SentenceTransformer based on abdeljalilELmajjodi/model

This is a sentence-transformers model finetuned from abdeljalilELmajjodi/model on the all-nli dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: abdeljalilELmajjodi/model
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Supported Modality: Text
Training Dataset:
- all-nli

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': 1024, 'pooling_mode': 'mean', 'include_prompt': True})
)

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'The school is having a special event in order to show the american culture on how other cultures are dealt with in parties.',
    'A school is hosting an event.',
    'A married couple is walking next to each other.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9874, 0.9852],
#         [0.9874, 1.0000, 0.9922],
#         [0.9852, 0.9922, 1.0000]])

Evaluation

Metrics

Semantic Similarity

Dataset: pair-score-evaluator-dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	-0.4587
spearman_cosine	-0.5207

Training Details

Training Dataset

all-nli

Dataset: all-nli
Size: 80 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 80 samples:
sentence1 sentence2 score
type string string float
details
min: 10 tokens
mean: 24.96 tokens
max: 52 tokens

min: 5 tokens
mean: 11.64 tokens
max: 29 tokens

min: 0.0
mean: 0.5
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 10 tokens mean: 24.96 tokens max: 52 tokens	min: 5 tokens mean: 11.64 tokens max: 29 tokens	min: 0.0 mean: 0.5 max: 1.0

Samples:

sentence1	sentence2	score
`A woman is walking across the street eating a banana, while a man is following with his briefcase.`	`a woman eating a banana crosses a street`	`1.0`
`A Little League team tries to catch a runner sliding into a base in an afternoon game.`	`A team is playing baseball on Saturn.`	`0.0`
`Woman in white in foreground and a man slightly behind walking with a sign for John's Pizza and Gyro in the background.`	`The woman is waiting for a friend.`	`0.5`

Loss: CoSENTLoss with these parameters:

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

Evaluation Dataset

all-nli

Dataset: all-nli
Size: 20 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 20 samples:
sentence1 sentence2 score
type string string float
details
min: 10 tokens
mean: 28.8 tokens
max: 52 tokens

min: 7 tokens
mean: 13.35 tokens
max: 25 tokens

min: 0.0
mean: 0.53
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 10 tokens mean: 28.8 tokens max: 52 tokens	min: 7 tokens mean: 13.35 tokens max: 25 tokens	min: 0.0 mean: 0.53 max: 1.0

Samples:

sentence1	sentence2	score
`A woman wearing all white and eating, walks next to a man holding a briefcase.`	`A married couple is walking next to each other.`	`0.5`
`A woman in a green jacket and hood over her head looking towards a valley.`	`The woman is wearing green.`	`1.0`
`Two adults, one female in white, with shades and one male, gray clothes, walking across a street, away from a eatery with a blurred image of a dark colored red shirted person in the foreground.`	`Two adults walking across a road near the convicted prisoner dressed in red`	`0.5`

Loss: CoSENTLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

num_train_epochs: 1
warmup_steps: 0.05
bf16: True
fp16_full_eval: True
load_best_model_at_end: True
push_to_hub: True
gradient_checkpointing: True

All Hyperparameters

Click to expand

do_predict: False
prediction_loss_only: True
per_device_train_batch_size: 8
per_device_eval_batch_size: 8
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: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_ratio: None
warmup_steps: 0.05
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
enable_jit_checkpoint: False
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
use_cpu: False
seed: 42
data_seed: None
bf16: True
fp16: False
bf16_full_eval: False
fp16_full_eval: True
tf32: None
local_rank: -1
ddp_backend: None
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': 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
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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
push_to_hub: True
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: True
gradient_checkpointing_kwargs: None
include_for_metrics: []
eval_do_concat_batches: True
auto_find_batch_size: False
full_determinism: False
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_num_input_tokens_seen: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
use_cache: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	pair-score-evaluator-dev_spearman_cosine
0.1	1	3.0301	-	-
0.5	5	3.0538	-	-
1.0	10	3.0176	2.468	-0.5207

The bold row denotes the saved checkpoint.

Training Time

Training: 3.0 minutes

Framework Versions

Python: 3.12.13
Sentence Transformers: 5.4.1
Transformers: 5.0.0
PyTorch: 2.10.0+cu128
Accelerate: 1.13.0
Datasets: 4.8.5
Tokenizers: 0.22.2

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",
}

CoSENTLoss

@article{10531646,
    author={Huang, Xiang and Peng, Hao and Zou, Dongcheng and Liu, Zhiwei and Li, Jianxin and Liu, Kay and Wu, Jia and Su, Jianlin and Yu, Philip S.},
    journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
    title={CoSENT: Consistent Sentence Embedding via Similarity Ranking},
    year={2024},
    doi={10.1109/TASLP.2024.3402087}
}