---
base_model: distilbert/distilbert-base-multilingual-cased
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:867042
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Even children can understand it.
sentences:
- मी सगळीकडे छापले. मी लिहिले आणि सर्व काही मोजले. आणि नऊ महिन्यामध्ये मुले कोणत्याही
भाषेतला संगणकासोबत मोकळे सोडल्यावर पश्चिम देशातील कार्यालातील सेक्रेटरीएवढ्या
पातळीवर येऊ शकतो
- Anslået bliver 5000 kvinder om året dræbt som følge af domestisk vold, mens tusindvis
overlever med varige mén.
- इस बात को बच्चे भी समझते हैं।
- source_sentence: What do you want to buy?
sentences:
- 这是我们为福特汽车公司做的项目，
- İçtenlikle umuyorum ki yakında hastalığından iyileşeceksin.
- എന്താ വാങ്ങിക്കേണ്ടത്? തോക്കാണോ?
- source_sentence: Oh, come on, Charles.
sentences:
- Este es un ejemplo que preparé para mi hija.
- Noaptea a fost atât de rece încât, atunci când m-am întors, eram aproape îngheţat.
- No tak, Charlesi!
- source_sentence: In 1830 English mathematician Charles Babbage published a book
entitled Reflections on the Decline of Science in England to summarize what he
observed to be the existing state of scientific affairs.
sentences:
- В 1830 г. английский математик Чарлз Бэббедж опубликовал книгу Reflections on
the Decline of Science in England, в которой он описал свои наблюдения состояния
научного мира.
- A szakosodás ezeken a területek új és érdekes munkahelyekhez vezethet.
- Grimes seoses Darwini „Liikide tekkimisega ”:„ Mitte ükski teine raamat, mis kunagi
on trükitud, pole tekitanud mõtlevate inimeste keskel taolist poleemikat.
- source_sentence: Palm DOC Conduit for KPilot
sentences:
- PalmDOC- conduit foar KPilot
- Sabíem, doncs, que si volíem veure actuar aquesta peça de metall segons la mecànica
quàntica, hauríem de fer fora tots els altres passatgers.
- Man nepatinka gyventi kaime.
---
# SentenceTransformer based on distilbert/distilbert-base-multilingual-cased
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased). 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:** [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
### 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': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(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("agentlans/distilbert-base-multilingual-cased-aligned")
# Run inference
sentences = [
'Palm DOC Conduit for KPilot',
'PalmDOC- conduit foar KPilot',
'Man nepatinka gyventi kaime.',
]
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]
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 867,042 training samples
* Columns: sentence_0 and sentence_1
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 3 tokens
- mean: 21.88 tokens
- max: 121 tokens
| - min: 3 tokens
- mean: 30.11 tokens
- max: 230 tokens
|
* Samples:
| sentence_0 | sentence_1 |
|:-----------------------------------------------------------------------|:------------------------------------------------------------------------|
| They need to be internationally recognized and supported. | Mereka harus diakui dan dibantu secara internasional. |
| I ride with these kids once a week, every Tuesday. | Ik rijd met deze kinderen een keer per week, elke dinsdag. |
| We still have some. | අපි ගාව තව ඒවා තියෙනවනේ. |
* Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `num_train_epochs`: 1
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `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
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: False
- `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
- `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`: round_robin
Click to expand
| Epoch | Step | Training Loss |
|:------:|:------:|:-------------:|
| 0.0046 | 500 | 0.1996 |
| 0.0092 | 1000 | 0.087 |
| 0.0138 | 1500 | 0.0771 |
| 0.0185 | 2000 | 0.0646 |
| 0.0231 | 2500 | 0.0443 |
| 0.0277 | 3000 | 0.0526 |
| 0.0323 | 3500 | 0.05 |
| 0.0369 | 4000 | 0.0479 |
| 0.0415 | 4500 | 0.0477 |
| 0.0461 | 5000 | 0.0427 |
| 0.0507 | 5500 | 0.0343 |
| 0.0554 | 6000 | 0.0358 |
| 0.0600 | 6500 | 0.0452 |
| 0.0646 | 7000 | 0.0397 |
| 0.0692 | 7500 | 0.0289 |
| 0.0738 | 8000 | 0.0274 |
| 0.0784 | 8500 | 0.0364 |
| 0.0830 | 9000 | 0.0283 |
| 0.0877 | 9500 | 0.0295 |
| 0.0923 | 10000 | 0.0337 |
| 0.0969 | 10500 | 0.0303 |
| 0.1015 | 11000 | 0.0252 |
| 0.1061 | 11500 | 0.0241 |
| 0.1107 | 12000 | 0.0225 |
| 0.1153 | 12500 | 0.0263 |
| 0.1199 | 13000 | 0.0255 |
| 0.1246 | 13500 | 0.0311 |
| 0.1292 | 14000 | 0.0201 |
| 0.1338 | 14500 | 0.0209 |
| 0.1384 | 15000 | 0.0205 |
| 0.1430 | 15500 | 0.0242 |
| 0.1476 | 16000 | 0.0332 |
| 0.1522 | 16500 | 0.0346 |
| 0.1569 | 17000 | 0.0225 |
| 0.1615 | 17500 | 0.0245 |
| 0.1661 | 18000 | 0.0166 |
| 0.1707 | 18500 | 0.0196 |
| 0.1753 | 19000 | 0.0264 |
| 0.1799 | 19500 | 0.0212 |
| 0.1845 | 20000 | 0.0201 |
| 0.1891 | 20500 | 0.0238 |
| 0.1938 | 21000 | 0.0175 |
| 0.1984 | 21500 | 0.022 |
| 0.2030 | 22000 | 0.0201 |
| 0.2076 | 22500 | 0.0197 |
| 0.2122 | 23000 | 0.0137 |
| 0.2168 | 23500 | 0.017 |
| 0.2214 | 24000 | 0.031 |
| 0.2261 | 24500 | 0.0238 |
| 0.2307 | 25000 | 0.0194 |
| 0.2353 | 25500 | 0.024 |
| 0.2399 | 26000 | 0.022 |
| 0.2445 | 26500 | 0.0276 |
| 0.2491 | 27000 | 0.016 |
| 0.2537 | 27500 | 0.0203 |
| 0.2583 | 28000 | 0.0245 |
| 0.2630 | 28500 | 0.0161 |
| 0.2676 | 29000 | 0.0132 |
| 0.2722 | 29500 | 0.0142 |
| 0.2768 | 30000 | 0.0171 |
| 0.2814 | 30500 | 0.0207 |
| 0.2860 | 31000 | 0.0189 |
| 0.2906 | 31500 | 0.0169 |
| 0.2953 | 32000 | 0.0225 |
| 0.2999 | 32500 | 0.0224 |
| 0.3045 | 33000 | 0.0114 |
| 0.3091 | 33500 | 0.0213 |
| 0.3137 | 34000 | 0.0146 |
| 0.3183 | 34500 | 0.0154 |
| 0.3229 | 35000 | 0.0218 |
| 0.3275 | 35500 | 0.0096 |
| 0.3322 | 36000 | 0.0147 |
| 0.3368 | 36500 | 0.0186 |
| 0.3414 | 37000 | 0.0214 |
| 0.3460 | 37500 | 0.0231 |
| 0.3506 | 38000 | 0.0165 |
| 0.3552 | 38500 | 0.0157 |
| 0.3598 | 39000 | 0.0128 |
| 0.3645 | 39500 | 0.018 |
| 0.3691 | 40000 | 0.0183 |
| 0.3737 | 40500 | 0.0203 |
| 0.3783 | 41000 | 0.02 |
| 0.3829 | 41500 | 0.0165 |
| 0.3875 | 42000 | 0.0128 |
| 0.3921 | 42500 | 0.0106 |
| 0.3967 | 43000 | 0.0174 |
| 0.4014 | 43500 | 0.0168 |
| 0.4060 | 44000 | 0.0114 |
| 0.4106 | 44500 | 0.0158 |
| 0.4152 | 45000 | 0.0108 |
| 0.4198 | 45500 | 0.0141 |
| 0.4244 | 46000 | 0.0137 |
| 0.4290 | 46500 | 0.0137 |
| 0.4337 | 47000 | 0.0215 |
| 0.4383 | 47500 | 0.0123 |
| 0.4429 | 48000 | 0.0138 |
| 0.4475 | 48500 | 0.0152 |
| 0.4521 | 49000 | 0.0144 |
| 0.4567 | 49500 | 0.016 |
| 0.4613 | 50000 | 0.0132 |
| 0.4659 | 50500 | 0.0164 |
| 0.4706 | 51000 | 0.0155 |
| 0.4752 | 51500 | 0.0145 |
| 0.4798 | 52000 | 0.0173 |
| 0.4844 | 52500 | 0.02 |
| 0.4890 | 53000 | 0.0168 |
| 0.4936 | 53500 | 0.011 |
| 0.4982 | 54000 | 0.0116 |
| 0.5029 | 54500 | 0.009 |
| 0.5075 | 55000 | 0.0143 |
| 0.5121 | 55500 | 0.0111 |
| 0.5167 | 56000 | 0.0138 |
| 0.5213 | 56500 | 0.0104 |
| 0.5259 | 57000 | 0.0146 |
| 0.5305 | 57500 | 0.0116 |
| 0.5351 | 58000 | 0.0157 |
| 0.5398 | 58500 | 0.013 |
| 0.5444 | 59000 | 0.0144 |
| 0.5490 | 59500 | 0.0134 |
| 0.5536 | 60000 | 0.0114 |
| 0.5582 | 60500 | 0.0101 |
| 0.5628 | 61000 | 0.0164 |
| 0.5674 | 61500 | 0.0151 |
| 0.5721 | 62000 | 0.0138 |
| 0.5767 | 62500 | 0.0107 |
| 0.5813 | 63000 | 0.0102 |
| 0.5859 | 63500 | 0.0153 |
| 0.5905 | 64000 | 0.0103 |
| 0.5951 | 64500 | 0.0136 |
| 0.5997 | 65000 | 0.0107 |
| 0.6043 | 65500 | 0.0101 |
| 0.6090 | 66000 | 0.0101 |
| 0.6136 | 66500 | 0.0117 |
| 0.6182 | 67000 | 0.0113 |
| 0.6228 | 67500 | 0.0131 |
| 0.6274 | 68000 | 0.0068 |
| 0.6320 | 68500 | 0.0053 |
| 0.6366 | 69000 | 0.0113 |
| 0.6413 | 69500 | 0.0119 |
| 0.6459 | 70000 | 0.0094 |
| 0.6505 | 70500 | 0.0072 |
| 0.6551 | 71000 | 0.0171 |
| 0.6597 | 71500 | 0.0121 |
| 0.6643 | 72000 | 0.0134 |
| 0.6689 | 72500 | 0.0147 |
| 0.6735 | 73000 | 0.0075 |
| 0.6782 | 73500 | 0.0125 |
| 0.6828 | 74000 | 0.0064 |
| 0.6874 | 74500 | 0.0071 |
| 0.6920 | 75000 | 0.0073 |
| 0.6966 | 75500 | 0.0075 |
| 0.7012 | 76000 | 0.0097 |
| 0.7058 | 76500 | 0.01 |
| 0.7105 | 77000 | 0.0123 |
| 0.7151 | 77500 | 0.0093 |
| 0.7197 | 78000 | 0.0103 |
| 0.7243 | 78500 | 0.0179 |
| 0.7289 | 79000 | 0.0091 |
| 0.7335 | 79500 | 0.0121 |
| 0.7381 | 80000 | 0.0104 |
| 0.7428 | 80500 | 0.0083 |
| 0.7474 | 81000 | 0.0116 |
| 0.7520 | 81500 | 0.0084 |
| 0.7566 | 82000 | 0.0077 |
| 0.7612 | 82500 | 0.0081 |
| 0.7658 | 83000 | 0.0101 |
| 0.7704 | 83500 | 0.0093 |
| 0.7750 | 84000 | 0.0095 |
| 0.7797 | 84500 | 0.0107 |
| 0.7843 | 85000 | 0.0108 |
| 0.7889 | 85500 | 0.0095 |
| 0.7935 | 86000 | 0.0082 |
| 0.7981 | 86500 | 0.0103 |
| 0.8027 | 87000 | 0.0069 |
| 0.8073 | 87500 | 0.009 |
| 0.8120 | 88000 | 0.0081 |
| 0.8166 | 88500 | 0.0074 |
| 0.8212 | 89000 | 0.0069 |
| 0.8258 | 89500 | 0.0066 |
| 0.8304 | 90000 | 0.0065 |
| 0.8350 | 90500 | 0.0065 |
| 0.8396 | 91000 | 0.0088 |
| 0.8442 | 91500 | 0.008 |
| 0.8489 | 92000 | 0.0069 |
| 0.8535 | 92500 | 0.0095 |
| 0.8581 | 93000 | 0.0082 |
| 0.8627 | 93500 | 0.0068 |
| 0.8673 | 94000 | 0.006 |
| 0.8719 | 94500 | 0.0082 |
| 0.8765 | 95000 | 0.0121 |
| 0.8812 | 95500 | 0.0098 |
| 0.8858 | 96000 | 0.0083 |
| 0.8904 | 96500 | 0.008 |
| 0.8950 | 97000 | 0.0053 |
| 0.8996 | 97500 | 0.0102 |
| 0.9042 | 98000 | 0.0093 |
| 0.9088 | 98500 | 0.0042 |
| 0.9134 | 99000 | 0.0093 |
| 0.9181 | 99500 | 0.0138 |
| 0.9227 | 100000 | 0.0105 |
| 0.9273 | 100500 | 0.0079 |
| 0.9319 | 101000 | 0.0118 |
| 0.9365 | 101500 | 0.0072 |
| 0.9411 | 102000 | 0.0094 |
| 0.9457 | 102500 | 0.0108 |
| 0.9504 | 103000 | 0.0092 |
| 0.9550 | 103500 | 0.0062 |
| 0.9596 | 104000 | 0.0073 |
| 0.9642 | 104500 | 0.0089 |
| 0.9688 | 105000 | 0.0092 |
| 0.9734 | 105500 | 0.0076 |
| 0.9780 | 106000 | 0.0103 |
| 0.9826 | 106500 | 0.0064 |
| 0.9873 | 107000 | 0.0072 |
| 0.9919 | 107500 | 0.0052 |
| 0.9965 | 108000 | 0.0061 |