|
--- |
|
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", |
|
} |
|
``` |
|
|
|
<!-- |
|
## 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.* |
|
--> |
