README.md

---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- cross-encoder
- generated_from_trainer
- dataset_size:3952
- loss:BinaryCrossEntropyLoss
base_model: answerdotai/answerai-colbert-small-v1
pipeline_tag: text-ranking
library_name: sentence-transformers
---

# colbert-small-v1 trained on climatecheck

This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [answerdotai/answerai-colbert-small-v1](https://huggingface.co/answerdotai/answerai-colbert-small-v1) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

## Model Details

### Model Description
- **Model Type:** Cross Encoder
- **Base model:** [answerdotai/answerai-colbert-small-v1](https://huggingface.co/answerdotai/answerai-colbert-small-v1) <!-- at revision be1703c55532145a844da800eea4c9a692d7e267 -->
- **Maximum Sequence Length:** 512 tokens
- **Number of Output Labels:** 1 label
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0

### 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("gmguarino/answerai-colbert-small-v1-climatecheck-chunks")
# Get scores for pairs of texts
pairs = [
    ['Turns out, species that can adapt easily to different environments are often the ones that can survive in a wide variety of places. Interesting, right?', 'Welcome to the city Human populations are shifting en masse to cities, which is leading to rapid increases in the number and extent of urban areas. Such changes are well known to cause declines in many species, but they can also act as alternative selection pressures to which some species are able to adapt. Johnson and Munshi-South review the suite of pressures that urban environments exert, the ways in which species may (or may not) adapt, and the larger impact of these evolutionary events on natural processes and human populations. Understanding such urban evolution patterns will improve our ability to foster species persistence in the face of urbanization and to mitigate some of the challenges, such as disease, that adaptation can bring.'],
    ['Turns out, species that can adapt easily to different environments are often the ones that can survive in a wide variety of places. Interesting, right?', 'Science, this issue p. eaam8327 BACKGROUND The extent of urban areas is increasing around the world, and most humans now live in cities. Urbanization results in dramatic environmental change, including increased temperatures, more impervious surface cover, altered hydrology, and elevated pollution. Urban areas also host more non-native species and reduced abundance and diversity of many native species. These environmental changes brought by global urbanization are creating novel ecosystems with unknown consequences for the evolution of life. Here, we consider how early human settlements led to the evolution of human commensals, including some of the most notorious pests and disease vectors. We also comprehensively review how contemporary urbanization affects the evolution of species that coinhabit cities.'],
    ['Turns out, species that can adapt easily to different environments are often the ones that can survive in a wide variety of places. Interesting, right?', 'We also comprehensively review how contemporary urbanization affects the evolution of species that coinhabit cities. ADVANCES A recent surge of research shows that urbanization affects both nonadaptive and adaptive evolution. Some of the clearest results of urban evolution show that cities elevate the strength of random genetic drift (stochastic changes in allele frequencies) and restrict gene flow (the movement of alleles between populations due to dispersal and mating). Populations of native species in cities often represent either relicts that predate urbanization or populations that established after a city formed. Both scenarios frequently result in a loss of genetic diversity within populations and increased differentiation between populations.'],
    ['Turns out, species that can adapt easily to different environments are often the ones that can survive in a wide variety of places. Interesting, right?', 'Both scenarios frequently result in a loss of genetic diversity within populations and increased differentiation between populations. Fragmentation and urban infrastructure also create barriers to dispersal, and consequently, gene flow is often reduced among city populations, which further contributes to genetic differentiation between populations. The influence of urbanization on mutation and adaptive evolution are less clear. A small number of studies suggest that industrial pollution can elevate mutation rates, but the pervasiveness of this effect is unknown. A better studied phenomenon are the effects of urbanization on evolution by natural selection. A growing number of studies show that plant and animal populations experience divergent selection between urban and nonurban environments.'],
    ['Turns out, species that can adapt easily to different environments are often the ones that can survive in a wide variety of places. Interesting, right?', 'A growing number of studies show that plant and animal populations experience divergent selection between urban and nonurban environments. This divergent selection has led to adaptive evolution in life history, morphology, physiology, behavior, and reproductive traits. These adaptations typically evolve in response to pesticide use, pollution, local climate, or the physical structure of cities. Despite these important results, the genetic basis of adaptive evolution is known from only a few cases. Most studies also examine only a few populations in one city, and experimental validation is rare. OUTLOOK The study of evolution in urban areas provides insights into both fundamental and applied problems in biology.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'Turns out, species that can adapt easily to different environments are often the ones that can survive in a wide variety of places. Interesting, right?',
    [
        'Welcome to the city Human populations are shifting en masse to cities, which is leading to rapid increases in the number and extent of urban areas. Such changes are well known to cause declines in many species, but they can also act as alternative selection pressures to which some species are able to adapt. Johnson and Munshi-South review the suite of pressures that urban environments exert, the ways in which species may (or may not) adapt, and the larger impact of these evolutionary events on natural processes and human populations. Understanding such urban evolution patterns will improve our ability to foster species persistence in the face of urbanization and to mitigate some of the challenges, such as disease, that adaptation can bring.',
        'Science, this issue p. eaam8327 BACKGROUND The extent of urban areas is increasing around the world, and most humans now live in cities. Urbanization results in dramatic environmental change, including increased temperatures, more impervious surface cover, altered hydrology, and elevated pollution. Urban areas also host more non-native species and reduced abundance and diversity of many native species. These environmental changes brought by global urbanization are creating novel ecosystems with unknown consequences for the evolution of life. Here, we consider how early human settlements led to the evolution of human commensals, including some of the most notorious pests and disease vectors. We also comprehensively review how contemporary urbanization affects the evolution of species that coinhabit cities.',
        'We also comprehensively review how contemporary urbanization affects the evolution of species that coinhabit cities. ADVANCES A recent surge of research shows that urbanization affects both nonadaptive and adaptive evolution. Some of the clearest results of urban evolution show that cities elevate the strength of random genetic drift (stochastic changes in allele frequencies) and restrict gene flow (the movement of alleles between populations due to dispersal and mating). Populations of native species in cities often represent either relicts that predate urbanization or populations that established after a city formed. Both scenarios frequently result in a loss of genetic diversity within populations and increased differentiation between populations.',
        'Both scenarios frequently result in a loss of genetic diversity within populations and increased differentiation between populations. Fragmentation and urban infrastructure also create barriers to dispersal, and consequently, gene flow is often reduced among city populations, which further contributes to genetic differentiation between populations. The influence of urbanization on mutation and adaptive evolution are less clear. A small number of studies suggest that industrial pollution can elevate mutation rates, but the pervasiveness of this effect is unknown. A better studied phenomenon are the effects of urbanization on evolution by natural selection. A growing number of studies show that plant and animal populations experience divergent selection between urban and nonurban environments.',
        'A growing number of studies show that plant and animal populations experience divergent selection between urban and nonurban environments. This divergent selection has led to adaptive evolution in life history, morphology, physiology, behavior, and reproductive traits. These adaptations typically evolve in response to pesticide use, pollution, local climate, or the physical structure of cities. Despite these important results, the genetic basis of adaptive evolution is known from only a few cases. Most studies also examine only a few populations in one city, and experimental validation is rare. OUTLOOK The study of evolution in urban areas provides insights into both fundamental and applied problems in biology.',
    ]
)
# [{'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

### Training Dataset

#### Unnamed Dataset

* Size: 3,952 training samples
* Columns: <code>anchor</code>, <code>passage</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                                           | passage                                                                                          | label                                                          |
  |:--------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                                           | string                                                                                           | float                                                          |
  | details | <ul><li>min: 30 characters</li><li>mean: 107.43 characters</li><li>max: 209 characters</li></ul> | <ul><li>min: 83 characters</li><li>mean: 598.79 characters</li><li>max: 954 characters</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.53</li><li>max: 1.0</li></ul> |
* Samples:
  | anchor                                                                                                                                                               | passage                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | label            |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
  | <code>Turns out, species that can adapt easily to different environments are often the ones that can survive in a wide variety of places. Interesting, right?</code> | <code>Welcome to the city Human populations are shifting en masse to cities, which is leading to rapid increases in the number and extent of urban areas. Such changes are well known to cause declines in many species, but they can also act as alternative selection pressures to which some species are able to adapt. Johnson and Munshi-South review the suite of pressures that urban environments exert, the ways in which species may (or may not) adapt, and the larger impact of these evolutionary events on natural processes and human populations. Understanding such urban evolution patterns will improve our ability to foster species persistence in the face of urbanization and to mitigate some of the challenges, such as disease, that adaptation can bring.</code>                                                                   | <code>0.0</code> |
  | <code>Turns out, species that can adapt easily to different environments are often the ones that can survive in a wide variety of places. Interesting, right?</code> | <code>Science, this issue p. eaam8327 BACKGROUND The extent of urban areas is increasing around the world, and most humans now live in cities. Urbanization results in dramatic environmental change, including increased temperatures, more impervious surface cover, altered hydrology, and elevated pollution. Urban areas also host more non-native species and reduced abundance and diversity of many native species. These environmental changes brought by global urbanization are creating novel ecosystems with unknown consequences for the evolution of life. Here, we consider how early human settlements led to the evolution of human commensals, including some of the most notorious pests and disease vectors. We also comprehensively review how contemporary urbanization affects the evolution of species that coinhabit cities.</code> | <code>0.0</code> |
  | <code>Turns out, species that can adapt easily to different environments are often the ones that can survive in a wide variety of places. Interesting, right?</code> | <code>We also comprehensively review how contemporary urbanization affects the evolution of species that coinhabit cities. ADVANCES A recent surge of research shows that urbanization affects both nonadaptive and adaptive evolution. Some of the clearest results of urban evolution show that cities elevate the strength of random genetic drift (stochastic changes in allele frequencies) and restrict gene flow (the movement of alleles between populations due to dispersal and mating). Populations of native species in cities often represent either relicts that predate urbanization or populations that established after a city formed. Both scenarios frequently result in a loss of genetic diversity within populations and increased differentiation between populations.</code>                                                         | <code>0.0</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
  ```json
  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": null
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `fp16`: True
- `dataloader_num_workers`: 2

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 8
- `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`: 5
- `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`: False
- `fp16`: True
- `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`: 2
- `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}
- `tp_size`: 0
- `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
- `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
<details><summary>Click to expand</summary>

| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0040 | 1    | 0.6585        |
| 0.0405 | 10   | 0.6842        |
| 0.0810 | 20   | 0.6886        |
| 0.1215 | 30   | 0.6834        |
| 0.1619 | 40   | 0.69          |
| 0.2024 | 50   | 0.6837        |
| 0.2429 | 60   | 0.685         |
| 0.0040 | 1    | 0.6433        |
| 0.0202 | 5    | 0.6869        |
| 0.0405 | 10   | 0.6759        |
| 0.0607 | 15   | 0.6893        |
| 0.0810 | 20   | 0.6853        |
| 0.1012 | 25   | 0.6718        |
| 0.1215 | 30   | 0.6883        |
| 0.1417 | 35   | 0.6805        |
| 0.1619 | 40   | 0.698         |
| 0.1822 | 45   | 0.6801        |
| 0.2024 | 50   | 0.682         |
| 0.2227 | 55   | 0.676         |
| 0.2429 | 60   | 0.6905        |
| 0.2632 | 65   | 0.6841        |
| 0.2834 | 70   | 0.6837        |
| 0.0040 | 1    | 0.6265        |
| 0.0202 | 5    | 0.6856        |
| 0.0405 | 10   | 0.6707        |
| 0.0607 | 15   | 0.6894        |
| 0.0810 | 20   | 0.6832        |
| 0.1012 | 25   | 0.665         |
| 0.1215 | 30   | 0.6877        |
| 0.1417 | 35   | 0.6769        |
| 0.1619 | 40   | 0.7012        |
| 0.1822 | 45   | 0.6763        |
| 0.2024 | 50   | 0.679         |
| 0.2227 | 55   | 0.6705        |
| 0.2429 | 60   | 0.6917        |
| 0.2632 | 65   | 0.6818        |
| 0.2834 | 70   | 0.6821        |
| 0.3036 | 75   | 0.6588        |
| 0.3239 | 80   | 0.6492        |
| 0.3441 | 85   | 0.6676        |
| 0.3644 | 90   | 0.6397        |
| 0.3846 | 95   | 0.666         |
| 0.4049 | 100  | 0.696         |
| 0.4251 | 105  | 0.683         |
| 0.4453 | 110  | 0.6527        |
| 0.4656 | 115  | 0.7002        |
| 0.4858 | 120  | 0.7081        |
| 0.5061 | 125  | 0.7401        |
| 0.5263 | 130  | 0.6776        |
| 0.5466 | 135  | 0.6653        |
| 0.5668 | 140  | 0.6787        |
| 0.5870 | 145  | 0.6678        |
| 0.6073 | 150  | 0.6821        |
| 0.6275 | 155  | 0.6536        |
| 0.6478 | 160  | 0.6869        |
| 0.6680 | 165  | 0.6754        |
| 0.6883 | 170  | 0.7247        |
| 0.7085 | 175  | 0.7043        |
| 0.7287 | 180  | 0.6749        |
| 0.7490 | 185  | 0.6787        |
| 0.7692 | 190  | 0.6628        |
| 0.7895 | 195  | 0.6699        |
| 0.8097 | 200  | 0.6463        |
| 0.8300 | 205  | 0.6361        |
| 0.8502 | 210  | 0.619         |
| 0.8704 | 215  | 0.6158        |
| 0.8907 | 220  | 0.6604        |
| 0.9109 | 225  | 0.5839        |
| 0.9312 | 230  | 0.5836        |
| 0.9514 | 235  | 0.5937        |
| 0.9717 | 240  | 0.6356        |
| 0.9919 | 245  | 0.5775        |
| 1.0121 | 250  | 0.5566        |
| 1.0324 | 255  | 0.5879        |
| 1.0526 | 260  | 0.5725        |
| 1.0729 | 265  | 0.5549        |
| 1.0931 | 270  | 0.5182        |
| 1.1134 | 275  | 0.5257        |
| 1.1336 | 280  | 0.5633        |
| 1.1538 | 285  | 0.5348        |
| 1.1741 | 290  | 0.5485        |
| 1.1943 | 295  | 0.574         |
| 1.2146 | 300  | 0.5967        |
| 1.2348 | 305  | 0.4148        |
| 1.2551 | 310  | 0.5355        |
| 1.2753 | 315  | 0.5388        |
| 1.2955 | 320  | 0.4969        |
| 1.3158 | 325  | 0.4887        |
| 1.3360 | 330  | 0.5494        |
| 1.3563 | 335  | 0.4695        |
| 1.3765 | 340  | 0.6148        |
| 1.3968 | 345  | 0.6179        |
| 1.4170 | 350  | 0.555         |
| 1.4372 | 355  | 0.4942        |
| 1.4575 | 360  | 0.4936        |
| 1.4777 | 365  | 0.4824        |
| 1.4980 | 370  | 0.4783        |
| 1.5182 | 375  | 0.6457        |
| 1.5385 | 380  | 0.4025        |
| 1.5587 | 385  | 0.4587        |
| 1.5789 | 390  | 0.5683        |
| 1.5992 | 395  | 0.5296        |
| 1.6194 | 400  | 0.4801        |
| 1.6397 | 405  | 0.452         |
| 1.6599 | 410  | 0.3888        |
| 1.6802 | 415  | 0.4634        |
| 1.7004 | 420  | 0.5594        |
| 1.7206 | 425  | 0.4489        |
| 1.7409 | 430  | 0.5764        |
| 1.7611 | 435  | 0.4233        |
| 1.7814 | 440  | 0.4016        |
| 1.8016 | 445  | 0.4774        |
| 1.8219 | 450  | 0.5146        |
| 1.8421 | 455  | 0.601         |
| 1.8623 | 460  | 0.4857        |
| 1.8826 | 465  | 0.4385        |
| 1.9028 | 470  | 0.474         |
| 1.9231 | 475  | 0.4027        |
| 1.9433 | 480  | 0.557         |
| 1.9636 | 485  | 0.5921        |
| 1.9838 | 490  | 0.4424        |
| 2.0040 | 495  | 0.5546        |
| 2.0243 | 500  | 0.4989        |
| 2.0445 | 505  | 0.5128        |
| 2.0648 | 510  | 0.4469        |
| 2.0850 | 515  | 0.4591        |
| 2.1053 | 520  | 0.4151        |
| 2.1255 | 525  | 0.5473        |
| 2.1457 | 530  | 0.4153        |
| 2.1660 | 535  | 0.3811        |
| 2.1862 | 540  | 0.3508        |
| 2.2065 | 545  | 0.4734        |
| 2.2267 | 550  | 0.3578        |
| 2.2470 | 555  | 0.3539        |
| 2.2672 | 560  | 0.3924        |
| 2.2874 | 565  | 0.3067        |
| 2.3077 | 570  | 0.3795        |
| 2.3279 | 575  | 0.37          |
| 2.3482 | 580  | 0.3612        |
| 2.3684 | 585  | 0.3223        |
| 2.3887 | 590  | 0.4666        |
| 2.4089 | 595  | 0.4536        |
| 2.4291 | 600  | 0.4246        |
| 2.4494 | 605  | 0.4609        |
| 2.4696 | 610  | 0.404         |
| 2.4899 | 615  | 0.4847        |
| 2.5101 | 620  | 0.5884        |
| 2.5304 | 625  | 0.5785        |
| 2.5506 | 630  | 0.5211        |
| 2.5709 | 635  | 0.3566        |
| 2.5911 | 640  | 0.3911        |
| 2.6113 | 645  | 0.4295        |
| 2.6316 | 650  | 0.3605        |
| 2.6518 | 655  | 0.5329        |
| 2.6721 | 660  | 0.4455        |
| 2.6923 | 665  | 0.3665        |
| 2.7126 | 670  | 0.4392        |
| 2.7328 | 675  | 0.3559        |
| 2.7530 | 680  | 0.4053        |
| 2.7733 | 685  | 0.5254        |
| 2.7935 | 690  | 0.4304        |
| 2.8138 | 695  | 0.3854        |
| 2.8340 | 700  | 0.4575        |
| 2.8543 | 705  | 0.5575        |
| 2.8745 | 710  | 0.4285        |
| 2.8947 | 715  | 0.4409        |
| 2.9150 | 720  | 0.3567        |
| 2.9352 | 725  | 0.3528        |
| 2.9555 | 730  | 0.4723        |
| 2.9757 | 735  | 0.4635        |
| 2.9960 | 740  | 0.4086        |
| 3.0162 | 745  | 0.3767        |
| 3.0364 | 750  | 0.4548        |
| 3.0567 | 755  | 0.3311        |
| 3.0769 | 760  | 0.3817        |
| 3.0972 | 765  | 0.3231        |
| 3.1174 | 770  | 0.479         |
| 3.1377 | 775  | 0.4462        |
| 3.1579 | 780  | 0.383         |
| 3.1781 | 785  | 0.3326        |
| 3.1984 | 790  | 0.4318        |
| 3.2186 | 795  | 0.4167        |
| 3.2389 | 800  | 0.487         |
| 3.2591 | 805  | 0.362         |
| 3.2794 | 810  | 0.3862        |
| 3.2996 | 815  | 0.4245        |
| 3.3198 | 820  | 0.261         |
| 3.3401 | 825  | 0.3718        |
| 3.3603 | 830  | 0.3077        |
| 3.3806 | 835  | 0.3098        |
| 3.4008 | 840  | 0.2903        |
| 3.4211 | 845  | 0.4764        |
| 3.4413 | 850  | 0.3676        |
| 3.4615 | 855  | 0.4486        |
| 3.4818 | 860  | 0.3227        |
| 3.5020 | 865  | 0.3489        |
| 3.5223 | 870  | 0.4432        |
| 3.5425 | 875  | 0.3406        |
| 3.5628 | 880  | 0.4052        |
| 3.5830 | 885  | 0.2647        |
| 3.6032 | 890  | 0.399         |
| 3.6235 | 895  | 0.2908        |
| 3.6437 | 900  | 0.4351        |
| 3.6640 | 905  | 0.3273        |
| 3.6842 | 910  | 0.4671        |
| 3.7045 | 915  | 0.2794        |
| 3.7247 | 920  | 0.4279        |
| 3.7449 | 925  | 0.239         |
| 3.7652 | 930  | 0.3938        |
| 3.7854 | 935  | 0.4376        |
| 3.8057 | 940  | 0.4792        |
| 3.8259 | 945  | 0.3866        |
| 3.8462 | 950  | 0.2753        |
| 3.8664 | 955  | 0.2502        |
| 3.8866 | 960  | 0.3265        |
| 3.9069 | 965  | 0.4292        |
| 3.9271 | 970  | 0.2987        |
| 3.9474 | 975  | 0.3569        |
| 3.9676 | 980  | 0.3146        |
| 3.9879 | 985  | 0.3535        |
| 4.0081 | 990  | 0.4034        |
| 4.0283 | 995  | 0.4426        |
| 4.0486 | 1000 | 0.3035        |
| 4.0688 | 1005 | 0.2956        |
| 4.0891 | 1010 | 0.3476        |
| 4.1093 | 1015 | 0.3306        |
| 4.1296 | 1020 | 0.2093        |
| 4.1498 | 1025 | 0.3127        |
| 4.1700 | 1030 | 0.3995        |
| 4.1903 | 1035 | 0.4798        |
| 4.2105 | 1040 | 0.2275        |
| 4.2308 | 1045 | 0.4751        |
| 4.2510 | 1050 | 0.4534        |
| 4.2713 | 1055 | 0.3419        |
| 4.2915 | 1060 | 0.3475        |
| 4.3117 | 1065 | 0.2916        |
| 4.3320 | 1070 | 0.3456        |
| 4.3522 | 1075 | 0.3619        |
| 4.3725 | 1080 | 0.2504        |
| 4.3927 | 1085 | 0.2638        |
| 4.4130 | 1090 | 0.3414        |
| 4.4332 | 1095 | 0.2609        |
| 4.4534 | 1100 | 0.2555        |
| 4.4737 | 1105 | 0.3007        |
| 4.4939 | 1110 | 0.3586        |
| 4.5142 | 1115 | 0.4047        |
| 4.5344 | 1120 | 0.271         |
| 4.5547 | 1125 | 0.2517        |
| 4.5749 | 1130 | 0.4167        |
| 4.5951 | 1135 | 0.341         |
| 4.6154 | 1140 | 0.3734        |
| 4.6356 | 1145 | 0.3632        |
| 4.6559 | 1150 | 0.4568        |
| 4.6761 | 1155 | 0.3237        |
| 4.6964 | 1160 | 0.4222        |
| 4.7166 | 1165 | 0.2528        |
| 4.7368 | 1170 | 0.2831        |
| 4.7571 | 1175 | 0.5008        |
| 4.7773 | 1180 | 0.2495        |
| 4.7976 | 1185 | 0.3158        |
| 4.8178 | 1190 | 0.5574        |
| 4.8381 | 1195 | 0.3171        |
| 4.8583 | 1200 | 0.193         |
| 4.8785 | 1205 | 0.408         |
| 4.8988 | 1210 | 0.2704        |
| 4.9190 | 1215 | 0.2975        |
| 4.9393 | 1220 | 0.1966        |
| 4.9595 | 1225 | 0.38          |
| 4.9798 | 1230 | 0.4851        |
| 5.0    | 1235 | 0.3162        |

</details>

### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

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