huggingface.co/CDXV/hack_ai_embbedding_model

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tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:80
  - loss:CoSENTLoss
base_model: abdeljalilELmajjodi/model
widget:
  - source_sentence: Children smiling and waving at camera
    sentences:
      - The kids are frowning
      - The people are eating omelettes.
      - Near a couple of restaurants, two people walk across the street.
  - source_sentence: Two women, holding food carryout containers, hug.
    sentences:
      - two coworkers cross pathes on a street
      - The woman is wearing white.
      - Two groups of rival gang members flipped each other off.
  - source_sentence: >-
      A woman is walking across the street eating a banana, while a man is
      following with his briefcase.
    sentences:
      - >-
        A woman eats a banana and walks across a street, and there is a man
        trailing behind her.
      - An elderly man sits in a small shop.
      - The adults are both male and female.
  - source_sentence: People waiting to get on a train or just getting off.
    sentences:
      - There are people waiting on a train.
      - >-
        The two sisters saw each other across the crowded diner and shared a
        hug, both clutching their doggie bags.
      - There are two woman in this picture.
  - source_sentence: >-
      The school is having a special event in order to show the american culture
      on how other cultures are dealt with in parties.
    sentences:
      - They are protesting outside the capital.
      - A woman ordering pizza.
      - A school is hosting an event.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: SentenceTransformer based on abdeljalilELmajjodi/model
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: pair score evaluator dev
          type: pair-score-evaluator-dev
        metrics:
          - type: pearson_cosine
            value: -0.22939457943411037
            name: Pearson Cosine
          - type: spearman_cosine
            value: -0.18637822325921868
            name: Spearman Cosine

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.',
    'They are protesting outside the capital.',
]
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, 0.9755, 0.9818],
#         [0.9755, 1.0000, 0.9960],
#         [0.9818, 0.9960, 1.0000]])

Evaluation

Metrics

Semantic Similarity

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

Metric	Value
pearson_cosine	-0.2294
spearman_cosine	-0.1864

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: 25.76 tokens
max: 52 tokens

min: 5 tokens
mean: 11.9 tokens
max: 29 tokens

min: 0.0
mean: 0.48
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 10 tokens mean: 25.76 tokens max: 52 tokens	min: 5 tokens mean: 11.9 tokens max: 29 tokens	min: 0.0 mean: 0.48 max: 1.0

Samples:

sentence1	sentence2	score
`A man with blond-hair, and a brown shirt drinking out of a public water fountain.`	`A blond man drinking water from a fountain.`	`1.0`
`A man, woman, and child enjoying themselves on a beach.`	`A family of three is at the mall shopping.`	`0.0`
`A woman wearing all white and eating, walks next to a man holding a briefcase.`	`A married couple is sleeping.`	`0.0`

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: 25.6 tokens
max: 52 tokens

min: 8 tokens
mean: 12.3 tokens
max: 29 tokens

min: 0.0
mean: 0.6
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 10 tokens mean: 25.6 tokens max: 52 tokens	min: 8 tokens mean: 12.3 tokens max: 29 tokens	min: 0.0 mean: 0.6 max: 1.0

Samples:

sentence1	sentence2	score
`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`
`Woman in white in foreground and a man slightly behind walking with a sign for John's Pizza and Gyro in the background.`	`A woman ordering pizza.`	`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.`	`A boy flips a burger.`	`0.0`

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	2.7147	-	-
0.5	5	3.4691	-	-
1.0	10	3.0569	2.7822	-0.1864

The bold row denotes the saved checkpoint.

Training Time

Training: 3.3 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}
}