gte-small-pair_score
This is a sentence-transformers model finetuned from thenlper/gte-small. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
Model Details
Model Description
- Model Type: Sentence Transformer
- Base model: thenlper/gte-small
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 384 tokens
- Similarity Function: Cosine Similarity
- Language: en
- License: apache-2.0
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
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 SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'bean bag',
'bag',
'v-neck dress',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Semantic Similarity
- Dataset:
sts-dev
- Evaluated with
EmbeddingSimilarityEvaluator
Metric | Value |
---|---|
pearson_cosine | -0.1723 |
spearman_cosine | -0.2199 |
pearson_manhattan | -0.1876 |
spearman_manhattan | -0.2264 |
pearson_euclidean | -0.1815 |
spearman_euclidean | -0.2199 |
pearson_dot | -0.1723 |
spearman_dot | -0.2199 |
pearson_max | -0.1723 |
spearman_max | -0.2199 |
Training Details
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 32per_device_eval_batch_size
: 32learning_rate
: 2e-05num_train_epochs
: 4warmup_ratio
: 0.1fp16
: Trueload_best_model_at_end
: True
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 32per_device_eval_batch_size
: 32per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 4max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Trueignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falsebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: proportional
Training Logs
Click to expand
Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine |
---|---|---|---|---|
0 | 0 | - | - | -0.2199 |
0.0063 | 100 | 6.3669 | 6.6513 | - |
0.0125 | 200 | 6.1795 | 6.2541 | - |
0.0188 | 300 | 5.893 | 5.7733 | - |
0.0251 | 400 | 5.5099 | 5.3626 | - |
0.0314 | 500 | 5.1589 | 4.9902 | - |
0.0376 | 600 | 4.8599 | 4.6523 | - |
0.0439 | 700 | 4.6075 | 4.4233 | - |
0.0502 | 800 | 4.3831 | 4.2431 | - |
0.0564 | 900 | 4.1737 | 4.1350 | - |
0.0627 | 1000 | 4.0266 | 4.0327 | - |
0.0690 | 1100 | 3.9526 | 3.9281 | - |
0.0752 | 1200 | 3.8773 | 3.8735 | - |
0.0815 | 1300 | 3.7856 | 3.7779 | - |
0.0878 | 1400 | 3.5994 | 3.7054 | - |
0.0941 | 1500 | 3.7067 | 3.6155 | - |
0.1003 | 1600 | 3.5471 | 3.5798 | - |
0.1066 | 1700 | 3.6679 | 3.4654 | - |
0.1129 | 1800 | 3.4484 | 3.4175 | - |
0.1191 | 1900 | 3.377 | 3.4129 | - |
0.1254 | 2000 | 3.4259 | 3.3347 | - |
0.1317 | 2100 | 3.4832 | 3.2113 | - |
0.1380 | 2200 | 3.3043 | 3.1641 | - |
0.1442 | 2300 | 3.2344 | 3.1529 | - |
0.1505 | 2400 | 3.1238 | 3.2577 | - |
0.1568 | 2500 | 3.1456 | 3.0678 | - |
0.1630 | 2600 | 3.0223 | 3.0006 | - |
0.1693 | 2700 | 3.2046 | 2.9682 | - |
0.1756 | 2800 | 3.0866 | 3.0524 | - |
0.1819 | 2900 | 2.9271 | 3.0573 | - |
0.1881 | 3000 | 2.7692 | 3.0558 | - |
0.1944 | 3100 | 3.1498 | 2.7866 | - |
0.2007 | 3200 | 3.0683 | 2.8478 | - |
0.2069 | 3300 | 2.5776 | 2.9459 | - |
0.2132 | 3400 | 2.9394 | 2.7133 | - |
0.2195 | 3500 | 2.6996 | 2.8582 | - |
0.2257 | 3600 | 2.569 | 2.8092 | - |
0.2320 | 3700 | 2.6535 | 2.7977 | - |
0.2383 | 3800 | 2.6679 | 2.8578 | - |
0.2446 | 3900 | 2.592 | 2.8251 | - |
0.2508 | 4000 | 2.4931 | 2.5976 | - |
0.2571 | 4100 | 2.3012 | 2.9260 | - |
0.2634 | 4200 | 2.4728 | 2.7869 | - |
0.2696 | 4300 | 2.4391 | 2.8987 | - |
0.2759 | 4400 | 2.3825 | 2.7804 | - |
0.2822 | 4500 | 2.6257 | 2.8309 | - |
0.2885 | 4600 | 2.4304 | 3.2419 | - |
0.2947 | 4700 | 3.0246 | 2.5732 | - |
0.3010 | 4800 | 2.6894 | 2.8058 | - |
0.3073 | 4900 | 2.5333 | 2.4582 | - |
0.3135 | 5000 | 2.3268 | 2.8622 | - |
0.3198 | 5100 | 2.6996 | 2.7515 | - |
0.3261 | 5200 | 2.8175 | 2.5842 | - |
0.3324 | 5300 | 2.1244 | 2.7252 | - |
0.3386 | 5400 | 2.7331 | 2.5053 | - |
0.3449 | 5500 | 2.3226 | 2.2430 | - |
0.3512 | 5600 | 2.0706 | 2.6055 | - |
0.3574 | 5700 | 2.2461 | 2.8949 | - |
0.3637 | 5800 | 2.6365 | 2.5272 | - |
0.3700 | 5900 | 2.7119 | 2.4331 | - |
0.3762 | 6000 | 2.6146 | 2.3858 | - |
0.3825 | 6100 | 2.1998 | 2.6891 | - |
0.3888 | 6200 | 2.5076 | 2.3827 | - |
0.3951 | 6300 | 2.5244 | 2.6522 | - |
0.4013 | 6400 | 2.0613 | 2.4750 | - |
0.4076 | 6500 | 2.465 | 2.5254 | - |
0.4139 | 6600 | 2.3201 | 2.2249 | - |
0.4201 | 6700 | 2.234 | 2.5168 | - |
0.4264 | 6800 | 2.1277 | 2.5358 | - |
0.4327 | 6900 | 2.3801 | 2.4992 | - |
0.4390 | 7000 | 2.1443 | 2.4043 | - |
0.4452 | 7100 | 1.9136 | 2.3874 | - |
0.4515 | 7200 | 2.3067 | 2.6475 | - |
0.4578 | 7300 | 2.1464 | 2.4704 | - |
0.4640 | 7400 | 2.2151 | 2.5199 | - |
0.4703 | 7500 | 2.4653 | 2.5293 | - |
0.4766 | 7600 | 2.4425 | 2.1264 | - |
0.4828 | 7700 | 2.3138 | 2.1810 | - |
0.4891 | 7800 | 2.247 | 2.1404 | - |
0.4954 | 7900 | 2.1621 | 2.2123 | - |
0.5017 | 8000 | 2.1338 | 2.5108 | - |
0.5079 | 8100 | 2.1846 | 2.1493 | - |
0.5142 | 8200 | 2.1167 | 2.2879 | - |
0.5205 | 8300 | 2.2143 | 2.1664 | - |
0.5267 | 8400 | 2.3152 | 2.1072 | - |
0.5330 | 8500 | 1.7618 | 2.0324 | - |
0.5393 | 8600 | 2.0777 | 2.4468 | - |
0.5456 | 8700 | 2.1573 | 2.2053 | - |
0.5518 | 8800 | 1.9831 | 2.3277 | - |
0.5581 | 8900 | 1.9083 | 1.9949 | - |
0.5644 | 9000 | 1.932 | 1.9848 | - |
0.5706 | 9100 | 2.3223 | 1.9192 | - |
0.5769 | 9200 | 1.7583 | 2.0066 | - |
0.5832 | 9300 | 1.6394 | 2.0322 | - |
0.5895 | 9400 | 1.973 | 2.1010 | - |
0.5957 | 9500 | 2.2377 | 2.1176 | - |
0.6020 | 9600 | 2.2269 | 2.0027 | - |
0.6083 | 9700 | 1.971 | 1.9329 | - |
0.6145 | 9800 | 1.8982 | 1.9797 | - |
0.6208 | 9900 | 2.2853 | 1.8433 | - |
0.6271 | 10000 | 1.6657 | 2.0091 | - |
0.6333 | 10100 | 2.0732 | 1.7602 | - |
0.6396 | 10200 | 1.6951 | 1.8849 | - |
0.6459 | 10300 | 1.6548 | 2.0066 | - |
0.6522 | 10400 | 1.7187 | 1.9644 | - |
0.6584 | 10500 | 2.1948 | 1.8392 | - |
0.6647 | 10600 | 1.9756 | 1.8404 | - |
0.6710 | 10700 | 1.7644 | 1.9101 | - |
0.6772 | 10800 | 1.6295 | 1.9440 | - |
0.6835 | 10900 | 1.7687 | 1.9031 | - |
0.6898 | 11000 | 1.8203 | 1.9650 | - |
0.6961 | 11100 | 2.3055 | 1.8432 | - |
0.7023 | 11200 | 1.8294 | 1.7364 | - |
0.7086 | 11300 | 2.0026 | 1.7894 | - |
0.7149 | 11400 | 1.9916 | 1.8343 | - |
0.7211 | 11500 | 1.8698 | 1.8079 | - |
0.7274 | 11600 | 1.5213 | 1.6849 | - |
0.7337 | 11700 | 1.7462 | 1.7328 | - |
0.7400 | 11800 | 1.3519 | 1.8370 | - |
0.7462 | 11900 | 1.4935 | 1.7247 | - |
0.7525 | 12000 | 1.1721 | 1.6529 | - |
0.7588 | 12100 | 2.2432 | 1.6329 | - |
0.7650 | 12200 | 1.6931 | 1.6563 | - |
0.7713 | 12300 | 1.2736 | 1.6984 | - |
0.7776 | 12400 | 1.7063 | 1.6574 | - |
0.7838 | 12500 | 1.7921 | 1.7760 | - |
0.7901 | 12600 | 1.875 | 1.7149 | - |
0.7964 | 12700 | 1.4435 | 1.8085 | - |
0.8027 | 12800 | 1.5271 | 1.7247 | - |
0.8089 | 12900 | 1.618 | 1.6542 | - |
0.8152 | 13000 | 1.9788 | 1.5686 | - |
0.8215 | 13100 | 1.8213 | 1.5603 | - |
0.8277 | 13200 | 1.3661 | 1.6376 | - |
0.8340 | 13300 | 1.3852 | 1.5953 | - |
0.8403 | 13400 | 1.4673 | 1.6346 | - |
0.8466 | 13500 | 1.6684 | 1.5818 | - |
0.8528 | 13600 | 1.686 | 1.5840 | - |
0.8591 | 13700 | 1.4397 | 1.5855 | - |
0.8654 | 13800 | 1.5973 | 1.7207 | - |
0.8716 | 13900 | 1.221 | 1.6381 | - |
0.8779 | 14000 | 1.2082 | 1.6335 | - |
0.8842 | 14100 | 1.5399 | 1.6434 | - |
0.8904 | 14200 | 1.5265 | 1.7266 | - |
0.8967 | 14300 | 0.9321 | 1.5981 | - |
0.9030 | 14400 | 1.1133 | 1.6126 | - |
0.9093 | 14500 | 1.0754 | 1.6227 | - |
0.9155 | 14600 | 1.3486 | 1.6143 | - |
0.9218 | 14700 | 1.6338 | 1.5452 | - |
0.9281 | 14800 | 1.389 | 1.6099 | - |
0.9343 | 14900 | 1.3776 | 1.6435 | - |
0.9406 | 15000 | 1.8714 | 1.5377 | - |
0.9469 | 15100 | 1.1286 | 1.6326 | - |
0.9532 | 15200 | 1.4029 | 1.6256 | - |
0.9594 | 15300 | 1.7772 | 1.5221 | - |
0.9657 | 15400 | 1.3415 | 1.5604 | - |
0.9720 | 15500 | 1.1088 | 1.5749 | - |
0.9782 | 15600 | 1.4602 | 1.4941 | - |
0.9845 | 15700 | 1.867 | 1.3731 | - |
0.9908 | 15800 | 1.4541 | 1.4206 | - |
0.9971 | 15900 | 1.1966 | 1.4982 | - |
1.0033 | 16000 | 1.1447 | 1.5121 | - |
1.0096 | 16100 | 1.1266 | 1.4103 | - |
1.0159 | 16200 | 1.1971 | 1.5044 | - |
1.0221 | 16300 | 1.3376 | 1.5337 | - |
1.0284 | 16400 | 1.7977 | 1.5712 | - |
1.0347 | 16500 | 1.6946 | 1.5323 | - |
1.0409 | 16600 | 0.8674 | 1.4462 | - |
1.0472 | 16700 | 1.6447 | 1.4831 | - |
1.0535 | 16800 | 1.2709 | 1.5756 | - |
1.0598 | 16900 | 1.5217 | 1.5060 | - |
1.0660 | 17000 | 1.2986 | 1.4834 | - |
1.0723 | 17100 | 0.9976 | 1.4841 | - |
1.0786 | 17200 | 1.3457 | 1.4228 | - |
1.0848 | 17300 | 0.987 | 1.3807 | - |
1.0911 | 17400 | 1.2714 | 1.3471 | - |
1.0974 | 17500 | 1.298 | 1.4134 | - |
1.1037 | 17600 | 0.9522 | 1.4225 | - |
1.1099 | 17700 | 1.0634 | 1.4474 | - |
1.1162 | 17800 | 1.2889 | 1.4679 | - |
1.1225 | 17900 | 1.7532 | 1.3757 | - |
1.1287 | 18000 | 1.6613 | 1.3808 | - |
1.1350 | 18100 | 1.1765 | 1.3903 | - |
1.1413 | 18200 | 1.2787 | 1.3921 | - |
1.1476 | 18300 | 1.2532 | 1.3519 | - |
1.1538 | 18400 | 1.8056 | 1.2984 | - |
1.1601 | 18500 | 1.0985 | 1.3322 | - |
1.1664 | 18600 | 1.8665 | 1.4060 | - |
1.1726 | 18700 | 1.2427 | 1.3775 | - |
1.1789 | 18800 | 1.1241 | 1.3168 | - |
1.1852 | 18900 | 1.2348 | 1.3539 | - |
1.1914 | 19000 | 1.1709 | 1.3540 | - |
1.1977 | 19100 | 0.8844 | 1.3142 | - |
1.2040 | 19200 | 1.0035 | 1.3781 | - |
1.2103 | 19300 | 1.4279 | 1.2615 | - |
1.2165 | 19400 | 1.3327 | 1.2696 | - |
1.2228 | 19500 | 0.993 | 1.3170 | - |
1.2291 | 19600 | 0.7869 | 1.2967 | - |
1.2353 | 19700 | 0.985 | 1.3057 | - |
1.2416 | 19800 | 1.1603 | 1.2797 | - |
1.2479 | 19900 | 1.2469 | 1.2394 | - |
1.2542 | 20000 | 1.521 | 1.2309 | - |
1.2604 | 20100 | 1.2632 | 1.2353 | - |
1.2667 | 20200 | 1.3621 | 1.2433 | - |
1.2730 | 20300 | 1.5145 | 1.3065 | - |
1.2792 | 20400 | 1.3708 | 1.2423 | - |
1.2855 | 20500 | 1.1716 | 1.2923 | - |
1.2918 | 20600 | 1.419 | 1.2194 | - |
1.2980 | 20700 | 1.1644 | 1.2369 | - |
1.3043 | 20800 | 1.6589 | 1.1971 | - |
1.3106 | 20900 | 1.0299 | 1.2343 | - |
1.3169 | 21000 | 1.3452 | 1.2725 | - |
1.3231 | 21100 | 1.4234 | 1.2416 | - |
1.3294 | 21200 | 1.2496 | 1.3609 | - |
1.3357 | 21300 | 1.2133 | 1.2893 | - |
1.3419 | 21400 | 0.8682 | 1.2353 | - |
1.3482 | 21500 | 0.9499 | 1.2423 | - |
1.3545 | 21600 | 1.2896 | 1.1797 | - |
1.3608 | 21700 | 1.2392 | 1.1962 | - |
1.3670 | 21800 | 0.9206 | 1.2483 | - |
1.3733 | 21900 | 1.174 | 1.2328 | - |
1.3796 | 22000 | 1.6361 | 1.1558 | - |
1.3858 | 22100 | 0.8284 | 1.2711 | - |
1.3921 | 22200 | 1.2814 | 1.2462 | - |
1.3984 | 22300 | 1.1595 | 1.2613 | - |
1.4047 | 22400 | 1.3129 | 1.1816 | - |
1.4109 | 22500 | 1.1353 | 1.2454 | - |
1.4172 | 22600 | 1.3302 | 1.1398 | - |
1.4235 | 22700 | 1.1591 | 1.2936 | - |
1.4297 | 22800 | 0.6551 | 1.2345 | - |
1.4360 | 22900 | 1.2884 | 1.1629 | - |
1.4423 | 23000 | 1.1769 | 1.2340 | - |
1.4485 | 23100 | 1.1331 | 1.2036 | - |
1.4548 | 23200 | 1.1008 | 1.1685 | - |
1.4611 | 23300 | 1.1487 | 1.1274 | - |
1.4674 | 23400 | 0.7753 | 1.1738 | - |
1.4736 | 23500 | 1.3236 | 1.2377 | - |
1.4799 | 23600 | 0.919 | 1.2018 | - |
1.4862 | 23700 | 0.8516 | 1.2297 | - |
1.4924 | 23800 | 1.092 | 1.1629 | - |
1.4987 | 23900 | 0.673 | 1.2162 | - |
1.5050 | 24000 | 0.994 | 1.1778 | - |
1.5113 | 24100 | 0.8766 | 1.1902 | - |
1.5175 | 24200 | 1.3818 | 1.1638 | - |
1.5238 | 24300 | 1.1215 | 1.1666 | - |
1.5301 | 24400 | 0.8485 | 1.1907 | - |
1.5363 | 24500 | 1.1033 | 1.2318 | - |
1.5426 | 24600 | 0.9001 | 1.2113 | - |
1.5489 | 24700 | 1.3256 | 1.2309 | - |
1.5552 | 24800 | 0.8162 | 1.2139 | - |
1.5614 | 24900 | 0.5741 | 1.2375 | - |
1.5677 | 25000 | 0.883 | 1.2039 | - |
1.5740 | 25100 | 1.1212 | 1.1428 | - |
1.5802 | 25200 | 0.8229 | 1.2338 | - |
1.5865 | 25300 | 0.8856 | 1.1461 | - |
1.5928 | 25400 | 1.2323 | 1.1569 | - |
1.5990 | 25500 | 0.9724 | 1.1549 | - |
1.6053 | 25600 | 1.0791 | 1.1161 | - |
1.6116 | 25700 | 0.9845 | 1.1061 | - |
1.6179 | 25800 | 1.1591 | 1.1103 | - |
1.6241 | 25900 | 1.116 | 1.1405 | - |
1.6304 | 26000 | 1.2221 | 1.1528 | - |
1.6367 | 26100 | 0.9085 | 1.1396 | - |
1.6429 | 26200 | 0.9543 | 1.1953 | - |
1.6492 | 26300 | 1.1855 | 1.1792 | - |
1.6555 | 26400 | 1.0583 | 1.1666 | - |
1.6618 | 26500 | 0.6583 | 1.1152 | - |
1.6680 | 26600 | 1.3067 | 1.0397 | - |
1.6743 | 26700 | 1.5336 | 1.1244 | - |
1.6806 | 26800 | 0.614 | 1.1273 | - |
1.6868 | 26900 | 1.0336 | 1.0680 | - |
1.6931 | 27000 | 1.462 | 1.0983 | - |
1.6994 | 27100 | 0.8858 | 1.0672 | - |
1.7056 | 27200 | 0.7494 | 1.1624 | - |
1.7119 | 27300 | 0.8152 | 1.0928 | - |
1.7182 | 27400 | 0.7785 | 1.0952 | - |
1.7245 | 27500 | 1.0471 | 1.0999 | - |
1.7307 | 27600 | 1.0994 | 0.9880 | - |
1.7370 | 27700 | 1.0706 | 1.0416 | - |
1.7433 | 27800 | 1.1158 | 1.0676 | - |
1.7495 | 27900 | 0.9893 | 1.0289 | - |
1.7558 | 28000 | 1.2939 | 1.0150 | - |
1.7621 | 28100 | 0.9543 | 1.0767 | - |
1.7684 | 28200 | 0.7907 | 1.0717 | - |
1.7746 | 28300 | 0.92 | 1.1133 | - |
1.7809 | 28400 | 0.8636 | 1.0702 | - |
1.7872 | 28500 | 0.9118 | 1.0536 | - |
1.7934 | 28600 | 1.2643 | 1.1354 | - |
1.7997 | 28700 | 0.8284 | 1.0715 | - |
1.8060 | 28800 | 0.8447 | 1.0458 | - |
1.8123 | 28900 | 1.2102 | 1.1001 | - |
1.8185 | 29000 | 1.1042 | 1.0364 | - |
1.8248 | 29100 | 0.9638 | 1.0947 | - |
1.8311 | 29200 | 0.6847 | 1.0312 | - |
1.8373 | 29300 | 1.7671 | 1.0470 | - |
1.8436 | 29400 | 0.7525 | 1.1158 | - |
1.8499 | 29500 | 1.2843 | 1.0140 | - |
1.8561 | 29600 | 0.6844 | 1.1604 | - |
1.8624 | 29700 | 1.2824 | 1.0052 | - |
1.8687 | 29800 | 1.314 | 1.0323 | - |
1.8750 | 29900 | 1.0796 | 1.0885 | - |
1.8812 | 30000 | 1.0012 | 1.0267 | - |
1.8875 | 30100 | 1.4932 | 1.0438 | - |
1.8938 | 30200 | 1.0404 | 1.0163 | - |
1.9000 | 30300 | 0.614 | 1.0367 | - |
1.9063 | 30400 | 1.2676 | 1.0803 | - |
1.9126 | 30500 | 1.2431 | 1.0428 | - |
1.9189 | 30600 | 1.4063 | 1.0319 | - |
1.9251 | 30700 | 0.7787 | 0.9666 | - |
1.9314 | 30800 | 1.0311 | 1.0376 | - |
1.9377 | 30900 | 1.0353 | 0.9869 | - |
1.9439 | 31000 | 1.2221 | 0.9686 | - |
1.9502 | 31100 | 0.5806 | 0.9663 | - |
1.9565 | 31200 | 0.6919 | 0.9838 | - |
1.9628 | 31300 | 0.8028 | 0.9759 | - |
1.9690 | 31400 | 0.8365 | 0.9641 | - |
1.9753 | 31500 | 0.7518 | 1.0081 | - |
1.9816 | 31600 | 1.0654 | 0.9843 | - |
1.9878 | 31700 | 0.8637 | 0.9635 | - |
1.9941 | 31800 | 0.8663 | 0.9538 | - |
2.0004 | 31900 | 0.8524 | 0.9628 | - |
2.0066 | 32000 | 1.2748 | 0.9382 | - |
2.0129 | 32100 | 0.8138 | 0.9461 | - |
2.0192 | 32200 | 0.4484 | 0.9221 | - |
2.0255 | 32300 | 0.8839 | 0.9567 | - |
2.0317 | 32400 | 0.7599 | 0.9440 | - |
2.0380 | 32500 | 0.8665 | 0.9652 | - |
2.0443 | 32600 | 0.5802 | 0.9475 | - |
2.0505 | 32700 | 0.7731 | 0.9197 | - |
2.0568 | 32800 | 0.7913 | 1.0024 | - |
2.0631 | 32900 | 0.7758 | 0.9257 | - |
2.0694 | 33000 | 0.7468 | 0.9663 | - |
2.0756 | 33100 | 0.9947 | 0.9788 | - |
2.0819 | 33200 | 0.5618 | 0.9480 | - |
2.0882 | 33300 | 0.8805 | 0.9520 | - |
2.0944 | 33400 | 0.9755 | 0.9288 | - |
2.1007 | 33500 | 0.8942 | 0.9234 | - |
2.1070 | 33600 | 0.7242 | 0.9413 | - |
2.1133 | 33700 | 0.6231 | 0.9660 | - |
2.1195 | 33800 | 0.7144 | 0.8901 | - |
2.1258 | 33900 | 0.7139 | 0.9536 | - |
2.1321 | 34000 | 0.6378 | 0.9370 | - |
2.1383 | 34100 | 0.7607 | 0.9209 | - |
2.1446 | 34200 | 0.8667 | 0.9734 | - |
2.1509 | 34300 | 0.8533 | 0.9177 | - |
2.1571 | 34400 | 0.6395 | 0.9285 | - |
2.1634 | 34500 | 0.7377 | 0.9047 | - |
2.1697 | 34600 | 0.7787 | 0.9968 | - |
2.1760 | 34700 | 0.6561 | 0.9653 | - |
2.1822 | 34800 | 0.6169 | 0.9404 | - |
2.1885 | 34900 | 0.7643 | 0.9397 | - |
2.1948 | 35000 | 0.998 | 0.9152 | - |
2.2010 | 35100 | 0.8246 | 0.9513 | - |
2.2073 | 35200 | 0.6655 | 0.9354 | - |
2.2136 | 35300 | 0.9279 | 0.9034 | - |
2.2199 | 35400 | 0.4239 | 0.9607 | - |
2.2261 | 35500 | 1.0023 | 0.8732 | - |
2.2324 | 35600 | 0.7426 | 0.8883 | - |
2.2387 | 35700 | 0.8675 | 0.9296 | - |
2.2449 | 35800 | 0.9226 | 0.9146 | - |
2.2512 | 35900 | 0.4944 | 0.9145 | - |
2.2575 | 36000 | 0.9663 | 0.8893 | - |
2.2637 | 36100 | 0.6455 | 0.9238 | - |
2.2700 | 36200 | 0.9673 | 0.8943 | - |
2.2763 | 36300 | 0.7974 | 0.8620 | - |
2.2826 | 36400 | 0.9777 | 0.8812 | - |
2.2888 | 36500 | 0.8741 | 0.8862 | - |
2.2951 | 36600 | 0.9642 | 0.9157 | - |
2.3014 | 36700 | 0.9225 | 0.8784 | - |
2.3076 | 36800 | 0.6789 | 0.9066 | - |
2.3139 | 36900 | 0.6726 | 0.9091 | - |
2.3202 | 37000 | 0.7326 | 0.9203 | - |
2.3265 | 37100 | 1.007 | 0.9125 | - |
2.3327 | 37200 | 0.6134 | 0.8837 | - |
2.3390 | 37300 | 0.9051 | 0.8945 | - |
2.3453 | 37400 | 0.837 | 0.8740 | - |
2.3515 | 37500 | 0.7615 | 0.9165 | - |
2.3578 | 37600 | 0.8304 | 0.9107 | - |
2.3641 | 37700 | 0.6255 | 0.8891 | - |
2.3704 | 37800 | 0.6775 | 0.8908 | - |
2.3766 | 37900 | 0.7159 | 0.8590 | - |
2.3829 | 38000 | 0.6422 | 0.8559 | - |
2.3892 | 38100 | 0.7773 | 0.8601 | - |
2.3954 | 38200 | 0.5457 | 0.8856 | - |
2.4017 | 38300 | 0.4997 | 0.8785 | - |
2.4080 | 38400 | 0.6319 | 0.8850 | - |
2.4142 | 38500 | 0.7096 | 0.8234 | - |
Framework Versions
- Python: 3.8.10
- Sentence Transformers: 3.1.1
- Transformers: 4.45.1
- PyTorch: 2.4.0+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.1
- Tokenizers: 0.20.0
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",
}
CoSENTLoss
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
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thenlper/gte-smallEvaluation results
- Pearson Cosine on sts devself-reported-0.172
- Spearman Cosine on sts devself-reported-0.220
- Pearson Manhattan on sts devself-reported-0.188
- Spearman Manhattan on sts devself-reported-0.226
- Pearson Euclidean on sts devself-reported-0.182
- Spearman Euclidean on sts devself-reported-0.220
- Pearson Dot on sts devself-reported-0.172
- Spearman Dot on sts devself-reported-0.220
- Pearson Max on sts devself-reported-0.172
- Spearman Max on sts devself-reported-0.220