Update README.md

README.md

@@ -1,10 +1,637 @@
-pipeline_tag:sentence-similarity
+---
+language: []
+library_name: sentence-transformers
 tags:
-  - sentence-transformers
-  - feature-extraction
-  - sentence-similarity
-  - transformers
-datasets:
-  - indonli
-language:
-  - id
+- 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.*
+-->
+
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+
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+
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