graph: None
ddp_backend: None
ddp_timeout: 1800
fsdp: None
fsdp_config: None
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
local_rank: -1
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}
warmup_ratio: None

Training Logs

Epoch	Step	Training Loss
0.0196	20	3.4136
0.0393	40	0.3637
0.0589	60	0.0574
0.0786	80	0.0375
0.0982	100	0.0302
0.1179	120	0.0257
0.1375	140	0.0234
0.1572	160	0.0218
0.1768	180	0.0205
0.1965	200	0.0184
0.2161	220	0.0179
0.2358	240	0.0162
0.2554	260	0.0164
0.2750	280	0.0178
0.2947	300	0.0157
0.3143	320	0.0146
0.3340	340	0.0163
0.3536	360	0.0153
0.3733	380	0.0145
0.3929	400	0.0150

Training Time

Training: 12.6 minutes

Framework Versions

Python: 3.11.16
Sentence Transformers: 6.1.0
Transformers: 5.17.0
PyTorch: 2.5.1+cu121
Accelerate: 1.15.0
Datasets: 5.0.1
Tokenizers: 0.23.2

Additional Resources

Training and Finetuning Embedding Models with Sentence Transformers: the end-to-end guide for training or finetuning Sentence Transformer models.
Introduction to Matryoshka Embedding Models: variable-size embeddings that can be truncated with minimal quality loss.
Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval: post-training compression of embedding vectors.
Multimodal Embedding & Reranker Models with Sentence Transformers: use text, image, audio, and video models through the same API.
Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers: train multimodal embedding models, with a Visual Document Retrieval walkthrough.

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",
}

CachedMultipleNegativesRankingLoss

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}

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Safetensors

Model size

0.3B params

Tensor type

F32

Model tree for chirag1701/resolve72-biencoder-v1

Base model

intfloat/multilingual-e5-base

Finetuned

(170)

this model

Papers for chirag1701/resolve72-biencoder-v1

anchor	positive	negative
`query: orelee s barbershop \| 1795 westchester drive, high point, nc`	`query: orelee s barbershop \| 1795 westchester drive, nc, high point`	`query: हाई क्रिएटिव सिस्टम्स प्रा लि \| 38/2, mumbai, mumbai city, महाराष्ट्र`
`query: b retail \| 1712 montebello avenue, phoenix, az`	`query: b retail \| phoenix, az, 1712 montebello ave`	`query: shivam it \| 7a/404, alica nagar lokhandwala complex, kandivali east, महाराष्ट्र`
`query: christ chapel \| 2100 cameron drive, unit apartment g, dundalk, md`	`query: christ chape1 \| 2100 cameron dr, dundalk, md`	`query: skyhigh consultancy \| maharashtra, b 102, pratik industrial estate, m g link road bhandup west, mumbai`