colbert-small-v1 trained on climatecheck

This is a Cross Encoder model finetuned from answerdotai/answerai-colbert-small-v1 using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

Model Details

Model Description

Model Sources

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import CrossEncoder

# Download from the ๐Ÿค— Hub
model = CrossEncoder("gmguarino/answerai-colbert-small-v1-climatecheck")
# 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. 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.'],
    ['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?', '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. 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. 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.'],
    ['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?', '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. 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. The thousands of cities throughout the world share some features while differing in other aspects related to their age, historical context, governmental policies, and local climate. Thus, the phenomenon of global urbanization represents an unintended but highly replicated global study of experimental evolution.'],
    ['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?', 'Thus, the phenomenon of global urbanization represents an unintended but highly replicated global study of experimental evolution. We can harness this global urban experiment to understand the repeatability and pace of evolution in response to human activity. Among the most important unresolved questions is, how often do native and exotic species adapt to the particular environmental challenges found in cities? Such adaptations could be the difference as to whether a species persists or vanishes from urban areas. In this way, the study of urban evolution can help us understand how evolution in populations may contribute to conservation of rare species, and how populations can be managed to facilitate the establishment of resilient and sustainable urban ecosystems. In a similar way, understanding evolution in urban areas can lead to improved human health. For example, human pests frequently adapt to pesticides and evade control efforts because of our limited understanding of the size of populations and movement of individuals. Applied evolutionary studies could lead to more effective mitigation of pests and disease agents. The study of urban evolution has rapidly become an important frontier in biology, with implications for healthy and sustainable human populations in urban ecosystems.'],
    ['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?', 'The study of urban evolution has rapidly become an important frontier in biology, with implications for healthy and sustainable human populations in urban ecosystems. A gradient in urbanization showing the skyline of Canadaโ€™s sixth largest city (Mississauga, Canada) on the horizon, and the Credit Valley and the University of Toronto Mississauga campus in the foreground. PHOTO CREDIT: ARJUN YADAV Our planet is an increasingly urbanized landscape, with over half of the human population residing in cities. Despite advances in urban ecology, we do not adequately understand how urbanization affects the evolution of organisms, nor how this evolution may affect ecosystems and human health. Here, we review evidence for the effects of urbanization on the evolution of microbes, plants, and animals that inhabit cities. Urbanization affects adaptive and nonadaptive evolutionary processes that shape the genetic diversity within and between populations. Rapid adaptation has facilitated the success of some native species in urban areas, but it has also allowed human pests and disease to spread more rapidly. The nascent field of urban evolution brings together efforts to understand evolution in response to environmental change while developing new hypotheses concerning adaptation to urban infrastructure and human socioeconomic activity.'],
]
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.',
        '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. 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. 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.',
        '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. 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. The thousands of cities throughout the world share some features while differing in other aspects related to their age, historical context, governmental policies, and local climate. Thus, the phenomenon of global urbanization represents an unintended but highly replicated global study of experimental evolution.',
        'Thus, the phenomenon of global urbanization represents an unintended but highly replicated global study of experimental evolution. We can harness this global urban experiment to understand the repeatability and pace of evolution in response to human activity. Among the most important unresolved questions is, how often do native and exotic species adapt to the particular environmental challenges found in cities? Such adaptations could be the difference as to whether a species persists or vanishes from urban areas. In this way, the study of urban evolution can help us understand how evolution in populations may contribute to conservation of rare species, and how populations can be managed to facilitate the establishment of resilient and sustainable urban ecosystems. In a similar way, understanding evolution in urban areas can lead to improved human health. For example, human pests frequently adapt to pesticides and evade control efforts because of our limited understanding of the size of populations and movement of individuals. Applied evolutionary studies could lead to more effective mitigation of pests and disease agents. The study of urban evolution has rapidly become an important frontier in biology, with implications for healthy and sustainable human populations in urban ecosystems.',
        'The study of urban evolution has rapidly become an important frontier in biology, with implications for healthy and sustainable human populations in urban ecosystems. A gradient in urbanization showing the skyline of Canadaโ€™s sixth largest city (Mississauga, Canada) on the horizon, and the Credit Valley and the University of Toronto Mississauga campus in the foreground. PHOTO CREDIT: ARJUN YADAV Our planet is an increasingly urbanized landscape, with over half of the human population residing in cities. Despite advances in urban ecology, we do not adequately understand how urbanization affects the evolution of organisms, nor how this evolution may affect ecosystems and human health. Here, we review evidence for the effects of urbanization on the evolution of microbes, plants, and animals that inhabit cities. Urbanization affects adaptive and nonadaptive evolutionary processes that shape the genetic diversity within and between populations. Rapid adaptation has facilitated the success of some native species in urban areas, but it has also allowed human pests and disease to spread more rapidly. The nascent field of urban evolution brings together efforts to understand evolution in response to environmental change while developing new hypotheses concerning adaptation to urban infrastructure and human socioeconomic activity.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Training Details

Training Dataset

Unnamed Dataset

  • Size: 2,369 training samples
  • Columns: anchor, passage, and label
  • Approximate statistics based on the first 1000 samples:
    anchor passage label
    type string string float
    details
    • min: 30 characters
    • mean: 114.16 characters
    • max: 209 characters
    • min: 77 characters
    • mean: 972.69 characters
    • max: 1537 characters
    • min: 0.0
    • mean: 0.58
    • max: 1.0
  • Samples:
    anchor passage label
    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... 0.0
    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? 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. 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 populati... 0.0
    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? 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. 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... 0.0
  • Loss: BinaryCrossEntropyLoss with these parameters:
    {
        "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: 10
  • warmup_ratio: 0.1
  • fp16: True
  • dataloader_num_workers: 2

All Hyperparameters

Click to expand
  • 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: 10
  • 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

Training Logs

Click to expand
Epoch Step Training Loss
0.0067 1 0.6935
0.6711 100 0.6812
0.0067 1 0.6977
0.0671 10 0.676
0.1342 20 0.6758
0.2013 30 0.6902
0.2685 40 0.6628
0.3356 50 0.6718
0.4027 60 0.6607
0.4698 70 0.6761
0.5369 80 0.6759
0.6040 90 0.6695
0.6711 100 0.6791
0.7383 110 0.6687
0.8054 120 0.6823
0.8725 130 0.682
0.9396 140 0.6784
1.0067 150 0.6745
1.0738 160 0.6505
1.1409 170 0.6132
1.2081 180 0.5973
1.2752 190 0.5939
1.3423 200 0.5827
1.4094 210 0.6278
1.4765 220 0.5761
1.5436 230 0.6245
1.6107 240 0.5832
1.6779 250 0.5128
1.7450 260 0.5529
1.8121 270 0.4886
1.8792 280 0.4943
1.9463 290 0.5186
2.0134 300 0.436
2.0805 310 0.4821
2.1477 320 0.4655
2.2148 330 0.369
2.2819 340 0.4655
2.3490 350 0.3635
2.4161 360 0.5141
2.4832 370 0.4408
2.5503 380 0.4071
2.6174 390 0.5308
2.6846 400 0.4403
2.7517 410 0.3813
2.8188 420 0.5094
2.8859 430 0.3671
2.9530 440 0.4118
3.0201 450 0.3945
3.0872 460 0.3318
3.1544 470 0.3555
3.2215 480 0.4101
3.2886 490 0.3764
3.3557 500 0.4482
3.4228 510 0.3738
3.4899 520 0.3805
3.5570 530 0.3611
3.6242 540 0.4942
3.6913 550 0.3983
3.7584 560 0.353
3.8255 570 0.3006
3.8926 580 0.343
3.9597 590 0.3998
4.0268 600 0.3937
4.0940 610 0.3768
4.1611 620 0.2224
4.2282 630 0.3857
4.2953 640 0.3617
4.3624 650 0.3278
4.4295 660 0.3239
4.4966 670 0.4745
4.5638 680 0.291
4.6309 690 0.3666
4.6980 700 0.322
4.7651 710 0.3243
4.8322 720 0.3856
4.8993 730 0.2332
4.9664 740 0.3998
5.0336 750 0.4055
5.1007 760 0.2819
5.1678 770 0.2425
5.2349 780 0.3387
5.3020 790 0.3621
5.3691 800 0.3063
5.4362 810 0.3306
5.5034 820 0.3163
5.5705 830 0.2516
5.6376 840 0.2746
5.7047 850 0.2841
5.7718 860 0.2799
5.8389 870 0.3608
5.9060 880 0.2268
5.9732 890 0.2612
6.0403 900 0.1797
6.1074 910 0.2328
6.1745 920 0.2906
6.2416 930 0.2722
6.3087 940 0.2567
6.3758 950 0.3215
6.4430 960 0.2563
6.5101 970 0.2294
6.5772 980 0.233
6.6443 990 0.2697
6.7114 1000 0.2585
6.7785 1010 0.2863
6.8456 1020 0.2296
6.9128 1030 0.2399
6.9799 1040 0.2736
7.0470 1050 0.3086
7.1141 1060 0.2781
7.1812 1070 0.2201
7.2483 1080 0.1738
7.3154 1090 0.244
7.3826 1100 0.2045
7.4497 1110 0.2975
7.5168 1120 0.1997
7.5839 1130 0.3476
7.6510 1140 0.3303
7.7181 1150 0.1835
7.7852 1160 0.2045
7.8523 1170 0.2652
7.9195 1180 0.2576
7.9866 1190 0.2281
8.0537 1200 0.2479
8.1208 1210 0.2592
8.1879 1220 0.1455
8.2550 1230 0.3126
8.3221 1240 0.1814
8.3893 1250 0.175
8.4564 1260 0.1712
8.5235 1270 0.2163
8.5906 1280 0.2158
8.6577 1290 0.2192
8.7248 1300 0.22
8.7919 1310 0.3208
8.8591 1320 0.1588
8.9262 1330 0.2477
8.9933 1340 0.1668
9.0604 1350 0.2315
9.1275 1360 0.2444
9.1946 1370 0.363
9.2617 1380 0.1657
9.3289 1390 0.2557
9.3960 1400 0.2392
9.4631 1410 0.1683
9.5302 1420 0.1472
9.5973 1430 0.1653
9.6644 1440 0.2288
9.7315 1450 0.1856
9.7987 1460 0.1729
9.8658 1470 0.2009
9.9329 1480 0.1241
10.0 1490 0.1783

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

@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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