metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:25052
  - loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
  - source_sentence: HAZIR TEMELLI KAFES kule 30m 130x3.5
    sentences:
      - Electrical cable and accessories
      - Building construction machinery and accessories
      - Mounting Hardware
  - source_sentence: GRUP ICI TELESALES BIREYSEL SATIS GIDERLERI
    sentences:
      - Chassis components
      - Computers
      - Telecommunication Services
  - source_sentence: 02A5C02_SERI_NUMARALI_VARLIK_ICIN_MEMORY_UPGRADE
    sentences:
      - Chassis components
      - Building construction machinery and accessories
      - Computers
  - source_sentence: anten 885 to 975 mhz
    sentences:
      - Chassis components
      - Media storage devices
      - Circuit assemblies and radio frequency RF components
  - source_sentence: LG 35'' 35WN75C QHD UltraWide Curved Monitor
    sentences:
      - Computer displays
      - Computers
      - Personal communication devices
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: >-
      SentenceTransformer based on
      sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: pearson_cosine
            value: .nan
            name: Pearson Cosine
          - type: spearman_cosine
            value: .nan
            name: Spearman Cosine

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-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/paraphrase-multilingual-MiniLM-L12-v2
Maximum Sequence Length: 128 tokens
Output Dimensionality: 384 dimensions
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})
)

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("alpcansoydas/product-model-07.01.25-total45class-ifhavemorethan100sampleperclass-0.73acc")
# Run inference
sentences = [
    "LG 35'' 35WN75C QHD UltraWide Curved Monitor",
    'Computer displays',
    'Computers',
]
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

Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	nan
spearman_cosine	nan

Training Details

Training Dataset

Unnamed Dataset

Size: 25,052 training samples
Columns: sentence1 and sentence2
Approximate statistics based on the first 1000 samples:
sentence1 sentence2
type string string
details
min: 3 tokens
mean: 16.38 tokens
max: 128 tokens

min: 4 tokens
mean: 7.51 tokens
max: 15 tokens

	sentence1	sentence2
type	string	string
details	min: 3 tokens mean: 16.38 tokens max: 128 tokens	min: 4 tokens mean: 7.51 tokens max: 15 tokens

Samples:

sentence1	sentence2
`Lenovo ThinkPad 13 Business Ultrabook`	`Computers`
`Turuncu Renk Elektronik İsaretleyici`	`Electronic component parts and raw materials and accessories`
`GALATA BAKIM HIZMETI`	`Business function specific software`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

Size: 3,132 evaluation samples
Columns: sentence1 and sentence2
Approximate statistics based on the first 1000 samples:
sentence1 sentence2
type string string
details
min: 3 tokens
mean: 16.88 tokens
max: 88 tokens

min: 4 tokens
mean: 7.61 tokens
max: 15 tokens

	sentence1	sentence2
type	string	string
details	min: 3 tokens mean: 16.88 tokens max: 88 tokens	min: 4 tokens mean: 7.61 tokens max: 15 tokens

Samples:

sentence1	sentence2
`yapay çam ağacı dalı- 1m boyunda`	`Building construction machinery and accessories`
`Apple MacBookAir 13 inch M3 MXCR3TU/A (2024)`	`Computers`
`40x200x40x1.00 mm Kapaklı kablo kanalı`	`Electrical cable and accessories`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
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: 32
per_device_eval_batch_size: 32
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: 1.0
num_train_epochs: 3
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: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	spearman_cosine
0.1277	100	2.7301	2.2798	nan
0.2554	200	2.2058	2.0536	nan
0.3831	300	1.9996	1.9523	nan
0.5109	400	1.9243	1.8681	nan
0.6386	500	1.8063	1.8316	nan
0.7663	600	1.806	1.7893	nan
0.8940	700	1.7557	1.7632	nan
1.0217	800	1.7531	1.7564	nan
1.1494	900	1.6381	1.7492	nan
1.2771	1000	1.6087	1.7495	nan
1.4049	1100	1.5616	1.7211	nan
1.5326	1200	1.6219	1.7010	nan
1.6603	1300	1.5469	1.7011	nan
1.7880	1400	1.5912	1.6926	nan
1.9157	1500	1.5579	1.6825	nan
2.0434	1600	1.5455	1.6762	nan
2.1711	1700	1.4239	1.6916	nan
2.2989	1800	1.4543	1.6862	nan
2.4266	1900	1.4292	1.6812	nan
2.5543	2000	1.4573	1.6741	nan
2.6820	2100	1.4321	1.6683	nan
2.8097	2200	1.425	1.6683	nan
2.9374	2300	1.4247	1.6648	nan

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.3.1
Transformers: 4.48.0.dev0
PyTorch: 2.5.1+cu121
Accelerate: 1.2.1
Datasets: 3.2.0
Tokenizers: 0.21.0

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",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}