| | --- |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - generated_from_trainer |
| | - dataset_size:79621 |
| | - loss:CosineSimilarityLoss |
| | base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
| | widget: |
| | - source_sentence: Data demografi Indonesia 2021 perempuan dan lakilaki |
| | sentences: |
| | - Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Komoditi HS, Februari |
| | 2015 |
| | - Statistik Potensi Desa Provinsi Jawa Barat 2014 |
| | - Pengeluaran untuk Konsumsi Penduduk Indonesia, September 2017 |
| | - source_sentence: Data analisis tematik kependudukan Indonesia migrasi dan ketenagakerjaan |
| | sentences: |
| | - Direktori Perusahaan Industri Penggilingan Padi Tahun 2012 Provinsi Bengkulu |
| | - Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut HS, Juni 2023 |
| | - Luas Panen dan Produksi Padi 2022 |
| | - source_sentence: Daftar perusahaan penggilingan padi Kalimantan |
| | sentences: |
| | - Ringkasan Neraca Arus Dana, Triwulan II, 2011*), (Miliar Rupiah) |
| | - Klasifikasi Baku Komoditas Indonesia 2012 Buku 1 |
| | - Statistik Penduduk Lanjut Usia Provinsi Nusa Tenggara Barat 2010-Hasil Sensus |
| | Penduduk 2010 |
| | - source_sentence: Perdagangan luar negeri impor Januari 2010 |
| | sentences: |
| | - Buletin Statistik Perdagangan Luar Negeri Impor Januari 2010 |
| | - Statistik Tanaman Sayuran dan Buah-buahan Semusim Indonesia 2012 |
| | - Klasifikasi Baku Komoditas Indonesia (KBKI) 2012 Buku 4 |
| | - source_sentence: Biaya hidup kelompok perumahan Indonesia 2017 |
| | sentences: |
| | - Indeks Harga Perdagangan Besar 2007 |
| | - Statistik Upah 2013 |
| | - Survei Biaya Hidup (SBH) 2018 Bulukumba, Watampone, Makassar, Pare-Pare, dan Palopo |
| | datasets: |
| | - yahyaabd/allstats-search-pairs-dataset |
| | pipeline_tag: sentence-similarity |
| | library_name: sentence-transformers |
| | metrics: |
| | - pearson_cosine |
| | - spearman_cosine |
| | model-index: |
| | - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
| | results: |
| | - task: |
| | type: semantic-similarity |
| | name: Semantic Similarity |
| | dataset: |
| | name: allstats semantic mpnet eval |
| | type: allstats-semantic-mpnet-eval |
| | metrics: |
| | - type: pearson_cosine |
| | value: 0.9830659002996571 |
| | name: Pearson Cosine |
| | - type: spearman_cosine |
| | value: 0.8410118780087822 |
| | name: Spearman Cosine |
| | - task: |
| | type: semantic-similarity |
| | name: Semantic Similarity |
| | dataset: |
| | name: allstats semantic mpnet test |
| | type: allstats-semantic-mpnet-test |
| | metrics: |
| | - type: pearson_cosine |
| | value: 0.9831682336847792 |
| | name: Pearson Cosine |
| | - type: spearman_cosine |
| | value: 0.8378561244643341 |
| | name: Spearman Cosine |
| | --- |
| | |
| | # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
| |
|
| | This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on the [allstats-search-pairs-dataset](https://huggingface.co/datasets/yahyaabd/allstats-search-pairs-dataset) dataset. It maps sentences & paragraphs to a 768-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 75c57757a97f90ad739aca51fa8bfea0e485a7f2 --> |
| | - **Maximum Sequence Length:** 128 tokens |
| | - **Output Dimensionality:** 768 dimensions |
| | - **Similarity Function:** Cosine Similarity |
| | - **Training Dataset:** |
| | - [allstats-search-pairs-dataset](https://huggingface.co/datasets/yahyaabd/allstats-search-pairs-dataset) |
| | <!-- - **Language:** Unknown --> |
| | <!-- - **License:** Unknown --> |
| |
|
| | ### Model Sources |
| |
|
| | - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| | - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
| | - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
| |
|
| | ### Full Model Architecture |
| |
|
| | ``` |
| | SentenceTransformer( |
| | (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
| | (1): Pooling({'word_embedding_dimension': 768, '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: |
| |
|
| | ```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("yahyaabd/test-model") |
| | # Run inference |
| | sentences = [ |
| | 'Biaya hidup kelompok perumahan Indonesia 2017', |
| | 'Statistik Upah 2013', |
| | 'Survei Biaya Hidup (SBH) 2018 Bulukumba, Watampone, Makassar, Pare-Pare, dan Palopo', |
| | ] |
| | embeddings = model.encode(sentences) |
| | print(embeddings.shape) |
| | # [3, 768] |
| | |
| | # Get the similarity scores for the embeddings |
| | similarities = model.similarity(embeddings, embeddings) |
| | print(similarities.shape) |
| | # [3, 3] |
| | ``` |
| |
|
| | <!-- |
| | ### 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 |
| |
|
| | * Datasets: `allstats-semantic-mpnet-eval` and `allstats-semantic-mpnet-test` |
| | * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
| |
|
| | | Metric | allstats-semantic-mpnet-eval | allstats-semantic-mpnet-test | |
| | |:--------------------|:-----------------------------|:-----------------------------| |
| | | pearson_cosine | 0.9831 | 0.9832 | |
| | | **spearman_cosine** | **0.841** | **0.8379** | |
| | |
| | <!-- |
| | ## 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 |
| | |
| | #### allstats-search-pairs-dataset |
| | |
| | * Dataset: [allstats-search-pairs-dataset](https://huggingface.co/datasets/yahyaabd/allstats-search-pairs-dataset) at [6712cb1](https://huggingface.co/datasets/yahyaabd/allstats-search-pairs-dataset/tree/6712cb14bbd89da6f87890ac082b09e0adb7a02e) |
| | * Size: 79,621 training samples |
| | * Columns: <code>query</code>, <code>doc</code>, and <code>label</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | query | doc | label | |
| | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------| |
| | | type | string | string | float | |
| | | details | <ul><li>min: 5 tokens</li><li>mean: 10.78 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.73 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.44</li><li>max: 0.99</li></ul> | |
| | * Samples: |
| | | query | doc | label | |
| | |:--------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------|:------------------| |
| | | <code>Produksi jagung di Indonesia tahun 2009</code> | <code>Indeks Unit Value Ekspor Menurut Kode SITC Bulan Februari 2024</code> | <code>0.1</code> | |
| | | <code>Data produksi industri manufaktur 2021</code> | <code>Perkembangan Indeks Produksi Industri Manufaktur 2021</code> | <code>0.96</code> | |
| | | <code>direktori perusahaan industri penggilingan padi tahun 2012 provinsi sulawesi utara dan gorontalo</code> | <code>Neraca Pemerintahan Umum Indonesia 2007-2012</code> | <code>0.03</code> | |
| | * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
| | ```json |
| | { |
| | "loss_fct": "torch.nn.modules.loss.MSELoss" |
| | } |
| | ``` |
| | |
| | ### Evaluation Dataset |
| | |
| | #### allstats-search-pairs-dataset |
| | |
| | * Dataset: [allstats-search-pairs-dataset](https://huggingface.co/datasets/yahyaabd/allstats-search-pairs-dataset) at [6712cb1](https://huggingface.co/datasets/yahyaabd/allstats-search-pairs-dataset/tree/6712cb14bbd89da6f87890ac082b09e0adb7a02e) |
| | * Size: 9,952 evaluation samples |
| | * Columns: <code>query</code>, <code>doc</code>, and <code>label</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | query | doc | label | |
| | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------| |
| | | type | string | string | float | |
| | | details | <ul><li>min: 5 tokens</li><li>mean: 10.75 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.09 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 0.01</li><li>mean: 0.48</li><li>max: 0.99</li></ul> | |
| | * Samples: |
| | | query | doc | label | |
| | |:--------------------------------------------------------------------|:-----------------------------------------------------------------|:------------------| |
| | | <code>Daftar perusahaan industri pengolahan skala kecil 2006</code> | <code>Statistik Migrasi Nusa Tenggara Barat Hasil SP 2010</code> | <code>0.05</code> | |
| | | <code>Populasi Indonesia per provinsi 2000-2010</code> | <code>Indikator Ekonomi Desember 2023</code> | <code>0.08</code> | |
| | | <code>Data harga barang desa non-pangan tahun 2022</code> | <code>Statistik Kunjungan Tamu Asing 2004</code> | <code>0.1</code> | |
| | * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
| | ```json |
| | { |
| | "loss_fct": "torch.nn.modules.loss.MSELoss" |
| | } |
| | ``` |
| | |
| | ### Training Hyperparameters |
| | #### Non-Default Hyperparameters |
| |
|
| | - `eval_strategy`: steps |
| | - `per_device_train_batch_size`: 32 |
| | - `per_device_eval_batch_size`: 32 |
| | - `num_train_epochs`: 6 |
| | - `warmup_ratio`: 0.1 |
| | - `fp16`: True |
| | - `dataloader_num_workers`: 4 |
| | - `load_best_model_at_end`: True |
| | - `label_smoothing_factor`: 0.01 |
| | - `eval_on_start`: True |
| |
|
| | #### All Hyperparameters |
| | <details><summary>Click to expand</summary> |
| |
|
| | - `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`: 6 |
| | - `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`: 4 |
| | - `dataloader_prefetch_factor`: None |
| | - `past_index`: -1 |
| | - `disable_tqdm`: False |
| | - `remove_unused_columns`: True |
| | - `label_names`: None |
| | - `load_best_model_at_end`: True |
| | - `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`: 0.01 |
| | - `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`: True |
| | - `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 |
| |
|
| | </details> |
| |
|
| | ### Training Logs |
| | | Epoch | Step | Training Loss | Validation Loss | allstats-semantic-mpnet-eval_spearman_cosine | allstats-semantic-mpnet-test_spearman_cosine | |
| | |:----------:|:---------:|:-------------:|:---------------:|:--------------------------------------------:|:--------------------------------------------:| |
| | | 0 | 0 | - | 0.0958 | 0.6404 | - | |
| | | 0.1004 | 250 | 0.0482 | 0.0269 | 0.7685 | - | |
| | | 0.2009 | 500 | 0.0249 | 0.0200 | 0.7737 | - | |
| | | 0.3013 | 750 | 0.0196 | 0.0172 | 0.7768 | - | |
| | | 0.4018 | 1000 | 0.0184 | 0.0172 | 0.7744 | - | |
| | | 0.5022 | 1250 | 0.0185 | 0.0159 | 0.7751 | - | |
| | | 0.6027 | 1500 | 0.0155 | 0.0156 | 0.7825 | - | |
| | | 0.7031 | 1750 | 0.0165 | 0.0149 | 0.7826 | - | |
| | | 0.8035 | 2000 | 0.0146 | 0.0136 | 0.7791 | - | |
| | | 0.9040 | 2250 | 0.0132 | 0.0127 | 0.7825 | - | |
| | | 1.0044 | 2500 | 0.0127 | 0.0129 | 0.7818 | - | |
| | | 1.1049 | 2750 | 0.0104 | 0.0114 | 0.7849 | - | |
| | | 1.2053 | 3000 | 0.009 | 0.0108 | 0.7870 | - | |
| | | 1.3057 | 3250 | 0.0091 | 0.0112 | 0.7877 | - | |
| | | 1.4062 | 3500 | 0.009 | 0.0104 | 0.7888 | - | |
| | | 1.5066 | 3750 | 0.009 | 0.0101 | 0.7937 | - | |
| | | 1.6071 | 4000 | 0.0084 | 0.0099 | 0.7924 | - | |
| | | 1.7075 | 4250 | 0.008 | 0.0097 | 0.7942 | - | |
| | | 1.8080 | 4500 | 0.0079 | 0.0094 | 0.7946 | - | |
| | | 1.9084 | 4750 | 0.0078 | 0.0092 | 0.7928 | - | |
| | | 2.0088 | 5000 | 0.0081 | 0.0088 | 0.7986 | - | |
| | | 2.1093 | 5250 | 0.0056 | 0.0079 | 0.8025 | - | |
| | | 2.2097 | 5500 | 0.0052 | 0.0080 | 0.8019 | - | |
| | | 2.3102 | 5750 | 0.0048 | 0.0079 | 0.8073 | - | |
| | | 2.4106 | 6000 | 0.0053 | 0.0081 | 0.8058 | - | |
| | | 2.5110 | 6250 | 0.0049 | 0.0079 | 0.8091 | - | |
| | | 2.6115 | 6500 | 0.0053 | 0.0077 | 0.8081 | - | |
| | | 2.7119 | 6750 | 0.0052 | 0.0075 | 0.8075 | - | |
| | | 2.8124 | 7000 | 0.0049 | 0.0077 | 0.8089 | - | |
| | | 2.9128 | 7250 | 0.0051 | 0.0076 | 0.8066 | - | |
| | | 3.0133 | 7500 | 0.0048 | 0.0074 | 0.8127 | - | |
| | | 3.1137 | 7750 | 0.0034 | 0.0069 | 0.8162 | - | |
| | | 3.2141 | 8000 | 0.0033 | 0.0070 | 0.8164 | - | |
| | | 3.3146 | 8250 | 0.0036 | 0.0068 | 0.8194 | - | |
| | | 3.4150 | 8500 | 0.0032 | 0.0069 | 0.8156 | - | |
| | | 3.5155 | 8750 | 0.0032 | 0.0068 | 0.8196 | - | |
| | | 3.6159 | 9000 | 0.0032 | 0.0067 | 0.8197 | - | |
| | | 3.7164 | 9250 | 0.0034 | 0.0068 | 0.8194 | - | |
| | | 3.8168 | 9500 | 0.0034 | 0.0066 | 0.8194 | - | |
| | | 3.9172 | 9750 | 0.0032 | 0.0063 | 0.8239 | - | |
| | | 4.0177 | 10000 | 0.0032 | 0.0065 | 0.8229 | - | |
| | | 4.1181 | 10250 | 0.0023 | 0.0063 | 0.8258 | - | |
| | | 4.2186 | 10500 | 0.0022 | 0.0062 | 0.8293 | - | |
| | | 4.3190 | 10750 | 0.002 | 0.0063 | 0.8283 | - | |
| | | 4.4194 | 11000 | 0.0023 | 0.0060 | 0.8306 | - | |
| | | 4.5199 | 11250 | 0.0021 | 0.0061 | 0.8318 | - | |
| | | 4.6203 | 11500 | 0.0021 | 0.0060 | 0.8290 | - | |
| | | 4.7208 | 11750 | 0.0022 | 0.0058 | 0.8329 | - | |
| | | 4.8212 | 12000 | 0.0021 | 0.0058 | 0.8342 | - | |
| | | 4.9217 | 12250 | 0.0021 | 0.0058 | 0.8360 | - | |
| | | 5.0221 | 12500 | 0.0017 | 0.0057 | 0.8355 | - | |
| | | 5.1225 | 12750 | 0.0014 | 0.0057 | 0.8377 | - | |
| | | 5.2230 | 13000 | 0.0015 | 0.0058 | 0.8363 | - | |
| | | 5.3234 | 13250 | 0.0015 | 0.0058 | 0.8369 | - | |
| | | 5.4239 | 13500 | 0.0014 | 0.0057 | 0.8386 | - | |
| | | 5.5243 | 13750 | 0.0014 | 0.0057 | 0.8387 | - | |
| | | 5.6247 | 14000 | 0.0015 | 0.0057 | 0.8392 | - | |
| | | **5.7252** | **14250** | **0.0014** | **0.0056** | **0.8409** | **-** | |
| | | 5.8256 | 14500 | 0.0014 | 0.0056 | 0.8410 | - | |
| | | 5.9261 | 14750 | 0.0014 | 0.0056 | 0.8410 | - | |
| | | 6.0 | 14934 | - | - | - | 0.8379 | |
| |
|
| | * The bold row denotes the saved checkpoint. |
| |
|
| | ### Framework Versions |
| | - Python: 3.10.12 |
| | - Sentence Transformers: 3.3.1 |
| | - Transformers: 4.48.0 |
| | - PyTorch: 2.5.1+cu121 |
| | - Accelerate: 1.2.1 |
| | - Datasets: 3.2.0 |
| | - Tokenizers: 0.21.0 |
| |
|
| | ## 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", |
| | } |
| | ``` |
| |
|
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