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
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({'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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'A woman wearing all white and eating, walks next to a man holding a briefcase.',
    'A married couple is walking next to each other.',
    'An older man drinks his juice as he waits for his daughter to get off work.',
]
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, 0.9970, 0.9972],
#         [0.9970, 1.0000, 0.9964],
#         [0.9972, 0.9964, 1.0000]])

Evaluation

Metrics

Semantic Similarity

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

Metric	Value
pearson_cosine	0.2144
spearman_cosine	0.3157

Training Details

Training Dataset

all-nli

Dataset: all-nli at d482672
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: 25.65 tokens
max: 52 tokens
min: 6 tokens
mean: 12.25 tokens
max: 29 tokens
min: 0.0
mean: 0.51
max: 1.0

Samples:

sentence1	sentence2	score
`A woman in a green jacket and hood over her head looking towards a valley.`	`The woman is nake.`	`0.0`
`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 walk across the street to get away from a red shirted person who is chasing them.`	`0.5`
`An older man sits with his orange juice at a small table in a coffee shop while employees in bright colored shirts smile in the background.`	`An elderly man sits in a small shop.`	`0.5`

Loss: CoSENTLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "pairwise_cos_sim" 4}

Evaluation Dataset

all-nli

Dataset: all-nli at d482672
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: 26.05 tokens
max: 52 tokens
min: 5 tokens
mean: 10.9 tokens
max: 21 tokens
min: 0.0
mean: 0.47
max: 1.0

Samples:

sentence1	sentence2	score
`An older man sits with his orange juice at a small table in a coffee shop while employees in bright colored shirts smile in the background.`	`An older man drinks his juice as he waits for his daughter to get off work.`	`0.5`
`Woman in white in foreground and a man slightly behind walking with a sign for John's Pizza and Gyro in the background.`	`They are protesting outside the capital.`	`0.0`
`A man and a woman cross the street in front of a pizza and gyro restaurant.`	`Near a couple of restaurants, two people walk across the street.`	`1.0`

Loss: CoSENTLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "pairwise_cos_sim" 4}

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	3.1860	-	-
0.5	5	3.4406	-	-
1.0	10	3.0292	2.6321	0.3157

The bold row denotes the saved checkpoint.

Training Time

Training: 4.6 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

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",
}

CoSENTLoss

bibtex

@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}
}

	sentence1	sentence2	score
type	string	string	float
details	min: 10 tokens mean: 25.65 tokens max: 52 tokens	min: 6 tokens mean: 12.25 tokens max: 29 tokens	min: 0.0 mean: 0.51 max: 1.0

AKL126/result_model

SentenceTransformer based on abdeljalilELmajjodi/model

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Semantic Similarity

Training Details

Training Dataset

all-nli

Evaluation Dataset

all-nli

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Training Time

Framework Versions

Citation

BibTeX

Sentence Transformers

CoSENTLoss

AKL126/
result_model