|
--- |
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base_model: ai-forever/ruRoberta-large |
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datasets: [] |
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language: [] |
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library_name: sentence-transformers |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:19383 |
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- loss:MultipleNegativesRankingLoss |
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widget: |
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- source_sentence: '12.02.2.17 Панель ингаляционных аллергенов № 9 (IgE): эпителий |
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кошки, перхоть собаки, овсяница луговая' |
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sentences: |
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- Панель аллергенов плесени № 1 IgE (penicillium notatum, cladosporium herbarum, |
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aspergillus fumigatus, candida albicans, alternaria tenuis), |
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- Панель пищевых аллергенов № 51 IgE (помидор, картофель, морковь, чеснок, горчица), |
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- Прием (осмотр, консультация) врача-психотерапевта первичный |
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- source_sentence: '12.02.2.2.04 Панель пищевых аллергенов № 2 (IgG): треска, тунец, |
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креветки, лосось, мидии' |
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sentences: |
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- Панель пищевых аллергенов № 5 IgE (яичный белок, молоко, треска, пшеничная мука, |
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арахис, соевые бобы), |
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- Панель пищевых аллергенов № 7 IgE (яичный белок, рис, коровье молоко, aрахис, |
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пшеничная мука, соевые бобы), |
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- Панель ингаляционных аллергенов № 3 IgE (клещ - дерматофаг перинный, эпителий |
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кошки, эпителий собаки, плесневый гриб (Aspergillus fumigatus)), |
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- source_sentence: 12.4.6.04 Аллерген f27 - говядина, IgE (ImmunoCAP) |
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sentences: |
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- Панель ингаляционных аллергенов № 3 IgE (клещ - дерматофаг перинный, эпителий |
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кошки, эпителий собаки, плесневый гриб (Aspergillus fumigatus)), |
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- Панель аллергенов животных/перья птиц/ № 71 IgE (перо гуся, перо курицы, перо |
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утки, перо индюка), |
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- Панель ингаляционных аллергенов № 6 IgE (плесневый гриб (Cladosporium herbarum), |
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тимофеевка, плесневый гриб (Alternaria tenuis), береза, полынь обыкновенная), |
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- source_sentence: Микробиологическое исследование биосубстатов на микрофлору (отделяемое |
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зева, носа, глаз, ушей, гениталий, ран,мокрота) с постановкой чувствительности |
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[Мартьянова] |
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sentences: |
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- Панель ингаляционных аллергенов № 9 IgE (эпителий кошки, перхоть собаки, овсяница |
|
луговая, плесневый гриб (Alternaria tenuis), подорожник), |
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- Панель аллергенов плесени № 1 IgE (penicillium notatum, cladosporium herbarum, |
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aspergillus fumigatus, candida albicans, alternaria tenuis), |
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- Посев отделяемого верхних дыхательных путей на микрофлору, определение чувствительности |
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к антимикробным препаратам (одна локализация) (Upper Respiratory Culture. Bacteria |
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Identification and Antibiotic Susceptibility Testing)* |
|
- source_sentence: НЕТ ДО 20.04!!!!!!!! 12.01.16 Аллергокомпонент f77 - бета-лактоглобулин |
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nBos d 5, IgE (ImmunoCAP) |
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sentences: |
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- Ультразвуковое исследование плода |
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- Панель аллергенов животных № 70 IgE (эпителий морской свинки, эпителий кролика, |
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хомяк, крыса, мышь), |
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- Панель пищевых аллергенов № 15 IgE (апельсин, банан, яблоко, персик), |
|
--- |
|
|
|
# SentenceTransformer based on ai-forever/ruRoberta-large |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [ai-forever/ruRoberta-large](https://huggingface.co/ai-forever/ruRoberta-large). It maps sentences & paragraphs to a 1024-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:** [ai-forever/ruRoberta-large](https://huggingface.co/ai-forever/ruRoberta-large) <!-- at revision 5192d064ca6ac67c14c40e017ce41612e010f05f --> |
|
- **Maximum Sequence Length:** 514 tokens |
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- **Output Dimensionality:** 1024 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **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( |
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(0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: RobertaModel |
|
(1): Pooling({'word_embedding_dimension': 1024, '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}) |
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) |
|
``` |
|
|
|
## 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 = [ |
|
'НЕТ ДО 20.04!!!!!!!! 12.01.16 Аллергокомпонент f77 - бета-лактоглобулин nBos d 5, IgE (ImmunoCAP)', |
|
'Панель аллергенов животных № 70 IgE (эпителий морской свинки, эпителий кролика, хомяк, крыса, мышь),', |
|
'Ультразвуковое исследование плода', |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 1024] |
|
|
|
# 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> |
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|
|
</details> |
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--> |
|
|
|
<!-- |
|
### 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.* |
|
--> |
|
|
|
<!-- |
|
## 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.* |
|
--> |
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|
|
<!-- |
|
### Recommendations |
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 19,383 training samples |
|
* Columns: <code>sentence_0</code> and <code>sentence_1</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | sentence_0 | sentence_1 | |
|
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 5 tokens</li><li>mean: 30.0 tokens</li><li>max: 121 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 30.73 tokens</li><li>max: 105 tokens</li></ul> | |
|
* Samples: |
|
| sentence_0 | sentence_1 | |
|
|:-------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------| |
|
| <code>Ингибитор VIII фактора</code> | <code>Исследование уровня антигена фактора Виллебранда</code> | |
|
| <code>13.01.02 Антитела к экстрагируемому нуклеарному АГ (ЭНА/ENA-скрин), сыворотка крови</code> | <code>Антитела к экстрагируемому ядерному антигену, кач.</code> | |
|
| <code>Нет 12.4.092 Аллерген f203 - фисташковые орехи, IgE</code> | <code>Панель аллергенов деревьев № 2 IgE (клен ясенелистный, тополь, вяз, дуб, пекан),</code> | |
|
* Loss: [<code>MultipleNegativesRankingLoss</code>](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 |
|
|
|
- `per_device_train_batch_size`: 4 |
|
- `per_device_eval_batch_size`: 4 |
|
- `num_train_epochs`: 11 |
|
- `multi_dataset_batch_sampler`: round_robin |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: no |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 4 |
|
- `per_device_eval_batch_size`: 4 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 1 |
|
- `eval_accumulation_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`: 11 |
|
- `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 |
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- `use_cpu`: False |
|
- `use_mps_device`: False |
|
- `seed`: 42 |
|
- `data_seed`: None |
|
- `jit_mode_eval`: False |
|
- `use_ipex`: False |
|
- `bf16`: False |
|
- `fp16`: False |
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- `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 |
|
- `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 |
|
- `batch_sampler`: batch_sampler |
|
- `multi_dataset_batch_sampler`: round_robin |
|
|
|
</details> |
|
|
|
### Training Logs |
|
<details><summary>Click to expand</summary> |
|
|
|
| Epoch | Step | Training Loss | |
|
|:-------:|:-----:|:-------------:| |
|
| 0.1032 | 500 | 0.7937 | |
|
| 0.2064 | 1000 | 0.5179 | |
|
| 0.3095 | 1500 | 0.5271 | |
|
| 0.4127 | 2000 | 0.5696 | |
|
| 0.5159 | 2500 | 0.5232 | |
|
| 0.6191 | 3000 | 0.6401 | |
|
| 0.7222 | 3500 | 0.6337 | |
|
| 0.8254 | 4000 | 0.9436 | |
|
| 0.9286 | 4500 | 1.3872 | |
|
| 1.0318 | 5000 | 1.3834 | |
|
| 1.1350 | 5500 | 0.9831 | |
|
| 1.2381 | 6000 | 1.0122 | |
|
| 1.3413 | 6500 | 1.3708 | |
|
| 1.4445 | 7000 | 1.3794 | |
|
| 1.5477 | 7500 | 1.3784 | |
|
| 1.6508 | 8000 | 1.3856 | |
|
| 1.7540 | 8500 | 1.3809 | |
|
| 1.8572 | 9000 | 1.3776 | |
|
| 1.9604 | 9500 | 1.0041 | |
|
| 2.0636 | 10000 | 0.8559 | |
|
| 2.1667 | 10500 | 0.8531 | |
|
| 2.2699 | 11000 | 0.8446 | |
|
| 2.3731 | 11500 | 0.8487 | |
|
| 2.4763 | 12000 | 1.0807 | |
|
| 2.5794 | 12500 | 1.3792 | |
|
| 2.6826 | 13000 | 1.3923 | |
|
| 2.7858 | 13500 | 1.3787 | |
|
| 2.8890 | 14000 | 1.3803 | |
|
| 2.9922 | 14500 | 1.3641 | |
|
| 3.0953 | 15000 | 1.3725 | |
|
| 3.1985 | 15500 | 1.3624 | |
|
| 3.3017 | 16000 | 1.3659 | |
|
| 3.4049 | 16500 | 1.3609 | |
|
| 3.5080 | 17000 | 1.3496 | |
|
| 3.6112 | 17500 | 1.3639 | |
|
| 3.7144 | 18000 | 1.3487 | |
|
| 3.8176 | 18500 | 1.3463 | |
|
| 3.9208 | 19000 | 1.336 | |
|
| 4.0239 | 19500 | 1.3451 | |
|
| 4.1271 | 20000 | 1.3363 | |
|
| 4.2303 | 20500 | 1.3411 | |
|
| 4.3335 | 21000 | 1.3376 | |
|
| 4.4366 | 21500 | 1.3294 | |
|
| 4.5398 | 22000 | 1.3281 | |
|
| 4.6430 | 22500 | 1.3323 | |
|
| 4.7462 | 23000 | 1.3411 | |
|
| 4.8494 | 23500 | 1.3162 | |
|
| 4.9525 | 24000 | 1.3204 | |
|
| 5.0557 | 24500 | 1.324 | |
|
| 5.1589 | 25000 | 1.3253 | |
|
| 5.2621 | 25500 | 1.3283 | |
|
| 5.3652 | 26000 | 1.3298 | |
|
| 5.4684 | 26500 | 1.3144 | |
|
| 5.5716 | 27000 | 1.3162 | |
|
| 5.6748 | 27500 | 1.3148 | |
|
| 5.7780 | 28000 | 1.3254 | |
|
| 5.8811 | 28500 | 1.319 | |
|
| 5.9843 | 29000 | 1.3134 | |
|
| 6.0875 | 29500 | 1.3184 | |
|
| 6.1907 | 30000 | 1.3049 | |
|
| 6.2939 | 30500 | 1.3167 | |
|
| 6.3970 | 31000 | 1.3192 | |
|
| 6.5002 | 31500 | 1.2926 | |
|
| 6.6034 | 32000 | 1.3035 | |
|
| 6.7066 | 32500 | 1.3117 | |
|
| 6.8097 | 33000 | 1.3093 | |
|
| 6.9129 | 33500 | 1.278 | |
|
| 7.0161 | 34000 | 1.3143 | |
|
| 7.1193 | 34500 | 1.3144 | |
|
| 7.2225 | 35000 | 1.304 | |
|
| 7.3256 | 35500 | 1.3066 | |
|
| 7.4288 | 36000 | 1.2916 | |
|
| 7.5320 | 36500 | 1.2943 | |
|
| 7.6352 | 37000 | 1.2883 | |
|
| 7.7383 | 37500 | 1.3014 | |
|
| 7.8415 | 38000 | 1.3005 | |
|
| 7.9447 | 38500 | 1.2699 | |
|
| 8.0479 | 39000 | 1.3042 | |
|
| 8.1511 | 39500 | 1.289 | |
|
| 8.2542 | 40000 | 1.3012 | |
|
| 8.3574 | 40500 | 1.3017 | |
|
| 8.4606 | 41000 | 1.272 | |
|
| 8.5638 | 41500 | 1.2939 | |
|
| 8.6669 | 42000 | 1.2764 | |
|
| 8.7701 | 42500 | 1.2908 | |
|
| 8.8733 | 43000 | 1.2619 | |
|
| 8.9765 | 43500 | 1.2791 | |
|
| 9.0797 | 44000 | 1.2722 | |
|
| 9.1828 | 44500 | 1.278 | |
|
| 9.2860 | 45000 | 1.2911 | |
|
| 9.3892 | 45500 | 1.2791 | |
|
| 9.4924 | 46000 | 1.2791 | |
|
| 9.5955 | 46500 | 1.2782 | |
|
| 9.6987 | 47000 | 1.2789 | |
|
| 9.8019 | 47500 | 1.2858 | |
|
| 9.9051 | 48000 | 1.2601 | |
|
| 10.0083 | 48500 | 1.29 | |
|
| 10.1114 | 49000 | 1.276 | |
|
| 10.2146 | 49500 | 1.2801 | |
|
| 10.3178 | 50000 | 1.2853 | |
|
| 10.4210 | 50500 | 1.2655 | |
|
| 10.5241 | 51000 | 1.271 | |
|
| 10.6273 | 51500 | 1.2633 | |
|
| 10.7305 | 52000 | 1.2565 | |
|
| 10.8337 | 52500 | 1.2755 | |
|
| 10.9369 | 53000 | 1.2567 | |
|
|
|
</details> |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.3.0+cu121 |
|
- Accelerate: 0.31.0 |
|
- Datasets: 2.20.0 |
|
- Tokenizers: 0.19.1 |
|
|
|
## 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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|
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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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}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
|
} |
|
``` |
|
|
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