metadata

language:
  - en
license: apache-2.0
tags:
  - biencoder
  - sentence-transformers
  - text-classification
  - sentence-pair-classification
  - semantic-similarity
  - semantic-search
  - retrieval
  - reranking
  - generated_from_trainer
  - dataset_size:483820
  - loss:MultipleNegativesSymmetricRankingLoss
base_model: Alibaba-NLP/gte-modernbert-base
widget:
  - source_sentence: >-
      See Precambrian time scale # Proposed Geologic timeline for another set of
      periods 4600 -- 541 MYA .
    sentences:
      - >-
        In 2014 election , Biju Janata Dal candidate Tathagat Satapathy
        Bharatiya Janata party candidate Rudra Narayan Pany defeated with a
        margin of 1.37,340 votes .
      - >-
        In Scotland , the Strathclyde Partnership for Transport , formerly known
        as Strathclyde Passenger Transport Executive , comprises the former
        Strathclyde region , which includes the urban area around Glasgow .
      - >-
        See Precambrian Time Scale # Proposed Geological Timeline for another
        set of periods of 4600 -- 541 MYA .
  - source_sentence: >-
      It is also 5 kilometers northeast of Tamaqua , 27 miles south of Allentown
      and 9 miles northwest of Hazleton .
    sentences:
      - In 1948 he moved to Massachusetts , and eventually settled in Vermont .
      - >-
        Suddenly I remembered that I was a New Zealander , I caught the first
        plane home and came back .
      - >-
        It is also 5 miles northeast of Tamaqua , 27 miles south of Allentown ,
        and 9 miles northwest of Hazleton .
  - source_sentence: >-
      The party has a Member of Parliament , a member of the House of Lords ,
      three members of the London Assembly and two Members of the European
      Parliament .
    sentences:
      - >-
        The party has one Member of Parliament , one member of the House of
        Lords , three Members of the London Assembly and two Members of the
        European Parliament .
      - >-
        Grapsid crabs dominate in Australia , Malaysia and Panama , while
        gastropods Cerithidea scalariformis and Melampus coeffeus are important
        seed predators in Florida mangroves .
      - >-
        Music Story is a music service website and international music data
        provider that curates , aggregates and analyses metadata for digital
        music services .
  - source_sentence: >-
      The play received two 1969 Tony Award nominations : Best Actress in a Play
      ( Michael Annals ) and Best Costume Design ( Charlotte Rae ) .
    sentences:
      - >-
        Ravishanker is a fellow of the International Statistical Institute and
        an elected member of the American Statistical Association .
      - >-
        In 1969 , the play received two Tony - Award nominations : Best Actress
        in a Theatre Play ( Michael Annals ) and Best Costume Design ( Charlotte
        Rae ) .
      - >-
        AMD and Nvidia both have proprietary methods of scaling , CrossFireX for
        AMD , and SLI for Nvidia .
  - source_sentence: >-
      He was a close friend of Ángel Cabrera and is a cousin of golfer Tony
      Croatto .
    sentences:
      - >-
        He was a close friend of Ángel Cabrera , and is a cousin of golfer Tony
        Croatto .
      - >-
        Eugenijus Bartulis ( born December 7 , 1949 in Kaunas ) is a Lithuanian
        Roman Catholic priest , and Bishop of Šiauliai .
      - >-
        UWIRE also distributes its members content to professional media outlets
        , including Yahoo , CNN and CBS News .
datasets:
  - redis/langcache-sentencepairs-v1
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_precision@1
  - cosine_recall@1
  - cosine_ndcg@10
  - cosine_mrr@1
  - cosine_map@100
model-index:
  - name: Redis fine-tuned BiEncoder model for semantic caching on LangCache
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: train
          type: train
        metrics:
          - type: cosine_accuracy@1
            value: 0.5978783286425633
            name: Cosine Accuracy@1
          - type: cosine_precision@1
            value: 0.5978783286425633
            name: Cosine Precision@1
          - type: cosine_recall@1
            value: 0.5765917883925028
            name: Cosine Recall@1
          - type: cosine_ndcg@10
            value: 0.7905393533594786
            name: Cosine Ndcg@10
          - type: cosine_mrr@1
            value: 0.5978783286425633
            name: Cosine Mrr@1
          - type: cosine_map@100
            value: 0.7375956597574003
            name: Cosine Map@100

Redis fine-tuned BiEncoder model for semantic caching on LangCache

This is a sentence-transformers model finetuned from Alibaba-NLP/gte-modernbert-base on the LangCache Sentence Pairs (all) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for sentence pair similarity.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: Alibaba-NLP/gte-modernbert-base
Maximum Sequence Length: 100 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- LangCache Sentence Pairs (all)
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': 100, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

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("redis/langcache-embed-v3")
# Run inference
sentences = [
    'He was a close friend of Ángel Cabrera and is a cousin of golfer Tony Croatto .',
    'He was a close friend of Ángel Cabrera , and is a cousin of golfer Tony Croatto .',
    'UWIRE also distributes its members content to professional media outlets , including Yahoo , CNN and CBS News .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9922, 0.0547],
#         [0.9922, 1.0000, 0.0449],
#         [0.0547, 0.0449, 1.0000]], dtype=torch.bfloat16)

Evaluation

Metrics

Information Retrieval

Dataset: train
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.5979
cosine_precision@1	0.5979
cosine_recall@1	0.5766
cosine_ndcg@10	0.7905
cosine_mrr@1	0.5979
cosine_map@100	0.7376

Training Details

Training Dataset

LangCache Sentence Pairs (all)

Dataset: LangCache Sentence Pairs (all)
Size: 26,850 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string int
details
min: 8 tokens
mean: 27.35 tokens
max: 53 tokens

min: 8 tokens
mean: 27.27 tokens
max: 52 tokens

1: 100.00%

	sentence1	sentence2	label
type	string	string	int
details	min: 8 tokens mean: 27.35 tokens max: 53 tokens	min: 8 tokens mean: 27.27 tokens max: 52 tokens	1: 100.00%

Samples:

sentence1	sentence2	label
`The newer Punts are still very much in existence today and race in the same fleets as the older boats .`	`The newer punts are still very much in existence today and run in the same fleets as the older boats .`	`1`
`After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .`	`Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .`	`1`
`The 12F was officially homologated on August 21 , 1929 and exhibited at the Paris Salon in 1930 .`	`The 12F was officially homologated on 21 August 1929 and displayed at the 1930 Paris Salon .`	`1`

Loss: MultipleNegativesSymmetricRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": false
}

Evaluation Dataset

LangCache Sentence Pairs (all)

Dataset: LangCache Sentence Pairs (all)
Size: 26,850 evaluation samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string int
details
min: 8 tokens
mean: 27.35 tokens
max: 53 tokens

min: 8 tokens
mean: 27.27 tokens
max: 52 tokens

1: 100.00%

	sentence1	sentence2	label
type	string	string	int
details	min: 8 tokens mean: 27.35 tokens max: 53 tokens	min: 8 tokens mean: 27.27 tokens max: 52 tokens	1: 100.00%

Samples:

sentence1	sentence2	label
`The newer Punts are still very much in existence today and race in the same fleets as the older boats .`	`The newer punts are still very much in existence today and run in the same fleets as the older boats .`	`1`
`After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .`	`Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .`	`1`
`The 12F was officially homologated on August 21 , 1929 and exhibited at the Paris Salon in 1930 .`	`The 12F was officially homologated on 21 August 1929 and displayed at the 1930 Paris Salon .`	`1`

Loss: MultipleNegativesSymmetricRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": false
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
learning_rate: 0.0003
adam_beta2: 0.98
adam_epsilon: 1e-06
max_steps: 200000
warmup_steps: 1000
load_best_model_at_end: True
optim: adamw_torch
ddp_find_unused_parameters: False
push_to_hub: True
hub_model_id: redis/langcache-embed-v3
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
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: 0.0003
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.98
adam_epsilon: 1e-06
max_grad_norm: 1.0
num_train_epochs: 3.0
max_steps: 200000
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
warmup_steps: 1000
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: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: 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}
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}
parallelism_config: 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: False
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: True
resume_from_checkpoint: None
hub_model_id: redis/langcache-embed-v3
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
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
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	train_cosine_ndcg@10
-1	-1	-	-	0.7522
0.5291	1000	0.0231	0.1710	0.7518
1.0582	2000	0.0147	0.1552	0.7593
1.5873	3000	0.0126	0.1616	0.7603
2.1164	4000	0.0113	0.1301	0.7644
2.6455	5000	0.0119	0.1276	0.7659
3.1746	6000	0.0099	0.1270	0.7648
3.7037	7000	0.0101	0.1239	0.7676
4.2328	8000	0.0093	0.1267	0.7709
4.7619	9000	0.0092	0.1190	0.7711
5.2910	10000	0.0088	0.1145	0.7735
5.8201	11000	0.009	0.1172	0.7735
6.3492	12000	0.0083	0.1144	0.7749
6.8783	13000	0.0088	0.1140	0.7736
7.4074	14000	0.0083	0.1134	0.7751
7.9365	15000	0.0087	0.1108	0.7742
8.4656	16000	0.0084	0.1119	0.7759
8.9947	17000	0.0081	0.1125	0.7762
9.5238	18000	0.0081	0.1134	0.7768
10.0529	19000	0.008	0.1126	0.7766
10.5820	20000	0.0079	0.1119	0.7755
11.1111	21000	0.0078	0.1112	0.7781
11.6402	22000	0.008	0.1113	0.7778
12.1693	23000	0.0082	0.1066	0.7796
12.6984	24000	0.0078	0.1098	0.7775
13.2275	25000	0.0078	0.1089	0.7800
13.7566	26000	0.0074	0.1091	0.7779
14.2857	27000	0.0078	0.1061	0.7782
14.8148	28000	0.0074	0.1073	0.7769
15.3439	29000	0.0078	0.1022	0.7804
15.8730	30000	0.0078	0.1035	0.7799
16.4021	31000	0.0074	0.1046	0.7793
16.9312	32000	0.0074	0.1043	0.7817
17.4603	33000	0.0071	0.1056	0.7831
17.9894	34000	0.0074	0.1022	0.7820
18.5185	35000	0.0073	0.1035	0.7820
19.0476	36000	0.0074	0.1020	0.7836
19.5767	37000	0.0071	0.1036	0.7828
20.1058	38000	0.007	0.1029	0.7845
20.6349	39000	0.0071	0.1019	0.7835
21.1640	40000	0.007	0.0991	0.7849
21.6931	41000	0.0071	0.1013	0.7828
22.2222	42000	0.0073	0.1033	0.7833
22.7513	43000	0.0068	0.0996	0.7835
23.2804	44000	0.007	0.0976	0.7850
23.8095	45000	0.0069	0.0986	0.7840
24.3386	46000	0.0068	0.0992	0.7856
24.8677	47000	0.0068	0.0988	0.7838
25.3968	48000	0.0068	0.0980	0.7857
25.9259	49000	0.007	0.0976	0.7860
26.4550	50000	0.0071	0.0994	0.7850
26.9841	51000	0.0067	0.0984	0.7862
27.5132	52000	0.0064	0.0992	0.7845
28.0423	53000	0.0068	0.1021	0.7840
28.5714	54000	0.0066	0.0974	0.7863
29.1005	55000	0.0066	0.1001	0.7848
29.6296	56000	0.0067	0.0997	0.7848
30.1587	57000	0.0067	0.0965	0.7868
30.6878	58000	0.0067	0.0968	0.7858
31.2169	59000	0.0066	0.0973	0.7861
31.7460	60000	0.0067	0.0972	0.7865
32.2751	61000	0.0065	0.0991	0.7855
32.8042	62000	0.0062	0.0960	0.7871
33.3333	63000	0.0068	0.1006	0.7863
33.8624	64000	0.0063	0.0980	0.7872
34.3915	65000	0.0066	0.0957	0.7871
34.9206	66000	0.0066	0.0971	0.7870
35.4497	67000	0.0063	0.0982	0.7857
35.9788	68000	0.0067	0.0944	0.7871
36.5079	69000	0.0062	0.0961	0.7870
37.0370	70000	0.0061	0.0924	0.7880
37.5661	71000	0.0064	0.0928	0.7878
38.0952	72000	0.0065	0.0934	0.7888
38.6243	73000	0.0069	0.0948	0.7873
39.1534	74000	0.0064	0.0922	0.7885
39.6825	75000	0.0064	0.0937	0.7888
40.2116	76000	0.0059	0.0941	0.7882
40.7407	77000	0.0067	0.0934	0.7900
41.2698	78000	0.0064	0.0926	0.7888
41.7989	79000	0.006	0.0948	0.7880
42.3280	80000	0.006	0.0953	0.7876
42.8571	81000	0.0058	0.0955	0.7887
43.3862	82000	0.0065	0.0945	0.7875
43.9153	83000	0.0063	0.0928	0.7888
44.4444	84000	0.0065	0.0959	0.7883
44.9735	85000	0.0063	0.0956	0.7876
45.5026	86000	0.006	0.0946	0.7893
46.0317	87000	0.0062	0.0954	0.7908
46.5608	88000	0.0061	0.0960	0.7896
47.0899	89000	0.006	0.0953	0.7893
47.6190	90000	0.0058	0.0941	0.7899
48.1481	91000	0.0059	0.0950	0.7892
48.6772	92000	0.0066	0.0948	0.7890
49.2063	93000	0.0058	0.0947	0.7886
49.7354	94000	0.006	0.0952	0.7891
50.2646	95000	0.0058	0.0948	0.7885
50.7937	96000	0.0058	0.0945	0.7894
51.3228	97000	0.0059	0.0936	0.7901
51.8519	98000	0.0059	0.0950	0.7900
52.3810	99000	0.0058	0.0954	0.7893
52.9101	100000	0.0058	0.0946	0.7900
53.4392	101000	0.0056	0.0943	0.7900
53.9683	102000	0.006	0.0950	0.7895
54.4974	103000	0.0059	0.0937	0.7899
55.0265	104000	0.0061	0.0941	0.7897
55.5556	105000	0.0059	0.0941	0.7903
56.0847	106000	0.0057	0.0924	0.7904
56.6138	107000	0.006	0.0933	0.7901
57.1429	108000	0.0059	0.0948	0.7888
57.6720	109000	0.0061	0.0938	0.7899
58.2011	110000	0.0058	0.0942	0.7904
58.7302	111000	0.0056	0.0943	0.7913
59.2593	112000	0.0056	0.0949	0.7915
59.7884	113000	0.0058	0.0947	0.7907
60.3175	114000	0.0058	0.0939	0.7910
60.8466	115000	0.0058	0.0942	0.7906
61.3757	116000	0.0055	0.0933	0.7910
61.9048	117000	0.0055	0.0936	0.7913
62.4339	118000	0.0059	0.0937	0.7904
62.9630	119000	0.0057	0.0943	0.7908
63.4921	120000	0.0056	0.0934	0.7912
64.0212	121000	0.0058	0.0936	0.7909
64.5503	122000	0.0055	0.0942	0.7896
65.0794	123000	0.0058	0.0939	0.7901
65.6085	124000	0.0057	0.0936	0.7907
66.1376	125000	0.0054	0.0951	0.7901
66.6667	126000	0.0055	0.0942	0.7912
67.1958	127000	0.0057	0.0943	0.7914
67.7249	128000	0.0057	0.0937	0.7910
68.2540	129000	0.0057	0.0933	0.7918
68.7831	130000	0.0055	0.0935	0.7913
69.3122	131000	0.0053	0.0935	0.7908
69.8413	132000	0.0057	0.0937	0.7905
70.3704	133000	0.0055	0.0940	0.7912
70.8995	134000	0.0052	0.0937	0.7913
71.4286	135000	0.005	0.0940	0.7917
71.9577	136000	0.0053	0.0933	0.7914
72.4868	137000	0.0056	0.0940	0.7915
73.0159	138000	0.0054	0.0937	0.7909
73.5450	139000	0.0051	0.0940	0.7909
74.0741	140000	0.0058	0.0938	0.7911
74.6032	141000	0.0056	0.0938	0.7912
75.1323	142000	0.0052	0.0931	0.7908
75.6614	143000	0.0052	0.0937	0.7905
76.1905	144000	0.0054	0.0940	0.7905
76.7196	145000	0.0055	0.0940	0.7907
77.2487	146000	0.0053	0.0941	0.7909
77.7778	147000	0.0057	0.0944	0.7907
78.3069	148000	0.0054	0.0947	0.7909
78.8360	149000	0.0054	0.0949	0.7907
79.3651	150000	0.0055	0.0948	0.7907
79.8942	151000	0.0058	0.0950	0.7907
80.4233	152000	0.0054	0.0946	0.7907
80.9524	153000	0.0053	0.0949	0.7909
81.4815	154000	0.0055	0.0947	0.7908
82.0106	155000	0.0053	0.0946	0.7906
82.5397	156000	0.0053	0.0949	0.7906
83.0688	157000	0.0051	0.0948	0.7912
83.5979	158000	0.0052	0.0954	0.7906
84.1270	159000	0.0054	0.0953	0.7908
84.6561	160000	0.005	0.0951	0.7911
85.1852	161000	0.0054	0.0953	0.7910
85.7143	162000	0.0056	0.0957	0.7907
86.2434	163000	0.0054	0.0953	0.7909
86.7725	164000	0.0051	0.0955	0.7912
87.3016	165000	0.0055	0.0956	0.7911
87.8307	166000	0.0056	0.0954	0.7909
88.3598	167000	0.0052	0.0955	0.7911
88.8889	168000	0.0052	0.0953	0.7910
89.4180	169000	0.0052	0.0952	0.7906
89.9471	170000	0.0053	0.0952	0.7908
90.4762	171000	0.0052	0.0954	0.7908
91.0053	172000	0.0054	0.0954	0.7907
91.5344	173000	0.0052	0.0951	0.7909
92.0635	174000	0.0053	0.0951	0.7907
92.5926	175000	0.0051	0.0950	0.7906
93.1217	176000	0.0054	0.0953	0.7907
93.6508	177000	0.0052	0.0953	0.7907
94.1799	178000	0.0051	0.0951	0.7908
94.7090	179000	0.0052	0.0952	0.7906
95.2381	180000	0.0053	0.0953	0.7909
95.7672	181000	0.0052	0.0953	0.7908
96.2963	182000	0.0051	0.0952	0.7906
96.8254	183000	0.0053	0.0953	0.7907
97.3545	184000	0.0051	0.0953	0.7907
97.8836	185000	0.0049	0.0953	0.7906
98.4127	186000	0.0051	0.0953	0.7907
98.9418	187000	0.0051	0.0954	0.7906
99.4709	188000	0.0053	0.0954	0.7906
100.0	189000	0.0051	0.0954	0.7904
100.5291	190000	0.0054	0.0953	0.7907
101.0582	191000	0.0052	0.0954	0.7905
101.5873	192000	0.0051	0.0954	0.7907
102.1164	193000	0.0052	0.0953	0.7907
102.6455	194000	0.0051	0.0955	0.7908
103.1746	195000	0.0054	0.0954	0.7906
103.7037	196000	0.0052	0.0954	0.7905
104.2328	197000	0.0053	0.0954	0.7906
104.7619	198000	0.0052	0.0954	0.7907
105.2910	199000	0.0055	0.0954	0.7904
105.8201	200000	0.0054	0.0955	0.7905

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.3
Sentence Transformers: 5.1.0
Transformers: 4.56.0
PyTorch: 2.8.0+cu128
Accelerate: 1.10.1
Datasets: 4.0.0
Tokenizers: 0.22.0

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",
}