This is a sentence-transformers model finetuned from youssefkhalil320/all-MiniLM-L6-v14-pair_score. 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: youssefkhalil320/all-MiniLM-L6-v14-pair_score
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 tokens
Similarity Function: Cosine Similarity

Language: en
License: apache-2.0

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': 256, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'medical accessory',
    'bedroom chair',
    'handball',
]
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]

Training Details

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
learning_rate: 2e-05
num_train_epochs: 2
warmup_ratio: 0.1
fp16: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
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: 2e-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: 2
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
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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss
0.0244	100	15.2955
0.0488	200	12.0245
0.0732	300	9.5958
0.0977	400	9.0251
0.1221	500	8.8519
0.1465	600	8.7625
0.1709	700	8.7085
0.1953	800	8.6742
0.2197	900	8.6344
0.2441	1000	8.5984
0.2686	1100	8.5607
0.2930	1200	8.5395
0.3174	1300	8.4973
0.3418	1400	8.4809
0.3662	1500	8.4588
0.3906	1600	8.4489
0.4150	1700	8.4255
0.4395	1800	8.4189
0.4639	1900	8.4012
0.4883	2000	8.3793
0.5127	2100	8.3654
0.5371	2200	8.3369
0.5615	2300	8.3483
0.5859	2400	8.3441
0.6104	2500	8.3357
0.6348	2600	8.3009
0.6592	2700	8.2997
0.6836	2800	8.2901
0.7080	2900	8.2797
0.7324	3000	8.2837
0.7568	3100	8.2856
0.7812	3200	8.2753
0.8057	3300	8.275
0.8301	3400	8.2577
0.8545	3500	8.2296
0.8789	3600	8.2368
0.9033	3700	8.244
0.9277	3800	8.2259
0.9521	3900	8.2262
0.9766	4000	8.252
1.0010	4100	8.1966
1.0254	4200	8.1923
1.0498	4300	8.1938
1.0742	4400	8.1771
1.0986	4500	8.1956
1.1230	4600	8.1826
1.1475	4700	8.1726
1.1719	4800	8.1826
1.1963	4900	8.1699
1.2207	5000	8.1576
1.2451	5100	8.1849
1.2695	5200	8.1753
1.2939	5300	8.1661
1.3184	5400	8.153
1.3428	5500	8.158
1.3672	5600	8.1577
1.3916	5700	8.152
1.4160	5800	8.1716
1.4404	5900	8.1528
1.4648	6000	8.1561
1.4893	6100	8.1658
1.5137	6200	8.1379
1.5381	6300	8.1437
1.5625	6400	8.1329
1.5869	6500	8.1251
1.6113	6600	8.1359
1.6357	6700	8.1334
1.6602	6800	8.1187
1.6846	6900	8.1461
1.7090	7000	8.1294
1.7334	7100	8.0935
1.7578	7200	8.1494
1.7822	7300	8.105
1.8066	7400	8.0987
1.8311	7500	8.1524
1.8555	7600	8.1228
1.8799	7700	8.1228
1.9043	7800	8.1315
1.9287	7900	8.1236
1.9531	8000	8.1499
1.9775	8100	8.1092

Framework Versions

Python: 3.8.10
Sentence Transformers: 3.1.1
Transformers: 4.45.2
PyTorch: 2.4.1+cu118
Accelerate: 1.0.1
Datasets: 3.0.1
Tokenizers: 0.20.3

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