metadata

language:
  - en
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:11002
  - loss:MultipleNegativesRankingLoss
base_model: jinaai/jina-embeddings-v2-base-en
widget:
  - source_sentence: Man jumps alone on a desert road with mountains in the background.
    sentences:
      - A man jumps on the desert road
      - A man plays a silver electric guitar.
      - A man doesnt jump on the desert road
  - source_sentence: Players from two teams tangle together in pursuit of a flying rugby ball.
    sentences:
      - Two teams playing.
      - Two teams not playing.
      - Men are dancing in the street.
  - source_sentence: The team won the game in the final minute.
    sentences:
      - In the final minute, the team won the game.
      - The team lost the game in the final minute.
      - >-
        For their anniversary, they took a hike through the mountains, enjoying
        the peace and quiet of nature.
  - source_sentence: He finished reading the book in one sitting.
    sentences:
      - He struggled to finish the book and took a week to read it.
      - In one sitting, he finished reading the book.
      - jazz players create spontaneous superior orchestra
  - source_sentence: Paint preserves wood
    sentences:
      - Coating protects timber
      - timber coating protects
      - Single cell life came before complex creatures
datasets:
  - bwang0911/word-orders-triplet
  - jinaai/negation-dataset
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on jinaai/jina-embeddings-v2-base-en

This is a sentence-transformers model finetuned from jinaai/jina-embeddings-v2-base-en on the word_orders and negation_dataset datasets. 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: jinaai/jina-embeddings-v2-base-en
Maximum Sequence Length: 128 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Datasets:
- word_orders
- negation_dataset
Language: en

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': 128, 'do_lower_case': False}) with Transformer model: JinaBertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("bwang0911/word-order-jina")
# Run inference
sentences = [
    'Paint preserves wood',
    'Coating protects timber',
    'timber coating protects',
]
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]

Training Details

Training Datasets

word_orders

Dataset: word_orders at 99609ac
Size: 1,002 training samples
Columns: anchor, pos, and neg

Approximate statistics based on the first 1000 samples:

	anchor	pos	neg
type	string	string	string
details	min: 5 tokens mean: 12.34 tokens max: 32 tokens	min: 5 tokens mean: 12.1 tokens max: 30 tokens	min: 5 tokens mean: 11.51 tokens max: 24 tokens

Samples:

anchor	pos	neg
`The river flows from the mountains to the sea`	`Water travels from mountain peaks to ocean`	`The river flows from the sea to the mountains`
`Train departs London for Paris`	`Railway journey from London heading to Paris`	`Train departs Paris for London`
`Cargo ship sails from Shanghai to Singapore`	`Maritime route Shanghai to Singapore`	`Cargo ship sails from Singapore to Shanghai`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20,
    "similarity_fct": "cos_sim"
}

negation_dataset

Dataset: negation_dataset at cd02256
Size: 10,000 training samples
Columns: anchor, entailment, and negative

Approximate statistics based on the first 1000 samples:

	anchor	entailment	negative
type	string	string	string
details	min: 6 tokens mean: 16.48 tokens max: 44 tokens	min: 4 tokens mean: 9.63 tokens max: 31 tokens	min: 5 tokens mean: 10.46 tokens max: 32 tokens

Samples:

anchor	entailment	negative
`Two young girls are playing outside in a non-urban environment.`	`Two girls are playing outside.`	`Two girls are not playing outside.`
`A man with a red shirt is watching another man who is standing on top of a attached cart filled to the top.`	`A man is standing on top of a cart.`	`A man is not standing on top of a cart.`
`A man in a blue shirt driving a Segway type vehicle.`	`A person is riding a motorized vehicle.`	`A person is not riding a motorized vehicle.`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 128
warmup_ratio: 0.1
fp16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 128
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: 5e-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: linear
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: True
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: False
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
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
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss
0.1149	10	2.0411
0.2299	20	1.5167
0.3448	30	0.64
0.4598	40	0.6058
0.5747	50	0.6042
0.6897	60	0.4193
0.8046	70	0.5208
0.9195	80	0.4864
1.0345	90	0.4145
1.1494	100	0.69
1.2644	110	0.9602
1.3793	120	0.2539
1.4943	130	0.2558
1.6092	140	0.2769
1.7241	150	0.2154
1.8391	160	0.293
1.9540	170	0.2598
2.0690	180	0.2113
2.1839	190	0.9366
2.2989	200	0.2121
2.4138	210	0.1486
2.5287	220	0.1765
2.6437	230	0.1438
2.7586	240	0.1589
2.8736	250	0.1869
2.9885	260	0.1682

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.3.1
Transformers: 4.46.0
PyTorch: 2.5.1+cu124
Accelerate: 1.1.1
Datasets: 3.1.0
Tokenizers: 0.20.1

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",
}

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}
}