metadata

base_model: sentence-transformers/all-MiniLM-L12-v2
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:100
  - loss:CosineSimilarityLoss
widget:
  - source_sentence: Children smiling and waving at camera
    sentences:
      - There are women showing affection.
      - The woman is waiting for a friend.
      - There are children present
  - source_sentence: >-
      A woman is walking across the street eating a banana, while a man is
      following with his briefcase.
    sentences:
      - The boy does a skateboarding trick.
      - A boy flips a burger.
      - >-
        A woman eats a banana and walks across a street, and there is a man
        trailing behind her.
  - source_sentence: >-
      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.
    sentences:
      - An elderly man sits in a small shop.
      - A person is training his horse for a competition.
      - Two adults swimming in water
  - 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:
      - The woman is wearing green.
      - A school is hosting an event.
      - The adults are both male and female.
  - source_sentence: >-
      A woman is walking across the street eating a banana, while a man is
      following with his briefcase.
    sentences:
      - The boy is wearing safety equipment.
      - Two women are at a restaurant drinking wine.
      - A person that is hungry.
model-index:
  - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: snli dev
          type: snli-dev
        metrics:
          - type: pearson_cosine
            value: -0.6052519474756299
            name: Pearson Cosine
          - type: spearman_cosine
            value: -0.6083622621490653
            name: Spearman Cosine
          - type: pearson_manhattan
            value: -0.5848188618976576
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: -0.6065714846764287
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: -0.5863856474033792
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: -0.6083622185008256
            name: Spearman Euclidean
          - type: pearson_dot
            value: -0.6052519468947102
            name: Pearson Dot
          - type: spearman_dot
            value: -0.6083623057915619
            name: Spearman Dot
          - type: pearson_max
            value: -0.5848188618976576
            name: Pearson Max
          - type: spearman_max
            value: -0.6065714846764287
            name: Spearman Max

SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L12-v2. It maps sentences & paragraphs to a 384-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: sentence-transformers/all-MiniLM-L12-v2
Maximum Sequence Length: 128 tokens
Output Dimensionality: 384 tokens
Similarity Function: Cosine Similarity

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: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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("cherifkhalifah/finetuned-snli-MiniLM-L12-v2")
# Run inference
sentences = [
    'A woman is walking across the street eating a banana, while a man is following with his briefcase.',
    'A person that is hungry.',
    'Two women are at a restaurant drinking wine.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Semantic Similarity

Dataset: snli-dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	-0.6053
spearman_cosine	-0.6084
pearson_manhattan	-0.5848
spearman_manhattan	-0.6066
pearson_euclidean	-0.5864
spearman_euclidean	-0.6084
pearson_dot	-0.6053
spearman_dot	-0.6084
pearson_max	-0.5848
spearman_max	-0.6066

Training Details

Training Dataset

Unnamed Dataset

Size: 100 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 100 samples:
sentence_0 sentence_1 label
type string string float
details
min: 8 tokens
mean: 23.59 tokens
max: 46 tokens

min: 5 tokens
mean: 11.36 tokens
max: 25 tokens

min: 0.0
mean: 0.49
max: 1.0

	sentence_0	sentence_1	label
type	string	string	float
details	min: 8 tokens mean: 23.59 tokens max: 46 tokens	min: 5 tokens mean: 11.36 tokens max: 25 tokens	min: 0.0 mean: 0.49 max: 1.0

Samples:

sentence_0	sentence_1	label
`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 working for John's Pizza.`	`0.5`
`A man with blond-hair, and a brown shirt drinking out of a public water fountain.`	`A blond man getting a drink of water from a fountain in the park.`	`0.5`
`A woman is walking across the street eating a banana, while a man is following with his briefcase.`	`A person eating.`	`0.0`

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 4
fp16: True
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
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
num_train_epochs: 4
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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
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
eval_use_gather_object: False
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	snli-dev_spearman_max
1.0	7	-0.6099
2.0	14	-0.6095
3.0	21	-0.6085
4.0	28	-0.6066

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.1.1
Transformers: 4.44.2
PyTorch: 2.4.1+cu121
Accelerate: 0.34.2
Datasets: 3.0.1
Tokenizers: 0.19.1

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",
}