|
--- |
|
language: [] |
|
library_name: sentence-transformers |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:10330 |
|
- loss:MultipleNegativesRankingLoss |
|
base_model: indobenchmark/indobert-base-p2 |
|
datasets: [] |
|
metrics: |
|
- pearson_cosine |
|
- spearman_cosine |
|
- pearson_manhattan |
|
- spearman_manhattan |
|
- pearson_euclidean |
|
- spearman_euclidean |
|
- pearson_dot |
|
- spearman_dot |
|
- pearson_max |
|
- spearman_max |
|
|
|
pipeline_tag: sentence-similarity |
|
model-index: |
|
- name: SentenceTransformer based on indobenchmark/indobert-base-p2 |
|
results: |
|
- task: |
|
type: semantic-similarity |
|
name: Semantic Similarity |
|
dataset: |
|
name: sts dev |
|
type: sts-dev |
|
metrics: |
|
- type: pearson_cosine |
|
value: -0.0979039836743928 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: -0.10370853946172742 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: -0.0986716229567464 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: -0.10051590980192249 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: -0.09806801008727767 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: -0.09978077307233649 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: -0.08215757856369725 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: -0.08205505573726227 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: -0.08215757856369725 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: -0.08205505573726227 |
|
name: Spearman Max |
|
- type: pearson_cosine |
|
value: -0.02784985879772803 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: -0.03497736614462515 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: -0.03551617173397621 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: -0.03865758617690966 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: -0.0355939001168591 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: -0.03886934284409788 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: -0.009209251203106355 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: -0.006641745341724743 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: -0.009209251203106355 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: -0.006641745341724743 |
|
name: Spearman Max |
|
--- |
|
|
|
# SentenceTransformer based on indobenchmark/indobert-base-p2 |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2). 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:** [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) <!-- at revision 94b4e0a82081fa57f227fcc2024d1ea89b57ac1f --> |
|
- **Maximum Sequence Length:** 200 tokens |
|
- **Output Dimensionality:** 768 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
|
<!-- - **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': 200, 'do_lower_case': False}) with Transformer model: BertModel |
|
(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("sentence_transformers_model_id") |
|
# Run inference |
|
sentences = [ |
|
'Penduduk kabupaten Raja Ampat mayoritas memeluk agama Kristen.', |
|
'Masyarakat kabupaten Raja Ampat mayoritas memeluk agama Islam.', |
|
'Gereja Baptis biasanya cenderung membentuk kelompok sendiri.', |
|
] |
|
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 |
|
* Dataset: `sts-dev` |
|
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
|
| Metric | Value | |
|
|:-------------------|:------------| |
|
| pearson_cosine | -0.0979 | |
|
| spearman_cosine | -0.1037 | |
|
| pearson_manhattan | -0.0987 | |
|
| spearman_manhattan | -0.1005 | |
|
| pearson_euclidean | -0.0981 | |
|
| spearman_euclidean | -0.0998 | |
|
| pearson_dot | -0.0822 | |
|
| spearman_dot | -0.0821 | |
|
| pearson_max | -0.0822 | |
|
| **spearman_max** | **-0.0821** | |
|
|
|
#### Semantic Similarity |
|
* Dataset: `sts-dev` |
|
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
|
| Metric | Value | |
|
|:-------------------|:------------| |
|
| pearson_cosine | -0.0278 | |
|
| spearman_cosine | -0.035 | |
|
| pearson_manhattan | -0.0355 | |
|
| spearman_manhattan | -0.0387 | |
|
| pearson_euclidean | -0.0356 | |
|
| spearman_euclidean | -0.0389 | |
|
| pearson_dot | -0.0092 | |
|
| spearman_dot | -0.0066 | |
|
| pearson_max | -0.0092 | |
|
| **spearman_max** | **-0.0066** | |
|
|
|
<!-- |
|
## 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: 10,330 training samples |
|
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | sentence_0 | sentence_1 | label | |
|
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------| |
|
| type | string | string | int | |
|
| details | <ul><li>min: 10 tokens</li><li>mean: 30.59 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.93 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>0: ~33.50%</li><li>1: ~32.70%</li><li>2: ~33.80%</li></ul> | |
|
* Samples: |
|
| sentence_0 | sentence_1 | label | |
|
|:-----------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------| |
|
| <code>Ini adalah coup de grâce dan dorongan yang dibutuhkan oleh para pendatang untuk mendapatkan kemerdekaan mereka.</code> | <code>Pendatang tidak mendapatkan kemerdekaan.</code> | <code>2</code> | |
|
| <code>Dua bayi almarhum Raja, Diana dan Suharna, diculik.</code> | <code>Jumlah bayi raja yang diculik sudah mencapai 2 bayi.</code> | <code>1</code> | |
|
| <code>Sebuah penelitian menunjukkan bahwa mengkonsumsi makanan yang tinggi kadar gulanya bisa meningkatkan rasa haus.</code> | <code>Tidak ada penelitian yang bertopik makanan yang kadar gulanya tinggi.</code> | <code>2</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 |
|
|
|
- `eval_strategy`: steps |
|
- `per_device_train_batch_size`: 4 |
|
- `per_device_eval_batch_size`: 4 |
|
- `num_train_epochs`: 20 |
|
- `multi_dataset_batch_sampler`: round_robin |
|
|
|
#### 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`: 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`: 20 |
|
- `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 |
|
- `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 | sts-dev_spearman_max | |
|
|:-------:|:-----:|:-------------:|:--------------------:| |
|
| 0.0998 | 129 | - | -0.0821 | |
|
| 0.0999 | 258 | - | -0.0541 | |
|
| 0.1936 | 500 | 0.0322 | - | |
|
| 0.1998 | 516 | - | -0.0474 | |
|
| 0.2997 | 774 | - | -0.0369 | |
|
| 0.3871 | 1000 | 0.0157 | - | |
|
| 0.3995 | 1032 | - | -0.0371 | |
|
| 0.4994 | 1290 | - | -0.0388 | |
|
| 0.5807 | 1500 | 0.0109 | - | |
|
| 0.5993 | 1548 | - | -0.0284 | |
|
| 0.6992 | 1806 | - | -0.0293 | |
|
| 0.7743 | 2000 | 0.0112 | - | |
|
| 0.7991 | 2064 | - | -0.0176 | |
|
| 0.8990 | 2322 | - | -0.0290 | |
|
| 0.9679 | 2500 | 0.0104 | - | |
|
| 0.9988 | 2580 | - | -0.0128 | |
|
| 1.0 | 2583 | - | -0.0123 | |
|
| 1.0987 | 2838 | - | -0.0200 | |
|
| 1.1614 | 3000 | 0.0091 | - | |
|
| 1.1986 | 3096 | - | -0.0202 | |
|
| 1.2985 | 3354 | - | -0.0204 | |
|
| 1.3550 | 3500 | 0.0052 | - | |
|
| 1.3984 | 3612 | - | -0.0231 | |
|
| 1.4983 | 3870 | - | -0.0312 | |
|
| 1.5486 | 4000 | 0.0017 | - | |
|
| 1.5981 | 4128 | - | -0.0277 | |
|
| 1.6980 | 4386 | - | -0.0366 | |
|
| 1.7422 | 4500 | 0.0054 | - | |
|
| 1.7979 | 4644 | - | -0.0192 | |
|
| 1.8978 | 4902 | - | -0.0224 | |
|
| 1.9357 | 5000 | 0.0048 | - | |
|
| 1.9977 | 5160 | - | -0.0240 | |
|
| 2.0 | 5166 | - | -0.0248 | |
|
| 2.0976 | 5418 | - | -0.0374 | |
|
| 2.1293 | 5500 | 0.0045 | - | |
|
| 2.1974 | 5676 | - | -0.0215 | |
|
| 2.2973 | 5934 | - | -0.0329 | |
|
| 2.3229 | 6000 | 0.0047 | - | |
|
| 2.3972 | 6192 | - | -0.0284 | |
|
| 2.4971 | 6450 | - | -0.0370 | |
|
| 2.5165 | 6500 | 0.0037 | - | |
|
| 2.5970 | 6708 | - | -0.0390 | |
|
| 2.6969 | 6966 | - | -0.0681 | |
|
| 2.7100 | 7000 | 0.0128 | - | |
|
| 2.7967 | 7224 | - | -0.0343 | |
|
| 2.8966 | 7482 | - | -0.0413 | |
|
| 2.9036 | 7500 | 0.0055 | - | |
|
| 2.9965 | 7740 | - | -0.0416 | |
|
| 3.0 | 7749 | - | -0.0373 | |
|
| 3.0964 | 7998 | - | -0.0630 | |
|
| 3.0972 | 8000 | 0.0016 | - | |
|
| 3.1963 | 8256 | - | -0.0401 | |
|
| 3.2907 | 8500 | 0.0018 | - | |
|
| 3.2962 | 8514 | - | -0.0303 | |
|
| 3.3961 | 8772 | - | -0.0484 | |
|
| 3.4843 | 9000 | 0.0017 | - | |
|
| 3.4959 | 9030 | - | -0.0619 | |
|
| 3.5958 | 9288 | - | -0.0411 | |
|
| 3.6779 | 9500 | 0.007 | - | |
|
| 3.6957 | 9546 | - | -0.0408 | |
|
| 3.7956 | 9804 | - | -0.0368 | |
|
| 3.8715 | 10000 | 0.0029 | - | |
|
| 3.8955 | 10062 | - | -0.0429 | |
|
| 3.9954 | 10320 | - | -0.0526 | |
|
| 4.0 | 10332 | - | -0.0494 | |
|
| 4.0650 | 10500 | 0.0004 | - | |
|
| 4.0952 | 10578 | - | -0.0385 | |
|
| 4.1951 | 10836 | - | -0.0467 | |
|
| 4.2586 | 11000 | 0.0004 | - | |
|
| 4.2950 | 11094 | - | -0.0500 | |
|
| 4.3949 | 11352 | - | -0.0458 | |
|
| 4.4522 | 11500 | 0.0011 | - | |
|
| 4.4948 | 11610 | - | -0.0389 | |
|
| 4.5947 | 11868 | - | -0.0401 | |
|
| 4.6458 | 12000 | 0.0046 | - | |
|
| 4.6945 | 12126 | - | -0.0370 | |
|
| 4.7944 | 12384 | - | -0.0495 | |
|
| 4.8393 | 12500 | 0.0104 | - | |
|
| 4.8943 | 12642 | - | -0.0504 | |
|
| 4.9942 | 12900 | - | -0.0377 | |
|
| 5.0 | 12915 | - | -0.0379 | |
|
| 5.0329 | 13000 | 0.0005 | - | |
|
| 5.0941 | 13158 | - | -0.0617 | |
|
| 5.1940 | 13416 | - | -0.0354 | |
|
| 5.2265 | 13500 | 0.0006 | - | |
|
| 5.2938 | 13674 | - | -0.0514 | |
|
| 5.3937 | 13932 | - | -0.0615 | |
|
| 5.4201 | 14000 | 0.0014 | - | |
|
| 5.4936 | 14190 | - | -0.0574 | |
|
| 5.5935 | 14448 | - | -0.0503 | |
|
| 5.6136 | 14500 | 0.0025 | - | |
|
| 5.6934 | 14706 | - | -0.0512 | |
|
| 5.7933 | 14964 | - | -0.0316 | |
|
| 5.8072 | 15000 | 0.0029 | - | |
|
| 5.8931 | 15222 | - | -0.0475 | |
|
| 5.9930 | 15480 | - | -0.0429 | |
|
| 6.0 | 15498 | - | -0.0377 | |
|
| 6.0008 | 15500 | 0.0003 | - | |
|
| 6.0929 | 15738 | - | -0.0486 | |
|
| 6.1928 | 15996 | - | -0.0512 | |
|
| 6.1943 | 16000 | 0.0002 | - | |
|
| 6.2927 | 16254 | - | -0.0383 | |
|
| 6.3879 | 16500 | 0.0017 | - | |
|
| 6.3926 | 16512 | - | -0.0460 | |
|
| 6.4925 | 16770 | - | -0.0439 | |
|
| 6.5815 | 17000 | 0.0046 | - | |
|
| 6.5923 | 17028 | - | -0.0378 | |
|
| 6.6922 | 17286 | - | -0.0289 | |
|
| 6.7751 | 17500 | 0.0081 | - | |
|
| 6.7921 | 17544 | - | -0.0415 | |
|
| 6.8920 | 17802 | - | -0.0451 | |
|
| 6.9686 | 18000 | 0.0021 | - | |
|
| 6.9919 | 18060 | - | -0.0386 | |
|
| 7.0 | 18081 | - | -0.0390 | |
|
| 7.0918 | 18318 | - | -0.0460 | |
|
| 7.1622 | 18500 | 0.0001 | - | |
|
| 7.1916 | 18576 | - | -0.0510 | |
|
| 7.2915 | 18834 | - | -0.0566 | |
|
| 7.3558 | 19000 | 0.0009 | - | |
|
| 7.3914 | 19092 | - | -0.0479 | |
|
| 7.4913 | 19350 | - | -0.0456 | |
|
| 7.5494 | 19500 | 0.0019 | - | |
|
| 7.5912 | 19608 | - | -0.0371 | |
|
| 7.6911 | 19866 | - | -0.0184 | |
|
| 7.7429 | 20000 | 0.003 | - | |
|
| 7.7909 | 20124 | - | -0.0312 | |
|
| 7.8908 | 20382 | - | -0.0307 | |
|
| 7.9365 | 20500 | 0.0008 | - | |
|
| 7.9907 | 20640 | - | -0.0291 | |
|
| 8.0 | 20664 | - | -0.0298 | |
|
| 8.0906 | 20898 | - | -0.0452 | |
|
| 8.1301 | 21000 | 0.0001 | - | |
|
| 8.1905 | 21156 | - | -0.0405 | |
|
| 8.2904 | 21414 | - | -0.0417 | |
|
| 8.3237 | 21500 | 0.0007 | - | |
|
| 8.3902 | 21672 | - | -0.0430 | |
|
| 8.4901 | 21930 | - | -0.0487 | |
|
| 8.5172 | 22000 | 0.0 | - | |
|
| 8.5900 | 22188 | - | -0.0471 | |
|
| 8.6899 | 22446 | - | -0.0361 | |
|
| 8.7108 | 22500 | 0.0037 | - | |
|
| 8.7898 | 22704 | - | -0.0443 | |
|
| 8.8897 | 22962 | - | -0.0404 | |
|
| 8.9044 | 23000 | 0.0009 | - | |
|
| 8.9895 | 23220 | - | -0.0421 | |
|
| 9.0 | 23247 | - | -0.0425 | |
|
| 9.0894 | 23478 | - | -0.0451 | |
|
| 9.0979 | 23500 | 0.0001 | - | |
|
| 9.1893 | 23736 | - | -0.0458 | |
|
| 9.2892 | 23994 | - | -0.0479 | |
|
| 9.2915 | 24000 | 0.0 | - | |
|
| 9.3891 | 24252 | - | -0.0400 | |
|
| 9.4851 | 24500 | 0.0014 | - | |
|
| 9.4890 | 24510 | - | -0.0374 | |
|
| 9.5889 | 24768 | - | -0.0454 | |
|
| 9.6787 | 25000 | 0.0075 | - | |
|
| 9.6887 | 25026 | - | -0.0230 | |
|
| 9.7886 | 25284 | - | -0.0345 | |
|
| 9.8722 | 25500 | 0.0007 | - | |
|
| 9.8885 | 25542 | - | -0.0301 | |
|
| 9.9884 | 25800 | - | -0.0363 | |
|
| 10.0 | 25830 | - | -0.0375 | |
|
| 10.0658 | 26000 | 0.0001 | - | |
|
| 10.0883 | 26058 | - | -0.0381 | |
|
| 10.1882 | 26316 | - | -0.0386 | |
|
| 10.2594 | 26500 | 0.0 | - | |
|
| 10.2880 | 26574 | - | -0.0390 | |
|
| 10.3879 | 26832 | - | -0.0366 | |
|
| 10.4530 | 27000 | 0.0007 | - | |
|
| 10.4878 | 27090 | - | -0.0464 | |
|
| 10.5877 | 27348 | - | -0.0509 | |
|
| 10.6465 | 27500 | 0.0021 | - | |
|
| 10.6876 | 27606 | - | -0.0292 | |
|
| 10.7875 | 27864 | - | -0.0514 | |
|
| 10.8401 | 28000 | 0.0017 | - | |
|
| 10.8873 | 28122 | - | -0.0485 | |
|
| 10.9872 | 28380 | - | -0.0471 | |
|
| 11.0 | 28413 | - | -0.0468 | |
|
| 11.0337 | 28500 | 0.0 | - | |
|
| 11.0871 | 28638 | - | -0.0460 | |
|
| 11.1870 | 28896 | - | -0.0450 | |
|
| 11.2273 | 29000 | 0.0 | - | |
|
| 11.2869 | 29154 | - | -0.0457 | |
|
| 11.3868 | 29412 | - | -0.0450 | |
|
| 11.4208 | 29500 | 0.0008 | - | |
|
| 11.4866 | 29670 | - | -0.0440 | |
|
| 11.5865 | 29928 | - | -0.0384 | |
|
| 11.6144 | 30000 | 0.0028 | - | |
|
| 11.6864 | 30186 | - | -0.0066 | |
|
|
|
</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: 2.19.2 |
|
- 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", |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
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}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
|
<!-- |
|
## 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.* |
|
--> |
