Add new CrossEncoder model

482394a verified 8 months ago

36 kB

language:
  - en
tags:
  - sentence-transformers
  - cross-encoder
  - reranker
  - generated_from_trainer
  - dataset_size:654438
  - loss:BinaryCrossEntropyLoss
base_model: jhu-clsp/ettin-encoder-68m
datasets:
  - microsoft/ms_marco
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
  - map
  - mrr@10
  - ndcg@10
model-index:
  - name: CrossEncoder based on jhu-clsp/ettin-encoder-68m
    results:
      - task:
          type: cross-encoder-reranking
          name: Cross Encoder Reranking
        dataset:
          name: NanoMSMARCO R100
          type: NanoMSMARCO_R100
        metrics:
          - type: map
            value: 0.4957
            name: Map
          - type: mrr@10
            value: 0.4842
            name: Mrr@10
          - type: ndcg@10
            value: 0.5462
            name: Ndcg@10
      - task:
          type: cross-encoder-reranking
          name: Cross Encoder Reranking
        dataset:
          name: NanoNFCorpus R100
          type: NanoNFCorpus_R100
        metrics:
          - type: map
            value: 0.3315
            name: Map
          - type: mrr@10
            value: 0.4747
            name: Mrr@10
          - type: ndcg@10
            value: 0.3291
            name: Ndcg@10
      - task:
          type: cross-encoder-reranking
          name: Cross Encoder Reranking
        dataset:
          name: NanoNQ R100
          type: NanoNQ_R100
        metrics:
          - type: map
            value: 0.4563
            name: Map
          - type: mrr@10
            value: 0.4704
            name: Mrr@10
          - type: ndcg@10
            value: 0.531
            name: Ndcg@10
      - task:
          type: cross-encoder-nano-beir
          name: Cross Encoder Nano BEIR
        dataset:
          name: NanoBEIR R100 mean
          type: NanoBEIR_R100_mean
        metrics:
          - type: map
            value: 0.4279
            name: Map
          - type: mrr@10
            value: 0.4764
            name: Mrr@10
          - type: ndcg@10
            value: 0.4688
            name: Ndcg@10

CrossEncoder based on jhu-clsp/ettin-encoder-68m

This is a Cross Encoder model finetuned from jhu-clsp/ettin-encoder-68m on the ms_marco 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: jhu-clsp/ettin-encoder-68m
Maximum Sequence Length: 7999 tokens
Number of Output Labels: 1 label
Training Dataset:
- ms_marco
Language: en

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.

from sentence_transformers import CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("bansalaman18/reranker-msmarco-v1.1-ettin-encoder-68m-bce")
# Get scores for pairs of texts
pairs = [
    ['how to put word count on word', 'To insert a word count into a Word 2013 document, place the cursor where you would like the word count to appear (say in the Header or Footer) and then: 1  click the Insert tab. 2  click the Quick Parts icon (towards the right hand end of the toolbar). 3  on the drop down that appears, select Field...'],
    ['what is the difference between discipleship and evangelism', 'Discipleship, on the other hand, meant helping someone who was already a believer walk out the life of faith. The word “discipleship” brought to my mind a small group Bible study, a conversation across the table with another woman, or an accountability group. And I knew which one I preferred. As a result, the discipleship I offered others contained a lot of good information but lacked the transforming power that can only come from the gospel. (I was also, simply, a coward.). I am beginning to see that evangelism and discipleship are not all that different.'],
    ['what metal is a trophy made from', 'The trophy stands 36.5 centimetres (14.4 inches) tall and is made  of 5 kg (11 lb) of 18 carat (75%) gold with a base (13 centimetres  [5.1 inches] in diameter) containing t … wo layers of malachite. Making the world better, one answer at a time. Trophies can be made out of anything you want. however, aluminum is a very reliable and trustworthy metal and it.......... oh crap.......... i have to do a poo...'],
    ['how do you define what a cult is?', 'The term cult has been misused. The word cult comes from the French cult which is from the Latin word cultus (care/adoration) and Latin Colere (to cultivate.) So, we can plant seeds of good or bad. You can have political cults such as sit ins during the Vietnam War. A good cult could be a religious one, yet some Christians will consider Jehovah Witness a cult and have labeled them as preying on the weak. When someone labels such a thing it is usually because of the lack of understanding. Good cults are usually a small group of people that can have a cult in most anything.'],
    ['where is silchar', 'Silchar (/ˈsɪlˌʧə/ or /ˈʃɪlˌʧə/) (Bengali: শিলচর Shilchor) shilchôr is the headquarters Of cachar district in the state Of assam In. India it is 343 (kilometres 213) mi south east Of. Guwahati it is the-second largest city of the state in terms of population and municipal. area 1 The Bhubaneshwar temple is about 50 km from Silchar and is on the top the Bhuvan hill. 2  This is a place of pilgrimage and during the festival of Shivaratri, thousand of Shivayats march towards the hilltop to worship Lord Shiva.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'how to put word count on word',
    [
        'To insert a word count into a Word 2013 document, place the cursor where you would like the word count to appear (say in the Header or Footer) and then: 1  click the Insert tab. 2  click the Quick Parts icon (towards the right hand end of the toolbar). 3  on the drop down that appears, select Field...',
        'Discipleship, on the other hand, meant helping someone who was already a believer walk out the life of faith. The word “discipleship” brought to my mind a small group Bible study, a conversation across the table with another woman, or an accountability group. And I knew which one I preferred. As a result, the discipleship I offered others contained a lot of good information but lacked the transforming power that can only come from the gospel. (I was also, simply, a coward.). I am beginning to see that evangelism and discipleship are not all that different.',
        'The trophy stands 36.5 centimetres (14.4 inches) tall and is made  of 5 kg (11 lb) of 18 carat (75%) gold with a base (13 centimetres  [5.1 inches] in diameter) containing t … wo layers of malachite. Making the world better, one answer at a time. Trophies can be made out of anything you want. however, aluminum is a very reliable and trustworthy metal and it.......... oh crap.......... i have to do a poo...',
        'The term cult has been misused. The word cult comes from the French cult which is from the Latin word cultus (care/adoration) and Latin Colere (to cultivate.) So, we can plant seeds of good or bad. You can have political cults such as sit ins during the Vietnam War. A good cult could be a religious one, yet some Christians will consider Jehovah Witness a cult and have labeled them as preying on the weak. When someone labels such a thing it is usually because of the lack of understanding. Good cults are usually a small group of people that can have a cult in most anything.',
        'Silchar (/ˈsɪlˌʧə/ or /ˈʃɪlˌʧə/) (Bengali: শিলচর Shilchor) shilchôr is the headquarters Of cachar district in the state Of assam In. India it is 343 (kilometres 213) mi south east Of. Guwahati it is the-second largest city of the state in terms of population and municipal. area 1 The Bhubaneshwar temple is about 50 km from Silchar and is on the top the Bhuvan hill. 2  This is a place of pilgrimage and during the festival of Shivaratri, thousand of Shivayats march towards the hilltop to worship Lord Shiva.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Reranking

Datasets: NanoMSMARCO_R100, NanoNFCorpus_R100 and NanoNQ_R100

Evaluated with CrossEncoderRerankingEvaluator with these parameters:

{
    "at_k": 10,
    "always_rerank_positives": true
}

Metric	NanoMSMARCO_R100	NanoNFCorpus_R100	NanoNQ_R100
map	0.4957 (+0.0061)	0.3315 (+0.0706)	0.4563 (+0.0367)
mrr@10	0.4842 (+0.0067)	0.4747 (-0.0251)	0.4704 (+0.0437)
ndcg@10	0.5462 (+0.0058)	0.3291 (+0.0040)	0.5310 (+0.0304)

Cross Encoder Nano BEIR

Dataset: NanoBEIR_R100_mean

Evaluated with CrossEncoderNanoBEIREvaluator with these parameters:

{
    "dataset_names": [
        "msmarco",
        "nfcorpus",
        "nq"
    ],
    "rerank_k": 100,
    "at_k": 10,
    "always_rerank_positives": true
}

Metric	Value
map	0.4279 (+0.0378)
mrr@10	0.4764 (+0.0084)
ndcg@10	0.4688 (+0.0134)

Training Details

Training Dataset

ms_marco

Dataset: ms_marco at a47ee7a
Size: 654,438 training samples
Columns: query, response, and label

Approximate statistics based on the first 1000 samples:

	query	response	label
type	string	string	float
details	min: 11 characters mean: 34.1 characters max: 95 characters	min: 62 characters mean: 417.26 characters max: 939 characters	min: 0.0 mean: 0.13 max: 1.0

Samples:

query	response	label
`what is a polyhedron definition`	A polyhedron is said to be convex if its surface (comprising its faces, edges and vertices) does not intersect itself and the line segment joining any two points of the polyhedron is contained in the interior or surface. A polyhedron is a 3-dimensional example of the more general polytope in any number of dimensions. Polyhedra with congruent regular faces of six or more sides are all non-convex, because the vertex of three regular hexagons defines a plane. The total number of convex polyhedra with equal regular faces is thus ten, comprising the five Platonic solids and the five non-uniform deltahedra.	`0.0`
`what can you carry in hand luggage on easyjet`	`Each passenger who pays for a hold bag can take up to 20kg of luggage. This weight allowance applies to the passenger rather than to the bag so purchasing extra bags is possible but will not increase the weight allowance.`	`0.0`
`what is dynamic segmentation in gis`	The result of the dynamic segmentation process is a dynamic feature class known as a route event source. A route event source can serve as the data source of a feature layer in ArcMap. For the most part, a dynamic feature layer behaves like any other feature layer. Event locating errors. The dynamic segmentation process creates a shape for each row in the input route event table. In some cases, however, the shape of the event feature might be empty. This happens when there is a reason that the event can't be properly located.	`0.0`

Loss: BinaryCrossEntropyLoss with these parameters:

{
    "activation_fn": "torch.nn.modules.linear.Identity",
    "pos_weight": null
}

Evaluation Dataset

ms_marco

Dataset: ms_marco at a47ee7a
Size: 1,000 evaluation samples
Columns: query, response, and label

Approximate statistics based on the first 1000 samples:

	query	response	label
type	string	string	float
details	min: 10 characters mean: 33.73 characters max: 117 characters	min: 57 characters mean: 412.4 characters max: 918 characters	min: 0.0 mean: 0.13 max: 1.0

Samples:

query	response	label
`how to put word count on word`	`To insert a word count into a Word 2013 document, place the cursor where you would like the word count to appear (say in the Header or Footer) and then: 1 click the Insert tab. 2 click the Quick Parts icon (towards the right hand end of the toolbar). 3 on the drop down that appears, select Field...`	`0.0`
`what is the difference between discipleship and evangelism`	Discipleship, on the other hand, meant helping someone who was already a believer walk out the life of faith. The word “discipleship” brought to my mind a small group Bible study, a conversation across the table with another woman, or an accountability group. And I knew which one I preferred. As a result, the discipleship I offered others contained a lot of good information but lacked the transforming power that can only come from the gospel. (I was also, simply, a coward.). I am beginning to see that evangelism and discipleship are not all that different.	`0.0`
`what metal is a trophy made from`	`The trophy stands 36.5 centimetres (14.4 inches) tall and is made of 5 kg (11 lb) of 18 carat (75%) gold with a base (13 centimetres [5.1 inches] in diameter) containing t … wo layers of malachite. Making the world better, one answer at a time. Trophies can be made out of anything you want. however, aluminum is a very reliable and trustworthy metal and it.......... oh crap.......... i have to do a poo...`	`1.0`

Loss: BinaryCrossEntropyLoss with these parameters:

{
    "activation_fn": "torch.nn.modules.linear.Identity",
    "pos_weight": null
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
learning_rate: 2e-05
num_train_epochs: 1
warmup_ratio: 0.1
seed: 12
bf16: True
remove_unused_columns: False
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: 128
per_device_eval_batch_size: 128
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: 1
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: 12
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
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: False
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
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	NanoMSMARCO_R100_ndcg@10	NanoNFCorpus_R100_ndcg@10	NanoNQ_R100_ndcg@10	NanoBEIR_R100_mean_ndcg@10
-1	-1	-	-	0.0442 (-0.4962)	0.2555 (-0.0695)	0.0464 (-0.4542)	0.1154 (-0.3400)
0.0002	1	2.1556	-	-	-	-	-
0.0196	100	0.84	0.3976	0.0474 (-0.4930)	0.2826 (-0.0424)	0.0395 (-0.4612)	0.1232 (-0.3322)
0.0391	200	0.402	0.3895	0.0508 (-0.4896)	0.2367 (-0.0883)	0.0673 (-0.4334)	0.1183 (-0.3371)
0.0587	300	0.3988	0.3854	0.0763 (-0.4642)	0.2175 (-0.1076)	0.1093 (-0.3913)	0.1344 (-0.3210)
0.0782	400	0.3899	0.3968	0.1461 (-0.3943)	0.2087 (-0.1163)	0.1574 (-0.3433)	0.1707 (-0.2846)
0.0978	500	0.3898	0.3708	0.2417 (-0.2987)	0.2259 (-0.0991)	0.2326 (-0.2680)	0.2334 (-0.2219)
0.1173	600	0.3783	0.3681	0.2933 (-0.2471)	0.2769 (-0.0481)	0.3659 (-0.1347)	0.3120 (-0.1433)
0.1369	700	0.3718	0.3610	0.3654 (-0.1751)	0.2852 (-0.0398)	0.4239 (-0.0768)	0.3582 (-0.0972)
0.1565	800	0.3643	0.3657	0.4721 (-0.0683)	0.2658 (-0.0593)	0.4801 (-0.0206)	0.4060 (-0.0494)
0.1760	900	0.3639	0.3637	0.4291 (-0.1113)	0.2635 (-0.0615)	0.4308 (-0.0699)	0.3745 (-0.0809)
0.1956	1000	0.3686	0.3518	0.4859 (-0.0545)	0.3071 (-0.0179)	0.5328 (+0.0322)	0.4419 (-0.0134)
0.2151	1100	0.3585	0.3529	0.4581 (-0.0823)	0.2611 (-0.0640)	0.5113 (+0.0107)	0.4102 (-0.0452)
0.2347	1200	0.3624	0.3522	0.4918 (-0.0486)	0.3244 (-0.0006)	0.4936 (-0.0071)	0.4366 (-0.0188)
0.2543	1300	0.3684	0.3607	0.3933 (-0.1471)	0.2893 (-0.0358)	0.4810 (-0.0196)	0.3879 (-0.0675)
0.2738	1400	0.3651	0.3437	0.4999 (-0.0406)	0.3170 (-0.0081)	0.5290 (+0.0283)	0.4486 (-0.0068)
0.2934	1500	0.3568	0.3466	0.5099 (-0.0306)	0.3312 (+0.0062)	0.4966 (-0.0040)	0.4459 (-0.0095)
0.3129	1600	0.36	0.3437	0.4762 (-0.0642)	0.3488 (+0.0237)	0.4963 (-0.0043)	0.4404 (-0.0149)
0.3325	1700	0.3557	0.3451	0.4572 (-0.0833)	0.3046 (-0.0204)	0.5173 (+0.0167)	0.4264 (-0.0290)
0.3520	1800	0.3529	0.3406	0.4707 (-0.0697)	0.3180 (-0.0071)	0.4842 (-0.0165)	0.4243 (-0.0311)
0.3716	1900	0.3514	0.3367	0.4901 (-0.0503)	0.2742 (-0.0509)	0.5322 (+0.0316)	0.4321 (-0.0232)
0.3912	2000	0.3499	0.3408	0.4859 (-0.0545)	0.2814 (-0.0437)	0.5011 (+0.0004)	0.4228 (-0.0326)
0.4107	2100	0.3595	0.3393	0.4821 (-0.0583)	0.3004 (-0.0246)	0.5499 (+0.0493)	0.4441 (-0.0112)
0.4303	2200	0.356	0.3442	0.4939 (-0.0465)	0.3279 (+0.0029)	0.5454 (+0.0448)	0.4557 (+0.0004)
0.4498	2300	0.3396	0.3351	0.5252 (-0.0152)	0.3024 (-0.0226)	0.5271 (+0.0264)	0.4516 (-0.0038)
0.4694	2400	0.3644	0.3396	0.5307 (-0.0098)	0.3204 (-0.0046)	0.5101 (+0.0094)	0.4537 (-0.0017)
0.4889	2500	0.3508	0.3371	0.5003 (-0.0402)	0.3006 (-0.0245)	0.5404 (+0.0398)	0.4471 (-0.0083)
0.5085	2600	0.3525	0.3396	0.5146 (-0.0258)	0.3001 (-0.0249)	0.5525 (+0.0518)	0.4557 (+0.0004)
0.5281	2700	0.3348	0.3393	0.4800 (-0.0604)	0.2778 (-0.0472)	0.5416 (+0.0410)	0.4332 (-0.0222)
0.5476	2800	0.3448	0.3458	0.5176 (-0.0229)	0.2905 (-0.0345)	0.5243 (+0.0236)	0.4441 (-0.0113)
0.5672	2900	0.3508	0.3379	0.4738 (-0.0667)	0.2924 (-0.0326)	0.5395 (+0.0389)	0.4352 (-0.0201)
0.5867	3000	0.3401	0.3404	0.5246 (-0.0158)	0.2930 (-0.0321)	0.5337 (+0.0330)	0.4504 (-0.0049)
0.6063	3100	0.3508	0.3383	0.5004 (-0.0400)	0.2890 (-0.0360)	0.5321 (+0.0314)	0.4405 (-0.0149)
0.6259	3200	0.3509	0.3364	0.5097 (-0.0308)	0.3321 (+0.0071)	0.5502 (+0.0496)	0.4640 (+0.0086)
0.6454	3300	0.3501	0.3369	0.5172 (-0.0232)	0.3084 (-0.0167)	0.5644 (+0.0637)	0.4633 (+0.0080)
0.6650	3400	0.3417	0.3336	0.4947 (-0.0457)	0.3133 (-0.0117)	0.5404 (+0.0397)	0.4495 (-0.0059)
0.6845	3500	0.3487	0.3335	0.4994 (-0.0410)	0.3328 (+0.0078)	0.5351 (+0.0344)	0.4558 (+0.0004)
0.7041	3600	0.3507	0.3377	0.5103 (-0.0301)	0.3111 (-0.0139)	0.5030 (+0.0023)	0.4415 (-0.0139)
0.7236	3700	0.34	0.3382	0.5254 (-0.0150)	0.3320 (+0.0070)	0.5154 (+0.0148)	0.4576 (+0.0023)
0.7432	3800	0.3392	0.3361	0.4892 (-0.0512)	0.3261 (+0.0011)	0.5268 (+0.0262)	0.4474 (-0.0080)
0.7628	3900	0.3511	0.3349	0.5129 (-0.0276)	0.3307 (+0.0057)	0.5180 (+0.0173)	0.4539 (-0.0015)
0.7823	4000	0.3508	0.3368	0.5462 (+0.0058)	0.3291 (+0.0040)	0.5310 (+0.0304)	0.4688 (+0.0134)
0.8019	4100	0.3439	0.3348	0.5409 (+0.0005)	0.3307 (+0.0056)	0.5312 (+0.0306)	0.4676 (+0.0122)
0.8214	4200	0.3487	0.3340	0.5324 (-0.0080)	0.3284 (+0.0034)	0.5191 (+0.0185)	0.4600 (+0.0046)
0.8410	4300	0.341	0.3351	0.5277 (-0.0127)	0.3276 (+0.0025)	0.5050 (+0.0044)	0.4535 (-0.0019)
0.8606	4400	0.3293	0.3344	0.5150 (-0.0254)	0.3355 (+0.0105)	0.5009 (+0.0002)	0.4505 (-0.0049)
0.8801	4500	0.3525	0.3346	0.5224 (-0.0180)	0.3239 (-0.0012)	0.5127 (+0.0121)	0.4530 (-0.0024)
0.8997	4600	0.3421	0.3331	0.5312 (-0.0092)	0.3376 (+0.0126)	0.5234 (+0.0228)	0.4641 (+0.0087)
0.9192	4700	0.3442	0.3336	0.5227 (-0.0177)	0.3330 (+0.0080)	0.5053 (+0.0047)	0.4537 (-0.0017)
0.9388	4800	0.3361	0.3328	0.5166 (-0.0238)	0.3378 (+0.0128)	0.5194 (+0.0187)	0.4579 (+0.0026)
0.9583	4900	0.3377	0.3335	0.5298 (-0.0106)	0.3300 (+0.0049)	0.5248 (+0.0241)	0.4615 (+0.0061)
0.9779	5000	0.3455	0.3332	0.5298 (-0.0106)	0.3305 (+0.0055)	0.5182 (+0.0176)	0.4595 (+0.0041)
0.9975	5100	0.3422	0.3333	0.5298 (-0.0106)	0.3303 (+0.0053)	0.5240 (+0.0234)	0.4614 (+0.0060)
-1	-1	-	-	0.5462 (+0.0058)	0.3291 (+0.0040)	0.5310 (+0.0304)	0.4688 (+0.0134)

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.13
Sentence Transformers: 5.0.0
Transformers: 4.51.0
PyTorch: 2.9.1+cu126
Accelerate: 1.8.1
Datasets: 3.6.0
Tokenizers: 0.21.4-dev.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",
}