Modelpublic

skripsiskripsi/myskripsisbert

sourceHugging Faceupdated 3mo agoView on Hugging Face

0likes71downloads

Model Card

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 retrieval.

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
—Supported Modality: Text

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': 'BertModel'})
  (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

bash

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 = [
    'spesifikasi industri untuk jaringan kawasan pribadi (personal area networks atau pan) tanpa kabel. bluetooth menghubungkan dan dapat dipakai untuk melakukan tukar-menukar informasi di antara peralatan-peralatan. ',
    'bluetooth adalah perangkat yang menjadi media tukar menukar (menerima mengirim) informasi di antara peralatan eketronik. bluetooth merupakan media tanpa kabel. biasanya bluethooth digunakan untuk mengirim foto atau file antar handphone.',
    'lcd (liquid crystal display), cpu (central processing unit), gps (global positioning system)',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.4212, 0.1034],
#         [0.4212, 1.0000, 0.1189],
#         [0.1034, 0.1189, 1.0000]])

<!--

Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details> -->

<!--

Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details> -->

<!--

Out-of-Scope Use

List how the model may foreseeably be misused and address what users ought not to do with the model. -->

Evaluation

Metrics

Semantic Similarity

—Dataset: val
—Evaluated with <code>EmbeddingSimilarityEvaluator</code>

Metric	Value
pearson_cosine	0.9196
spearman_cosine	0.9021

<!--

Bias, Risks and Limitations

What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. -->

<!--

Recommendations

What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. -->

Training Details

Training Dataset

Unnamed Dataset

—Size: 358 training samples
—Columns: <code>sentence0</code>, <code>sentence1</code>, and <code>label</code>
—Approximate statistics based on the first 100 samples: | | sentence0 | sentence1 | label | |:---------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | modality | text | text | | | details | <ul><li>min: 5 tokens</li><li>mean: 29.68 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 26 tokens</li><li>mean: 42.82 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 0.01</li><li>mean: 0.4</li><li>max: 1.0</li></ul> |
—Samples: | sentence0 | sentence1 | label | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------| | <code>alat yang dipakai untuk mengolah data menurut prosedur yang telah dirumuskan dimana komputer itu sendiri merupakan perangkat elektronik yang terdiri dari beberapa komponen yang saling bekerja sama membentuk sebuah sistem kerja yang dapat menjalankan pekerjaan secara otomatis berdasar urutan instruksi ataupun program yang diberikan kepadanya sehingga dapat menghasilkan suatu informasi berdasarkan program dan data yang ada. </code> | <code>komputer adalah rangkaian mesin elektronik yang dapat bekerja sama. sistem ini digunakan untuk memudahkan pekerjaan manusia. komputer bekerja otomatis berdasarkan urutan instruksi atau program yang diberikan.</code> | <code>0.29333333333333333</code> | | <code>informasi yang dibutuhkan akan semakin cepat dan mudah di akses untuk kepentingan pendidikan. inovasi dalam pembelajaran semakin berkembang dengan adanya inovasi e-learning yang semakin memudahkan proses pendidikan. kemajuan tik juga akan memungkinkan berkembangnya kelas virtual atau kelas yang berbasis teleconference yang tidak mengharuskan sang pendidik dan peserta didik berada dalam satu ruangan. </code> | <code>- memperluas pemasaran sehingga tidak mempermasalahkan jarak dan waktu - mengurangi biaya produksi, promosi - mempermudah penyimpanan data penjualan, data barang maupun laporan keuangan - menggantikan pekerjaan manual menjadi otomatis - proses produksi lebih cepat dan praktis</code> | <code>0.25666666666666665</code> | | <code>bisnis berjalan cepat , dapat maju dengan peasat , banyak informasi yang di dapat </code> | <code>- memperluas pemasaran sehingga tidak mempermasalahkan jarak dan waktu - mengurangi biaya produksi, promosi - mempermudah penyimpanan data penjualan, data barang maupun laporan keuangan - menggantikan pekerjaan manual menjadi otomatis - proses produksi lebih cepat dan praktis</code> | <code>0.23</code> |
—Loss: <code>CosineSimilarityLoss</code> with these parameters:

json

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss",
      "cos_score_transformation": "torch.nn.modules.linear.Identity"
  }

Training Hyperparameters

Non-Default Hyperparameters

—per_device_train_batch_size: 16
—num_train_epochs: 4
—per_device_eval_batch_size: 16
—multi_dataset_batch_sampler: round_robin

All Hyperparameters

<details><summary>Click to expand</summary>

—per_device_train_batch_size: 16
—num_train_epochs: 4
—max_steps: -1
—learning_rate: 5e-05
—lr_scheduler_type: linear
—lr_scheduler_kwargs: None
—warmup_steps: 0
—optim: adamwtorchfused
—optim_args: None
—weight_decay: 0.0
—adam_beta1: 0.9
—adam_beta2: 0.999
—adam_epsilon: 1e-08
—optim_target_modules: None
—gradient_accumulation_steps: 1
—average_tokens_across_devices: True
—max_grad_norm: 1
—label_smoothing_factor: 0.0
—bf16: False
—fp16: False
—bf16_full_eval: False
—fp16_full_eval: False
—tf32: None
—gradient_checkpointing: False
—gradient_checkpointing_kwargs: None
—torch_compile: False
—torch_compile_backend: None
—torch_compile_mode: None
—use_liger_kernel: False
—liger_kernel_config: None
—use_cache: False
—neftune_noise_alpha: None
—torch_empty_cache_steps: None
—auto_find_batch_size: False
—log_on_each_node: True
—logging_nan_inf_filter: True
—include_num_input_tokens_seen: no
—log_level: passive
—log_level_replica: warning
—disable_tqdm: False
—project: huggingface
—trackio_space_id: None
—trackio_bucket_id: None
—trackio_static_space_id: None
—per_device_eval_batch_size: 16
—prediction_loss_only: True
—eval_on_start: False
—eval_do_concat_batches: True
—eval_use_gather_object: False
—eval_accumulation_steps: None
—include_for_metrics: []
—batch_eval_metrics: False
—save_only_model: False
—save_on_each_node: False
—enable_jit_checkpoint: False
—push_to_hub: False
—hub_private_repo: None
—hub_model_id: None
—hub_strategy: every_save
—hub_always_push: False
—hub_revision: None
—load_best_model_at_end: False
—ignore_data_skip: False
—restore_callback_states_from_checkpoint: False
—full_determinism: False
—seed: 42
—data_seed: None
—use_cpu: False
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
—parallelism_config: None
—dataloader_drop_last: False
—dataloader_num_workers: 0
—dataloader_pin_memory: True
—dataloader_persistent_workers: False
—dataloader_prefetch_factor: None
—remove_unused_columns: True
—label_names: None
—train_sampling_strategy: random
—length_column_name: length
—ddp_find_unused_parameters: None
—ddp_bucket_cap_mb: None
—ddp_broadcast_buffers: False
—ddp_static_graph: None
—ddp_backend: None
—ddp_timeout: 1800
—fsdp: None
—fsdp_config: None
—deepspeed: None
—debug: []
—skip_memory_metrics: True
—do_predict: False
—resume_from_checkpoint: None
—warmup_ratio: None
—local_rank: -1
—prompts: None
—batch_sampler: batch_sampler
—multi_dataset_batch_sampler: round_robin
—router_mapping: {}
—learning_rate_mapping: {}

</details>

Training Logs

Epoch	Step	val_spearman_cosine
1.0	23	0.8524
2.0	46	0.8764
3.0	69	0.9021

Training Time

—Training: 3.9 minutes

Framework Versions

—Python: 3.12.13
—Sentence Transformers: 5.5.1
—Transformers: 5.12.0
—PyTorch: 2.11.0+cu128
—Accelerate: 1.14.0
—Datasets: 4.0.0
—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",
}

<!--

Glossary

Clearly define terms in order to be accessible across audiences. -->

<!--

Model Card Authors

Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. -->

<!--

Model Card Contact

Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->