Add auto-generated README

README.md

@@ -1,200 +1,508 @@
 ---
-library_name: transformers
+language:
+- en
 tags:
+- sentence-transformers
 - cross-encoder
+- text-classification
+- generated_from_trainer
+- dataset_size:404290
+- loss:BinaryCrossEntropyLoss
+base_model: distilbert/distilroberta-base
+datasets:
+- sentence-transformers/quora-duplicates
+pipeline_tag: text-classification
+library_name: sentence-transformers
+metrics:
+- accuracy
+- accuracy_threshold
+- f1
+- f1_threshold
+- precision
+- recall
+- average_precision
+co2_eq_emissions:
+  emissions: 26.889480385249758
+  energy_consumed: 0.06917762292257246
+  source: codecarbon
+  training_type: fine-tuning
+  on_cloud: false
+  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
+  ram_total_size: 31.777088165283203
+  hours_used: 0.214
+  hardware_used: 1 x NVIDIA GeForce RTX 3090
+model-index:
+- name: CrossEncoder based on distilbert/distilroberta-base
+  results:
+  - task:
+      type: cross-encoder-classification
+      name: Cross Encoder Classification
+    dataset:
+      name: quora duplicates dev
+      type: quora-duplicates-dev
+    metrics:
+    - type: accuracy
+      value: 0.8938
+      name: Accuracy
+    - type: accuracy_threshold
+      value: 0.5088549852371216
+      name: Accuracy Threshold
+    - type: f1
+      value: 0.8612281373675477
+      name: F1
+    - type: f1_threshold
+      value: 0.3856155276298523
+      name: F1 Threshold
+    - type: precision
+      value: 0.8182920912178554
+      name: Precision
+    - type: recall
+      value: 0.908919428725411
+      name: Recall
+    - type: average_precision
+      value: 0.920292628179356
+      name: Average Precision
+  - task:
+      type: cross-encoder-classification
+      name: Cross Encoder Classification
+    dataset:
+      name: quora duplicates test
+      type: quora-duplicates-test
+    metrics:
+    - type: accuracy
+      value: 0.8938
+      name: Accuracy
+    - type: accuracy_threshold
+      value: 0.5091445446014404
+      name: Accuracy Threshold
+    - type: f1
+      value: 0.8612281373675477
+      name: F1
+    - type: f1_threshold
+      value: 0.38580775260925293
+      name: F1 Threshold
+    - type: precision
+      value: 0.8182920912178554
+      name: Precision
+    - type: recall
+      value: 0.908919428725411
+      name: Recall
+    - type: average_precision
+      value: 0.92029239602284
+      name: Average Precision
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# CrossEncoder based on distilbert/distilroberta-base
 
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) dataset 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
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
+- **Model Type:** Cross Encoder
+- **Base model:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b -->
+- **Maximum Sequence Length:** 514 tokens
+- **Training Dataset:**
+    - [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates)
+- **Language:** en
+<!-- - **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...
+pairs = [
+    ['What is the step by step guide to invest in share market in india?', 'What is the step by step guide to invest in share market?'],
+    ['What is the story of Kohinoor (Koh-i-Noor) Diamond?', 'What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back?'],
+    ['How can I increase the speed of my internet connection while using a VPN?', 'How can Internet speed be increased by hacking through DNS?'],
+    ['Why am I mentally very lonely? How can I solve it?', 'Find the remainder when [math]23^{24}[/math] is divided by 24,23?'],
+    ['Which one dissolve in water quikly sugar, salt, methane and carbon di oxide?', 'Which fish would survive in salt water?'],
+]
+scores = model.predict(pairs)
+print(scores.shape)
+# [5]
+
+# ... or rank different texts based on similarity to a single text
+ranks = model.rank(
+    'What is the step by step guide to invest in share market in india?',
+    [
+        'What is the step by step guide to invest in share market?',
+        'What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back?',
+        'How can Internet speed be increased by hacking through DNS?',
+        'Find the remainder when [math]23^{24}[/math] is divided by 24,23?',
+        'Which fish would survive in salt water?',
+    ]
+)
+# [{'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
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+### Metrics
 
-## Environmental Impact
+#### Cross Encoder Classification
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+* Datasets: `quora-duplicates-dev` and `quora-duplicates-test`
+* Evaluated with [<code>CEClassificationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CEClassificationEvaluator)
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+| Metric                | quora-duplicates-dev | quora-duplicates-test |
+|:----------------------|:---------------------|:----------------------|
+| accuracy              | 0.8938               | 0.8938                |
+| accuracy_threshold    | 0.5089               | 0.5091                |
+| f1                    | 0.8612               | 0.8612                |
+| f1_threshold          | 0.3856               | 0.3858                |
+| precision             | 0.8183               | 0.8183                |
+| recall                | 0.9089               | 0.9089                |
+| **average_precision** | **0.9203**           | **0.9203**            |
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+<!--
+## Bias, Risks and Limitations
 
-## Technical Specifications [optional]
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
 
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
+<!--
+### Recommendations
 
-## Model Card Authors [optional]
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
 
-[More Information Needed]
+## Training Details
 
+### Training Dataset
+
+#### quora-duplicates
+
+* Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
+* Size: 404,290 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                                      | sentence2                                                                                      | label                                           |
+  |:--------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                                         | string                                                                                         | int                                             |
+  | details | <ul><li>min: 1 characters</li><li>mean: 59.15 characters</li><li>max: 354 characters</li></ul> | <ul><li>min: 6 characters</li><li>mean: 60.74 characters</li><li>max: 399 characters</li></ul> | <ul><li>0: ~64.20%</li><li>1: ~35.80%</li></ul> |
+* Samples:
+  | sentence1                                                                          | sentence2                                                                                                          | label          |
+  |:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>What are the features of the Indian caste system?</code>                     | <code>What triggers you the most when you play video games?</code>                                                 | <code>0</code> |
+  | <code>What is the best place to learn Mandarin Chinese in Singapore?</code>        | <code>What is the best place in Singapore for durian in December?</code>                                           | <code>0</code> |
+  | <code>What will be Hillary Clinton's India policy if she wins the election?</code> | <code>How would the bilateral relationship between India and the USA be under Hillary Clinton's presidency?</code> | <code>1</code> |
+* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#binarycrossentropyloss)
+
+### Evaluation Dataset
+
+#### quora-duplicates
+
+* Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
+* Size: 404,290 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                                      | sentence2                                                                                       | label                                           |
+  |:--------|:-----------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                                         | string                                                                                          | int                                             |
+  | details | <ul><li>min: 11 characters</li><li>mean: 57.9 characters</li><li>max: 244 characters</li></ul> | <ul><li>min: 12 characters</li><li>mean: 59.33 characters</li><li>max: 221 characters</li></ul> | <ul><li>0: ~62.00%</li><li>1: ~38.00%</li></ul> |
+* Samples:
+  | sentence1                                                                              | sentence2                                                                                             | label          |
+  |:---------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>What is the step by step guide to invest in share market in india?</code>        | <code>What is the step by step guide to invest in share market?</code>                                | <code>0</code> |
+  | <code>What is the story of Kohinoor (Koh-i-Noor) Diamond?</code>                       | <code>What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back?</code> | <code>0</code> |
+  | <code>How can I increase the speed of my internet connection while using a VPN?</code> | <code>How can Internet speed be increased by hacking through DNS?</code>                              | <code>0</code> |
+* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#binarycrossentropyloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+- `bf16`: 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`: 5e-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`: 42
+- `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`: 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`: 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 | Validation Loss | quora-duplicates-dev_average_precision | quora-duplicates-test_average_precision |
+|:------:|:----:|:-------------:|:---------------:|:--------------------------------------:|:---------------------------------------:|
+| -1     | -1   | -             | -               | 0.3711                                 | -                                       |
+| 0.0167 | 100  | 0.6574        | -               | -                                      | -                                       |
+| 0.0333 | 200  | 0.4804        | -               | -                                      | -                                       |
+| 0.0500 | 300  | 0.4406        | -               | -                                      | -                                       |
+| 0.0666 | 400  | 0.4208        | -               | -                                      | -                                       |
+| 0.0833 | 500  | 0.3929        | 0.3958          | 0.8210                                 | -                                       |
+| 0.0999 | 600  | 0.3986        | -               | -                                      | -                                       |
+| 0.1166 | 700  | 0.3743        | -               | -                                      | -                                       |
+| 0.1332 | 800  | 0.3938        | -               | -                                      | -                                       |
+| 0.1499 | 900  | 0.3602        | -               | -                                      | -                                       |
+| 0.1665 | 1000 | 0.3714        | 0.3437          | 0.8565                                 | -                                       |
+| 0.1832 | 1100 | 0.3486        | -               | -                                      | -                                       |
+| 0.1998 | 1200 | 0.3479        | -               | -                                      | -                                       |
+| 0.2165 | 1300 | 0.3417        | -               | -                                      | -                                       |
+| 0.2331 | 1400 | 0.3425        | -               | -                                      | -                                       |
+| 0.2498 | 1500 | 0.3353        | 0.3264          | 0.8742                                 | -                                       |
+| 0.2664 | 1600 | 0.3335        | -               | -                                      | -                                       |
+| 0.2831 | 1700 | 0.3274        | -               | -                                      | -                                       |
+| 0.2998 | 1800 | 0.3284        | -               | -                                      | -                                       |
+| 0.3164 | 1900 | 0.3118        | -               | -                                      | -                                       |
+| 0.3331 | 2000 | 0.3073        | 0.3282          | 0.8826                                 | -                                       |
+| 0.3497 | 2100 | 0.3233        | -               | -                                      | -                                       |
+| 0.3664 | 2200 | 0.3072        | -               | -                                      | -                                       |
+| 0.3830 | 2300 | 0.314         | -               | -                                      | -                                       |
+| 0.3997 | 2400 | 0.3065        | -               | -                                      | -                                       |
+| 0.4163 | 2500 | 0.3046        | 0.2877          | 0.8930                                 | -                                       |
+| 0.4330 | 2600 | 0.2857        | -               | -                                      | -                                       |
+| 0.4496 | 2700 | 0.285         | -               | -                                      | -                                       |
+| 0.4663 | 2800 | 0.2957        | -               | -                                      | -                                       |
+| 0.4829 | 2900 | 0.2965        | -               | -                                      | -                                       |
+| 0.4996 | 3000 | 0.2824        | 0.2842          | 0.8998                                 | -                                       |
+| 0.5162 | 3100 | 0.3019        | -               | -                                      | -                                       |
+| 0.5329 | 3200 | 0.2841        | -               | -                                      | -                                       |
+| 0.5495 | 3300 | 0.2981        | -               | -                                      | -                                       |
+| 0.5662 | 3400 | 0.2878        | -               | -                                      | -                                       |
+| 0.5828 | 3500 | 0.278         | 0.2803          | 0.9061                                 | -                                       |
+| 0.5995 | 3600 | 0.2841        | -               | -                                      | -                                       |
+| 0.6162 | 3700 | 0.2794        | -               | -                                      | -                                       |
+| 0.6328 | 3800 | 0.2808        | -               | -                                      | -                                       |
+| 0.6495 | 3900 | 0.27          | -               | -                                      | -                                       |
+| 0.6661 | 4000 | 0.2719        | 0.2697          | 0.9091                                 | -                                       |
+| 0.6828 | 4100 | 0.2792        | -               | -                                      | -                                       |
+| 0.6994 | 4200 | 0.2669        | -               | -                                      | -                                       |
+| 0.7161 | 4300 | 0.2696        | -               | -                                      | -                                       |
+| 0.7327 | 4400 | 0.2642        | -               | -                                      | -                                       |
+| 0.7494 | 4500 | 0.2684        | 0.2591          | 0.9140                                 | -                                       |
+| 0.7660 | 4600 | 0.2593        | -               | -                                      | -                                       |
+| 0.7827 | 4700 | 0.2756        | -               | -                                      | -                                       |
+| 0.7993 | 4800 | 0.2584        | -               | -                                      | -                                       |
+| 0.8160 | 4900 | 0.2525        | -               | -                                      | -                                       |
+| 0.8326 | 5000 | 0.267         | 0.2540          | 0.9168                                 | -                                       |
+| 0.8493 | 5100 | 0.2612        | -               | -                                      | -                                       |
+| 0.8659 | 5200 | 0.2607        | -               | -                                      | -                                       |
+| 0.8826 | 5300 | 0.2565        | -               | -                                      | -                                       |
+| 0.8993 | 5400 | 0.2432        | -               | -                                      | -                                       |
+| 0.9159 | 5500 | 0.2568        | 0.2489          | 0.9198                                 | -                                       |
+| 0.9326 | 5600 | 0.2572        | -               | -                                      | -                                       |
+| 0.9492 | 5700 | 0.2658        | -               | -                                      | -                                       |
+| 0.9659 | 5800 | 0.2568        | -               | -                                      | -                                       |
+| 0.9825 | 5900 | 0.2539        | -               | -                                      | -                                       |
+| 0.9992 | 6000 | 0.2458        | 0.2503          | 0.9203                                 | -                                       |
+| -1     | -1   | -             | -               | -                                      | 0.9203                                  |
+
+
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Energy Consumed**: 0.069 kWh
+- **Carbon Emitted**: 0.027 kg of CO2
+- **Hours Used**: 0.214 hours
+
+### Training Hardware
+- **On Cloud**: No
+- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
+- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
+- **RAM Size**: 31.78 GB
+
+### Framework Versions
+- Python: 3.11.6
+- Sentence Transformers: 3.5.0.dev0
+- Transformers: 4.49.0.dev0
+- PyTorch: 2.5.0+cu121
+- Accelerate: 1.3.0
+- Datasets: 2.20.0
+- 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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