This is a Cross Encoder model finetuned from cross-encoder/ms-marco-MiniLM-L12-v2 on the climate-cross-encoder-mixed-neg-v3 dataset 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 Type: Cross Encoder
Base model: cross-encoder/ms-marco-MiniLM-L12-v2
Maximum Sequence Length: 512 tokens
Number of Output Labels: 1 label
Training Dataset:
- climate-cross-encoder-mixed-neg-v3

Model Sources

Documentation: Sentence Transformers Documentation
Documentation: Cross Encoder Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Cross Encoders on Hugging Face

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.

python

from sentence_transformers import CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("CharlesPing/finetuned-ce-climate-multineg-v1")
# Get scores for pairs of texts
pairs = [
    ['Scientific analysis of past climates\xa0shows that greenhouse gasses, principally CO2,\xa0have controlled most ancient\xa0climate changes.', 'Greenhouse gases, in particular carbon dioxide and methane, played a significant role during the Eocene in controlling the surface temperature.'],
    ['Scientific analysis of past climates\xa0shows that greenhouse gasses, principally CO2,\xa0have controlled most ancient\xa0climate changes.', 'Climatic geomorphology is of limited use to study recent (Quaternary, Holocene) large climate changes since there are seldom discernible in the geomorphological record.'],
    ['Scientific analysis of past climates\xa0shows that greenhouse gasses, principally CO2,\xa0have controlled most ancient\xa0climate changes.', 'There is also a close correlation between CO2 and temperature, where CO2 has a strong control over global temperatures in Earth history.'],
    ['Scientific analysis of past climates\xa0shows that greenhouse gasses, principally CO2,\xa0have controlled most ancient\xa0climate changes.', 'While scientists knew of past climate change such as the ice ages, the concept of climate as unchanging was useful in the development of a general theory of what determines climate.'],
    ['Scientific analysis of past climates\xa0shows that greenhouse gasses, principally CO2,\xa0have controlled most ancient\xa0climate changes.', 'Some long term modifications along the history of the planet have been significant, such as the incorporation of oxygen to the atmosphere.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'Scientific analysis of past climates\xa0shows that greenhouse gasses, principally CO2,\xa0have controlled most ancient\xa0climate changes.',
    [
        'Greenhouse gases, in particular carbon dioxide and methane, played a significant role during the Eocene in controlling the surface temperature.',
        'Climatic geomorphology is of limited use to study recent (Quaternary, Holocene) large climate changes since there are seldom discernible in the geomorphological record.',
        'There is also a close correlation between CO2 and temperature, where CO2 has a strong control over global temperatures in Earth history.',
        'While scientists knew of past climate change such as the ice ages, the concept of climate as unchanging was useful in the development of a general theory of what determines climate.',
        'Some long term modifications along the history of the planet have been significant, such as the incorporation of oxygen to the atmosphere.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Reranking

Dataset: climate-rerank-multineg
Evaluated with CrossEncoderRerankingEvaluator with these parameters:
json
1{ 2 "at_k": 1, 3 "always_rerank_positives": false 4}

Metric	Value
map	0.6809 (-0.3191)
mrr@1	0.6748 (-0.3252)
ndcg@1	0.6748 (-0.3252)

Training Details

Training Dataset

climate-cross-encoder-mixed-neg-v3

Dataset: climate-cross-encoder-mixed-neg-v3 at cd49b57
Size: 41,052 training samples
Columns: query, doc, and label
Approximate statistics based on the first 1000 samples:
query doc label
type string string float
details
min: 49 characters
mean: 140.03 characters
max: 306 characters
min: 4 characters
mean: 136.03 characters
max: 731 characters
min: 0.0
mean: 0.09
max: 1.0

Samples:

query	doc	label
`“A leading Canadian authority on polar bears, Mitch Taylor, said: ‘We’re seeing an increase in bears that’s really unprecedented, and in places where we’re seeing a decrease in the population`	`Warnings about the future of the polar bear are often contrasted with the fact that worldwide population estimates have increased over the past 50 years and are relatively stable today.`	`1.0`
`“A leading Canadian authority on polar bears, Mitch Taylor, said: ‘We’re seeing an increase in bears that’s really unprecedented, and in places where we’re seeing a decrease in the population`	`Species distribution models of recent years indicate that the deer tick, known as "I. scapularis," is pushing its distribution to higher latitudes of the Northeastern United States and Canada, as well as pushing and maintaining populations in the South Central and Northern Midwest regions of the United States.`	`0.0`
`“A leading Canadian authority on polar bears, Mitch Taylor, said: ‘We’re seeing an increase in bears that’s really unprecedented, and in places where we’re seeing a decrease in the population`	`Bear and deer are among the animals present.`	`0.0`

Loss: BinaryCrossEntropyLoss with these parameters:
json
1{ 2 "activation_fn": "torch.nn.modules.linear.Identity", 3 "pos_weight": null 4}

Evaluation Dataset

climate-cross-encoder-mixed-neg-v3

Dataset: climate-cross-encoder-mixed-neg-v3 at cd49b57
Size: 4,290 evaluation samples
Columns: query, doc, and label
Approximate statistics based on the first 1000 samples:
query doc label
type string string float
details
min: 39 characters
mean: 116.67 characters
max: 240 characters
min: 18 characters
mean: 132.92 characters
max: 731 characters
min: 0.0
mean: 0.09
max: 1.0

Samples:

query	doc	label
`Scientific analysis of past climates shows that greenhouse gasses, principally CO2, have controlled most ancient climate changes.`	`Greenhouse gases, in particular carbon dioxide and methane, played a significant role during the Eocene in controlling the surface temperature.`	`1.0`
`Scientific analysis of past climates shows that greenhouse gasses, principally CO2, have controlled most ancient climate changes.`	`Climatic geomorphology is of limited use to study recent (Quaternary, Holocene) large climate changes since there are seldom discernible in the geomorphological record.`	`0.0`
`Scientific analysis of past climates shows that greenhouse gasses, principally CO2, have controlled most ancient climate changes.`	`There is also a close correlation between CO2 and temperature, where CO2 has a strong control over global temperatures in Earth history.`	`0.0`

Loss: BinaryCrossEntropyLoss with these parameters:
json
1{ 2 "activation_fn": "torch.nn.modules.linear.Identity", 3 "pos_weight": null 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 32
learning_rate: 2e-05
warmup_ratio: 0.1
fp16: True
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 32
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: 3
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: 0
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}
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

Epoch	Step	Training Loss	Validation Loss	climate-rerank-multineg_ndcg@1
0.0390	100	0.5097	-	-
0.0779	200	0.3662	-	-
0.1169	300	0.3034	-	-
0.1559	400	0.2655	-	-
0.1949	500	0.2651	0.2262	0.6585 (-0.3415)
0.2338	600	0.2161	-	-
0.2728	700	0.227	-	-
0.3118	800	0.235	-	-
0.3507	900	0.2243	-	-
0.3897	1000	0.2081	0.2174	0.6992 (-0.3008)
0.4287	1100	0.1961	-	-
0.4677	1200	0.207	-	-
0.5066	1300	0.2375	-	-
0.5456	1400	0.2117	-	-
0.5846	1500	0.2058	0.2253	0.6748 (-0.3252)
0.6235	1600	0.2163	-	-
0.6625	1700	0.2235	-	-
0.7015	1800	0.2193	-	-
0.7405	1900	0.1924	-	-
0.7794	2000	0.2084	0.2095	0.6748 (-0.3252)
0.8184	2100	0.2113	-	-
0.8574	2200	0.2276	-	-
0.8963	2300	0.2071	-	-
0.9353	2400	0.2374	-	-
0.9743	2500	0.2173	0.2172	0.6667 (-0.3333)
1.0133	2600	0.2011	-	-
1.0522	2700	0.1634	-	-
1.0912	2800	0.1807	-	-
1.1302	2900	0.1878	-	-
1.1691	3000	0.2037	0.2147	0.6911 (-0.3089)
1.2081	3100	0.1904	-	-
1.2471	3200	0.1911	-	-
1.2860	3300	0.1828	-	-
1.3250	3400	0.1686	-	-
1.3640	3500	0.1892	0.2179	0.6992 (-0.3008)
1.4030	3600	0.188	-	-
1.4419	3700	0.1691	-	-
1.4809	3800	0.1946	-	-
1.5199	3900	0.1938	-	-
1.5588	4000	0.211	0.2088	0.6992 (-0.3008)
1.5978	4100	0.1826	-	-
1.6368	4200	0.1608	-	-
1.6758	4300	0.1782	-	-
1.7147	4400	0.1803	-	-
1.7537	4500	0.1804	0.2160	0.6911 (-0.3089)
1.7927	4600	0.1823	-	-
1.8316	4700	0.1844	-	-
1.8706	4800	0.1727	-	-
1.9096	4900	0.1937	-	-
1.9486	5000	0.1662	0.2219	0.6829 (-0.3171)
1.9875	5100	0.1653	-	-
2.0265	5200	0.1658	-	-
2.0655	5300	0.1316	-	-
2.1044	5400	0.1379	-	-
2.1434	5500	0.152	0.2513	0.6504 (-0.3496)
2.1824	5600	0.1848	-	-
2.2214	5700	0.1507	-	-
2.2603	5800	0.1495	-	-
2.2993	5900	0.1469	-	-
2.3383	6000	0.1596	0.2407	0.6585 (-0.3415)
2.3772	6100	0.1518	-	-
2.4162	6200	0.1351	-	-
2.4552	6300	0.1706	-	-
2.4942	6400	0.1538	-	-
2.5331	6500	0.1329	0.2505	0.6911 (-0.3089)
2.5721	6600	0.147	-	-
2.6111	6700	0.1289	-	-
2.6500	6800	0.1698	-	-
2.6890	6900	0.1456	-	-
2.7280	7000	0.141	0.2618	0.6748 (-0.3252)
2.7670	7100	0.1413	-	-
2.8059	7200	0.1474	-	-
2.8449	7300	0.1381	-	-
2.8839	7400	0.1252	-	-
2.9228	7500	0.1384	0.2608	0.6748 (-0.3252)
2.9618	7600	0.1826	-	-

The bold row denotes the saved checkpoint.

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",
}

	query	doc	label
type	string	string	float
details	min: 49 characters mean: 140.03 characters max: 306 characters	min: 4 characters mean: 136.03 characters max: 731 characters	min: 0.0 mean: 0.09 max: 1.0