Update README.md

README.md

@@ -1,137 +1,405 @@
----
-tags:
-- sentence-transformers
-- cross-encoder
-- text-classification
-pipeline_tag: text-classification
-library_name: sentence-transformers
----
-
-# CrossEncoder
-
-This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model trained using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
-
-## Model Details
-
-### Model Description
-- **Model Type:** Cross Encoder
-<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
-- **Maximum Sequence Length:** 8192 tokens
-- **Number of Output Labels:** 1 label
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
-
-### Model Sources
-
-- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
-- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
-- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
-- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
-
-## 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 CrossEncoder
-
-# Download from the 🤗 Hub
-model = CrossEncoder("tomaarsen/reranker-ModernBERT-base-gooaq-bce-static-retriever-hardest")
-# Get scores for pairs of texts
-pairs = [
-    ['How many calories in an egg', 'There are on average between 55 and 80 calories in an egg depending on its size.'],
-    ['How many calories in an egg', 'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.'],
-    ['How many calories in an egg', 'Most of the calories in an egg come from the yellow yolk in the center.'],
-]
-scores = model.predict(pairs)
-print(scores.shape)
-# (3,)
-
-# Or rank different texts based on similarity to a single text
-ranks = model.rank(
-    'How many calories in an egg',
-    [
-        'There are on average between 55 and 80 calories in an egg depending on its size.',
-        'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.',
-        'Most of the calories in an egg come from the yellow yolk in the center.',
-    ]
-)
-# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
-```
-
-<!--
-### 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.*
--->
-
-<!--
-## 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
-
-### Framework Versions
-- Python: 3.11.6
-- Sentence Transformers: 3.5.0.dev0
-- Transformers: 4.48.3
-- PyTorch: 2.5.0+cu121
-- Accelerate: 1.3.0
-- Datasets: 2.20.0
-- Tokenizers: 0.21.0
-
-## Citation
-
-### BibTeX
-
-<!--
-## 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.*
+---
+tags:
+- sentence-transformers
+- cross-encoder
+- text-classification
+- generated_from_trainer
+- dataset_size:578402
+- loss:BinaryCrossEntropyLoss
+base_model: answerdotai/ModernBERT-base
+pipeline_tag: text-classification
+library_name: sentence-transformers
+metrics:
+- map
+- mrr@10
+- ndcg@10
+model-index:
+- name: CrossEncoder based on answerdotai/ModernBERT-base
+  results: []
+---
+
+# CrossEncoder based on answerdotai/ModernBERT-base
+
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Cross Encoder
+- **Base model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 8949b909ec900327062f0ebf497f51aef5e6f0c8 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Number of Output Labels:** 1 label
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
+
+## 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 CrossEncoder
+
+# Download from the 🤗 Hub
+model = CrossEncoder("sentence_transformers_model_id")
+# Get scores for pairs of texts
+pairs = [
+    ["how to obtain a teacher's certificate in texas?", '["Step 1: Obtain a Bachelor\'s Degree. One of the most important Texas teacher qualifications is a bachelor\'s degree. ... ", \'Step 2: Complete an Educator Preparation Program (EPP) ... \', \'Step 3: Pass Texas Teacher Certification Exams. ... \', \'Step 4: Complete a Final Application and Background Check.\']'],
+    ["how to obtain a teacher's certificate in texas?", 'Teacher education programs may take 4 years to complete after which certification plans are prepared for a three year period. During this plan period, the teacher must obtain a Standard Certification within 1-2 years. Learn how to get certified to teach in Texas.'],
+    ["how to obtain a teacher's certificate in texas?", "Washington Teachers Licensing Application Process Official transcripts showing proof of bachelor's degree. Proof of teacher program completion at an approved teacher preparation school. Passing scores on the required examinations. Completed application for teacher certification in Washington."],
+    ["how to obtain a teacher's certificate in texas?", 'Some aspiring educators may be confused about the difference between teaching certification and teaching certificates. Teacher certification is another term for the licensure required to teach in public schools, while a teaching certificate is awarded upon completion of an academic program.'],
+    ["how to obtain a teacher's certificate in texas?", 'In Texas, the minimum age to work is 14. Unlike some states, Texas does not require juvenile workers to obtain a child employment certificate or an age certificate to work. A prospective employer that wants one can request a certificate of age for any minors it employs, obtainable from the Texas Workforce Commission.'],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# (5,)
+
+# Or rank different texts based on similarity to a single text
+ranks = model.rank(
+    "how to obtain a teacher's certificate in texas?",
+    [
+        '["Step 1: Obtain a Bachelor\'s Degree. One of the most important Texas teacher qualifications is a bachelor\'s degree. ... ", \'Step 2: Complete an Educator Preparation Program (EPP) ... \', \'Step 3: Pass Texas Teacher Certification Exams. ... \', \'Step 4: Complete a Final Application and Background Check.\']',
+        'Teacher education programs may take 4 years to complete after which certification plans are prepared for a three year period. During this plan period, the teacher must obtain a Standard Certification within 1-2 years. Learn how to get certified to teach in Texas.',
+        "Washington Teachers Licensing Application Process Official transcripts showing proof of bachelor's degree. Proof of teacher program completion at an approved teacher preparation school. Passing scores on the required examinations. Completed application for teacher certification in Washington.",
+        'Some aspiring educators may be confused about the difference between teaching certification and teaching certificates. Teacher certification is another term for the licensure required to teach in public schools, while a teaching certificate is awarded upon completion of an academic program.',
+        'In Texas, the minimum age to work is 14. Unlike some states, Texas does not require juvenile workers to obtain a child employment certificate or an age certificate to work. A prospective employer that wants one can request a certificate of age for any minors it employs, obtainable from the Texas Workforce Commission.',
+    ]
+)
+# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
+```
+
+<!--
+### 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
+
+#### Cross Encoder Reranking
+
+* Datasets: `gooaq-dev`, `NanoMSMARCO`, `NanoNFCorpus` and `NanoNQ`
+* Evaluated with [<code>CERerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CERerankingEvaluator)
+
+| Metric      | gooaq-dev            | NanoMSMARCO          | NanoNFCorpus         | NanoNQ               |
+|:------------|:---------------------|:---------------------|:---------------------|:---------------------|
+| map         | 0.7821 (+0.2485)     | 0.4373 (-0.0523)     | 0.3354 (+0.0650)     | 0.5305 (+0.1098)     |
+| mrr@10      | 0.7800 (+0.2560)     | 0.4288 (-0.0487)     | 0.4934 (-0.0064)     | 0.5326 (+0.1059)     |
+| **ndcg@10** | **0.8269 (+0.2356)** | **0.5287 (-0.0117)** | **0.3612 (+0.0361)** | **0.5823 (+0.0817)** |
+
+#### Cross Encoder Nano BEIR
+
+* Dataset: `NanoBEIR_mean`
+* Evaluated with [<code>CENanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CENanoBEIREvaluator)
+
+| Metric      | Value                |
+|:------------|:---------------------|
+| map         | 0.4344 (+0.0408)     |
+| mrr@10      | 0.4849 (+0.0169)     |
+| **ndcg@10** | **0.4907 (+0.0354)** |
+
+<!--
+## 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: 578,402 training samples
+* Columns: <code>question</code>, <code>answer</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | question                                                                                       | answer                                                                                           | label                                           |
+  |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                                         | string                                                                                           | int                                             |
+  | details | <ul><li>min: 19 characters</li><li>mean: 43.6 characters</li><li>max: 100 characters</li></ul> | <ul><li>min: 56 characters</li><li>mean: 251.22 characters</li><li>max: 387 characters</li></ul> | <ul><li>0: ~82.90%</li><li>1: ~17.10%</li></ul> |
+* Samples:
+  | question                                                     | answer                                                                                                                                                                                                                                                                                                                        | label          |
+  |:-------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>how to obtain a teacher's certificate in texas?</code> | <code>["Step 1: Obtain a Bachelor's Degree. One of the most important Texas teacher qualifications is a bachelor's degree. ... ", 'Step 2: Complete an Educator Preparation Program (EPP) ... ', 'Step 3: Pass Texas Teacher Certification Exams. ... ', 'Step 4: Complete a Final Application and Background Check.']</code> | <code>1</code> |
+  | <code>how to obtain a teacher's certificate in texas?</code> | <code>Teacher education programs may take 4 years to complete after which certification plans are prepared for a three year period. During this plan period, the teacher must obtain a Standard Certification within 1-2 years. Learn how to get certified to teach in Texas.</code>                                          | <code>0</code> |
+  | <code>how to obtain a teacher's certificate in texas?</code> | <code>Washington Teachers Licensing Application Process Official transcripts showing proof of bachelor's degree. Proof of teacher program completion at an approved teacher preparation school. Passing scores on the required examinations. Completed application for teacher certification in Washington.</code>            | <code>0</code> |
+* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
+  ```json
+  {
+      "activation_fct": "torch.nn.modules.linear.Identity",
+      "pos_weight": 5
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `seed`: 12
+- `bf16`: True
+- `dataloader_num_workers`: 4
+- `load_best_model_at_end`: 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`: 64
+- `per_device_eval_batch_size`: 64
+- `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`: 2e-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`: 1
+- `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`: 12
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: True
+- `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`: 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`: 0.0
+- `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`: 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`: 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`: proportional
+
+</details>
+
+### Training Logs
+| Epoch      | Step     | Training Loss | gooaq-dev_ndcg@10    | NanoMSMARCO_ndcg@10  | NanoNFCorpus_ndcg@10 | NanoNQ_ndcg@10       | NanoBEIR_mean_ndcg@10 |
+|:----------:|:--------:|:-------------:|:--------------------:|:--------------------:|:--------------------:|:--------------------:|:---------------------:|
+| -1         | -1       | -             | 0.1541 (-0.4371)     | 0.0273 (-0.5131)     | 0.3068 (-0.0182)     | 0.0340 (-0.4666)     | 0.1227 (-0.3326)      |
+| 0.0001     | 1        | 1.3693        | -                    | -                    | -                    | -                    | -                     |
+| 0.0221     | 200      | 1.1942        | -                    | -                    | -                    | -                    | -                     |
+| 0.0443     | 400      | 1.1542        | -                    | -                    | -                    | -                    | -                     |
+| 0.0664     | 600      | 0.9421        | -                    | -                    | -                    | -                    | -                     |
+| 0.0885     | 800      | 0.7253        | -                    | -                    | -                    | -                    | -                     |
+| 0.1106     | 1000     | 0.6955        | 0.7578 (+0.1666)     | 0.4930 (-0.0474)     | 0.3038 (-0.0212)     | 0.6047 (+0.1040)     | 0.4672 (+0.0118)      |
+| 0.1328     | 1200     | 0.6236        | -                    | -                    | -                    | -                    | -                     |
+| 0.1549     | 1400     | 0.6155        | -                    | -                    | -                    | -                    | -                     |
+| 0.1770     | 1600     | 0.6102        | -                    | -                    | -                    | -                    | -                     |
+| 0.1992     | 1800     | 0.5621        | -                    | -                    | -                    | -                    | -                     |
+| 0.2213     | 2000     | 0.571         | 0.7910 (+0.1998)     | 0.5230 (-0.0174)     | 0.3468 (+0.0217)     | 0.5689 (+0.0683)     | 0.4796 (+0.0242)      |
+| 0.2434     | 2200     | 0.5575        | -                    | -                    | -                    | -                    | -                     |
+| 0.2655     | 2400     | 0.5539        | -                    | -                    | -                    | -                    | -                     |
+| 0.2877     | 2600     | 0.5507        | -                    | -                    | -                    | -                    | -                     |
+| 0.3098     | 2800     | 0.5483        | -                    | -                    | -                    | -                    | -                     |
+| 0.3319     | 3000     | 0.5204        | 0.8089 (+0.2177)     | 0.5283 (-0.0121)     | 0.3413 (+0.0162)     | 0.5783 (+0.0776)     | 0.4826 (+0.0272)      |
+| 0.3541     | 3200     | 0.5267        | -                    | -                    | -                    | -                    | -                     |
+| 0.3762     | 3400     | 0.5075        | -                    | -                    | -                    | -                    | -                     |
+| 0.3983     | 3600     | 0.5312        | -                    | -                    | -                    | -                    | -                     |
+| 0.4204     | 3800     | 0.4992        | -                    | -                    | -                    | -                    | -                     |
+| 0.4426     | 4000     | 0.5019        | 0.8119 (+0.2207)     | 0.5021 (-0.0383)     | 0.3405 (+0.0155)     | 0.5255 (+0.0249)     | 0.4561 (+0.0007)      |
+| 0.4647     | 4200     | 0.4957        | -                    | -                    | -                    | -                    | -                     |
+| 0.4868     | 4400     | 0.5112        | -                    | -                    | -                    | -                    | -                     |
+| 0.5090     | 4600     | 0.4992        | -                    | -                    | -                    | -                    | -                     |
+| 0.5311     | 4800     | 0.4767        | -                    | -                    | -                    | -                    | -                     |
+| 0.5532     | 5000     | 0.4854        | 0.8197 (+0.2284)     | 0.5562 (+0.0158)     | 0.3506 (+0.0256)     | 0.5767 (+0.0761)     | 0.4945 (+0.0392)      |
+| 0.5753     | 5200     | 0.4834        | -                    | -                    | -                    | -                    | -                     |
+| 0.5975     | 5400     | 0.4732        | -                    | -                    | -                    | -                    | -                     |
+| 0.6196     | 5600     | 0.4757        | -                    | -                    | -                    | -                    | -                     |
+| 0.6417     | 5800     | 0.4704        | -                    | -                    | -                    | -                    | -                     |
+| 0.6639     | 6000     | 0.4632        | 0.8187 (+0.2275)     | 0.5322 (-0.0082)     | 0.3650 (+0.0399)     | 0.5871 (+0.0865)     | 0.4948 (+0.0394)      |
+| 0.6860     | 6200     | 0.4492        | -                    | -                    | -                    | -                    | -                     |
+| 0.7081     | 6400     | 0.4717        | -                    | -                    | -                    | -                    | -                     |
+| 0.7303     | 6600     | 0.4639        | -                    | -                    | -                    | -                    | -                     |
+| 0.7524     | 6800     | 0.465         | -                    | -                    | -                    | -                    | -                     |
+| 0.7745     | 7000     | 0.4502        | 0.8261 (+0.2349)     | 0.5455 (+0.0050)     | 0.3540 (+0.0290)     | 0.6095 (+0.1089)     | 0.5030 (+0.0476)      |
+| 0.7966     | 7200     | 0.4582        | -                    | -                    | -                    | -                    | -                     |
+| 0.8188     | 7400     | 0.4628        | -                    | -                    | -                    | -                    | -                     |
+| 0.8409     | 7600     | 0.4496        | -                    | -                    | -                    | -                    | -                     |
+| 0.8630     | 7800     | 0.4571        | -                    | -                    | -                    | -                    | -                     |
+| 0.8852     | 8000     | 0.4459        | 0.8239 (+0.2326)     | 0.5236 (-0.0168)     | 0.3571 (+0.0320)     | 0.5826 (+0.0819)     | 0.4878 (+0.0324)      |
+| 0.9073     | 8200     | 0.457         | -                    | -                    | -                    | -                    | -                     |
+| 0.9294     | 8400     | 0.4481        | -                    | -                    | -                    | -                    | -                     |
+| 0.9515     | 8600     | 0.4515        | -                    | -                    | -                    | -                    | -                     |
+| 0.9737     | 8800     | 0.4453        | -                    | -                    | -                    | -                    | -                     |
+| **0.9958** | **9000** | **0.4566**    | **0.8269 (+0.2356)** | **0.5287 (-0.0117)** | **0.3612 (+0.0361)** | **0.5823 (+0.0817)** | **0.4907 (+0.0354)**  |
+| -1         | -1       | -             | 0.8269 (+0.2356)     | 0.5287 (-0.0117)     | 0.3612 (+0.0361)     | 0.5823 (+0.0817)     | 0.4907 (+0.0354)      |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.11.10
+- Sentence Transformers: 3.5.0.dev0
+- Transformers: 4.49.0.dev0
+- PyTorch: 2.6.0.dev20241112+cu121
+- Accelerate: 1.2.0
+- 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",
+}
+```
+
+<!--
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+
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