SentenceTransformer based on AI-Growth-Lab/PatentSBERTa
This is a sentence-transformers model finetuned from AI-Growth-Lab/PatentSBERTa. It maps sentences & paragraphs to a 768-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: AI-Growth-Lab/PatentSBERTa
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 tokens
- Similarity Function: Cosine Similarity
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: MPNetModel
(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("sentence_transformers_model_id")
# Run inference
sentences = [
'The air compressor device according to claim 4 with two opposite side wings formed on the pole body and extending between the front end and the flange of the pole body along the axis defining two opposite wide faces and two opposite narrow faces with a area smaller than that of the wide faces with two recessions respectively formed in the side wings adjacent to the front end of the pole body with the engaging ring mounted in the recessions with the engaging ring including front and rear flanges spaced along the axis and an annular engaging groove formed between the front and rear flanges with a buffer ring mounted in the engaging groove of the engaging ring and being in contact with the inner wall of the second end of the axial hole.',
'Accordingly the sealing and inflating assembly includes an air compressing device including an improved tire repairing container for quickly coupling and attaching and securing to an outlet tube of the air compressor and for quickly disengaging from the air compressor and for allowing the tire sealing preparation to be effectively supplied to seal and inflate the inflatable objects and for easily and quickly and changeably attaching and securing to the outlet tube of the air compressor.',
'Thus since the path delay from interface 102 to unit 2 is known to be 1 and interface 102 is on the same unit as interface 101 and the path delay between interface 101 and unit 0 is known 1 the path delay between interface 001 and unit 2 can be entered as 112.The path delay table then becomes as shown in Table 3 immediately below.Table 3Dest Unit IdUnit 0 1 2 3 40 IF 001 0 1 2 255 2550 IF 002 0 255 2 1 21 IF 101 1 0 255 2 2551 IF 102 255 0 1 2 22 IF 201 2 1 0 255 2552 IF 202 255 255 0 2 12 IF 203 2 255 0 1 23 IF 301 1 2 255 0 2553 IF 302 255 2 1 0 23 IF 303 255 255 2 0 14 IF 401 255 2 1 2 04 IF 402 2 255 2 1 0',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Binary Classification
- Evaluated with
BinaryClassificationEvaluator
Metric | Value |
---|---|
cosine_accuracy | 1.0 |
cosine_accuracy_threshold | -0.0896 |
cosine_f1 | 1.0 |
cosine_f1_threshold | -0.0896 |
cosine_precision | 1.0 |
cosine_recall | 1.0 |
cosine_ap | 1.0 |
dot_accuracy | 1.0 |
dot_accuracy_threshold | -3.4987 |
dot_f1 | 1.0 |
dot_f1_threshold | -3.4987 |
dot_precision | 1.0 |
dot_recall | 1.0 |
dot_ap | 1.0 |
manhattan_accuracy | 1.0 |
manhattan_accuracy_threshold | 203.8069 |
manhattan_f1 | 1.0 |
manhattan_f1_threshold | 203.8069 |
manhattan_precision | 1.0 |
manhattan_recall | 1.0 |
manhattan_ap | 1.0 |
euclidean_accuracy | 1.0 |
euclidean_accuracy_threshold | 9.311 |
euclidean_f1 | 1.0 |
euclidean_f1_threshold | 9.311 |
euclidean_precision | 1.0 |
euclidean_recall | 1.0 |
euclidean_ap | 1.0 |
max_accuracy | 1.0 |
max_accuracy_threshold | 203.8069 |
max_f1 | 1.0 |
max_f1_threshold | 203.8069 |
max_precision | 1.0 |
max_recall | 1.0 |
max_ap | 1.0 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 2,545,432 training samples
- Columns:
text_a
,text_b
, andlabel
- Approximate statistics based on the first 1000 samples:
text_a text_b label type string string float details - min: 14 tokens
- mean: 75.3 tokens
- max: 512 tokens
- min: 8 tokens
- mean: 117.82 tokens
- max: 512 tokens
- min: 1.0
- mean: 1.0
- max: 1.0
- Samples:
text_a text_b label A method for displaying a photo the method being performed by a display apparatus and comprising transmitting identification information of the display apparatus to a first portable apparatus and a second portable apparatus connecting through an access point to the first portable apparatus which receives the identification information of the display apparatus connecting through the access point to the second portable apparatus which receives the identification information of the display apparatus in response to a first photo being received from the first portable apparatus displaying the first photo on a display of the display apparatus and in response to a second photo being received from the second portable apparatus displaying the second photo on the display on which the first photo is displayed wherein the displaying the second photo comprises displaying the first photo and the second photo all together in different sizes.
Further applications may also be loaded onto the electronic device 902 through for example the wireless network 904 an auxiliary IO device 922 USB port 924 shortrange communications subsystem 936 or any combination of these interfaces.Such applications are then able to be installed by a user in the RAM 920 or a nonvolatile store for execution by the microprocessor 916.
1.0
The method of any one of claims 8 to 9 wherein the high energydensity welding process uses both a laser and GMAW welding and wherein the GMAW welding is selected from the group comprising MIG welding and MAG welding.
Indicated at 27 is a wide platelike material employed as a base or starting material in the fabrication of thesquare tubular structure 22.As shown in Figs.3 to 5 this wide platelike material 27 is composed of a longitudinally extending flat thin plates 28 29 thick corner plates 30 and thick plates 31 which are joined together side to side by butt welding.More particularly for example these plate materials are prejoined by high energy density welding like laser welding capable of deep penetration.
1.0
An apparatus of a server in a communication system comprising a transceiver configured to communicating with at least one of a network node and an external server and a controller configured to receive identifier information of a user equipment UE obtain information on a second authentication key for authenticating the UE if an error is detected for a first authentication key corresponding to the identifier information and authenticate the UE based on the information on the second authentication key.
Figure 2 illustrates an exemplary block diagram of an apparatus providing an access point node functionality.For the sake of clarity the apparatus or a corresponding component is called herein as an access point node.The access point node 200 is a computing device comprising not only prior art means but also means for implementing access point node functionality described with an embodiment and it may comprise separate means for each separate function or means may be configured to perform two or more functions and even to combine functions of different embodiments.These means may be implemented by various techniques.For example the means may be implemented in hardware one or more apparatuses firmware one or more apparatuses software one or more modules or combinations thereof.For afirmware or software implementation can be through unitsmodules e.g.procedures functions and so on that perform the functions described herein.
1.0
- Loss:
MultipleNegativesRankingLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Evaluation Dataset
Unnamed Dataset
- Size: 133,971 evaluation samples
- Columns:
text_a
,text_b
, andlabel
- Approximate statistics based on the first 1000 samples:
text_a text_b label type string string float details - min: 14 tokens
- mean: 73.52 tokens
- max: 512 tokens
- min: 7 tokens
- mean: 113.72 tokens
- max: 512 tokens
- min: 1.0
- mean: 1.0
- max: 1.0
- Samples:
text_a text_b label The display apparatus according to claim 3 wherein the curvature of the display panel in the second state is gradually reduced with increasing distance from the center portion or decreasing distance to the edge portion in the second direction.
Referring to FIG.5 in the shape deformingmaintaining unit 120 an actuator 123 is arranged between a first electrode 121 and a second electrode 122.The first electrode 121 is divided into a plurality of parts that are arranged on a surface of the actuator 123 in parallel and are set apart by a distance for example a constant distance whereas the second electrode 122 is arranged completely on the other surface of the actuator 123.When a circuit unit 124 applies voltages for example predetermined voltages V1 V2 and V3 to each of the divided parts of the first electrode 121 and a voltage for example a predetermined voltage to the second electrode 122 under control of a control unit see FIG.8 the actuator 123 changes shape in response to the applied voltages and thus the curvature of the flexible display panel 110 arranged on a surface of the shape deformingmaintaining unit 120 may be controlled.
1.0
The organic light emitting display device 10 of claim 4 wherein the shared node Ns has ashape ashape ashape or ashape.
The first transistor T1 is a transistor that is controlled by the sensing signal SENSE supplied by the first gate line GL1 is connected between the reference voltage line RVL supplying the reference voltage Vref or a connection pattern CP connected to the reference voltage line and the first node N1 of the driving transistor DT and is concerned in a sensing mode and is also referred to as a sensor transistor.
1.0
Measuring machine 10 according to claim 1 or 2 characterized in thatthe generated laser beam 15 with its oval 16 or linelike 16 crosssection is adjusted with the length of its crosssection 16 16 orthogonal to the triangulation baseline b.
According to the present exemplary embodiment a oneshot active type range sensor is used.Such a range sensor irradiates the object with multislit lines to which color identifications ID of different wavelength are attached.The range sensor then captures reflected light using the camera and measures the range employing triangulation.
1.0
- Loss:
MultipleNegativesRankingLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 32per_device_eval_batch_size
: 32learning_rate
: 2e-05weight_decay
: 0.01num_train_epochs
: 1warmup_ratio
: 0.1load_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
: Nonelearning_rate
: 2e-05weight_decay
: 0.01adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 1max_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
: Falsefp16_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
: Falsebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: proportional
Training Logs
Click to expand
Epoch | Step | Training Loss | loss | max_ap |
---|---|---|---|---|
0.0013 | 100 | 2.2516 | - | - |
0.0025 | 200 | 2.0871 | - | - |
0.0038 | 300 | 1.8806 | - | - |
0.0050 | 400 | 1.6918 | - | - |
0.0063 | 500 | 1.6214 | - | - |
0.0075 | 600 | 1.6796 | - | - |
0.0088 | 700 | 1.6149 | - | - |
0.0101 | 800 | 1.6178 | - | - |
0.0113 | 900 | 1.5865 | - | - |
0.0126 | 1000 | 1.4899 | - | - |
0.0138 | 1100 | 1.4679 | - | - |
0.0151 | 1200 | 1.4484 | - | - |
0.0163 | 1300 | 1.4421 | - | - |
0.0176 | 1400 | 1.4058 | - | - |
0.0189 | 1500 | 1.4092 | - | - |
0.0201 | 1600 | 1.3989 | - | - |
0.0214 | 1700 | 1.3503 | - | - |
0.0226 | 1800 | 1.3746 | - | - |
0.0239 | 1900 | 1.3267 | - | - |
0.0251 | 2000 | 1.2934 | - | - |
0.0264 | 2100 | 1.3097 | - | - |
0.0277 | 2200 | 1.2584 | - | - |
0.0289 | 2300 | 1.2713 | - | - |
0.0302 | 2400 | 1.2769 | - | - |
0.0314 | 2500 | 1.2311 | - | - |
0.0327 | 2600 | 1.2591 | - | - |
0.0339 | 2700 | 1.2431 | - | - |
0.0352 | 2800 | 1.2325 | - | - |
0.0365 | 2900 | 1.2377 | - | - |
0.0377 | 3000 | 1.1868 | - | - |
0.0390 | 3100 | 1.1844 | - | - |
0.0402 | 3200 | 1.1939 | - | - |
0.0415 | 3300 | 1.2116 | - | - |
0.0427 | 3400 | 1.1737 | - | - |
0.0440 | 3500 | 1.1517 | - | - |
0.0453 | 3600 | 1.1291 | - | - |
0.0465 | 3700 | 1.1196 | - | - |
0.0478 | 3800 | 1.1149 | - | - |
0.0490 | 3900 | 1.1194 | - | - |
0.0503 | 4000 | 1.0612 | - | - |
0.0515 | 4100 | 1.0918 | - | - |
0.0528 | 4200 | 1.0629 | - | - |
0.0541 | 4300 | 1.0455 | - | - |
0.0553 | 4400 | 1.0625 | - | - |
0.0566 | 4500 | 1.0586 | - | - |
0.0578 | 4600 | 1.0057 | - | - |
0.0591 | 4700 | 1.0269 | - | - |
0.0603 | 4800 | 1.0543 | - | - |
0.0616 | 4900 | 1.0117 | - | - |
0.0629 | 5000 | 1.0412 | - | - |
0.0641 | 5100 | 0.9773 | - | - |
0.0654 | 5200 | 0.9889 | - | - |
0.0666 | 5300 | 0.9499 | - | - |
0.0679 | 5400 | 0.9528 | - | - |
0.0691 | 5500 | 0.9468 | - | - |
0.0704 | 5600 | 0.9202 | - | - |
0.0717 | 5700 | 0.9896 | - | - |
0.0729 | 5800 | 0.9795 | - | - |
0.0742 | 5900 | 0.9582 | - | - |
0.0754 | 6000 | 0.9254 | - | - |
0.0767 | 6100 | 0.9476 | - | - |
0.0779 | 6200 | 0.9157 | - | - |
0.0792 | 6300 | 0.9221 | - | - |
0.0805 | 6400 | 0.8769 | - | - |
0.0817 | 6500 | 0.8659 | - | - |
0.0830 | 6600 | 0.9024 | - | - |
0.0842 | 6700 | 0.8898 | - | - |
0.0855 | 6800 | 0.8952 | - | - |
0.0867 | 6900 | 0.8773 | - | - |
0.0880 | 7000 | 0.8676 | - | - |
0.0893 | 7100 | 0.8263 | - | - |
0.0905 | 7200 | 0.8782 | - | - |
0.0918 | 7300 | 0.8295 | - | - |
0.0930 | 7400 | 0.873 | - | - |
0.0943 | 7500 | 0.8292 | - | - |
0.0955 | 7600 | 0.8301 | - | - |
0.0968 | 7700 | 0.858 | - | - |
0.0981 | 7800 | 0.8326 | - | - |
0.0993 | 7900 | 0.8334 | - | - |
0.1006 | 8000 | 0.8078 | - | - |
0.1018 | 8100 | 0.8069 | - | - |
0.1031 | 8200 | 0.8206 | - | - |
0.1043 | 8300 | 0.7977 | - | - |
0.1056 | 8400 | 0.7886 | - | - |
0.1069 | 8500 | 0.8012 | - | - |
0.1081 | 8600 | 0.7752 | - | - |
0.1094 | 8700 | 0.7324 | - | - |
0.1106 | 8800 | 0.7507 | - | - |
0.1119 | 8900 | 0.7568 | - | - |
0.1131 | 9000 | 0.7436 | - | - |
0.1144 | 9100 | 0.761 | - | - |
0.1157 | 9200 | 0.7374 | - | - |
0.1169 | 9300 | 0.7127 | - | - |
0.1182 | 9400 | 0.7132 | - | - |
0.1194 | 9500 | 0.7713 | - | - |
0.1207 | 9600 | 0.7215 | - | - |
0.1219 | 9700 | 0.7415 | - | - |
0.1232 | 9800 | 0.716 | - | - |
0.1245 | 9900 | 0.7365 | - | - |
0.1257 | 10000 | 0.7232 | 0.6556 | 1.0000 |
0.1270 | 10100 | 0.7121 | - | - |
0.1282 | 10200 | 0.7019 | - | - |
0.1295 | 10300 | 0.7208 | - | - |
0.1307 | 10400 | 0.6817 | - | - |
0.1320 | 10500 | 0.6886 | - | - |
0.1333 | 10600 | 0.7182 | - | - |
0.1345 | 10700 | 0.698 | - | - |
0.1358 | 10800 | 0.7276 | - | - |
0.1370 | 10900 | 0.6836 | - | - |
0.1383 | 11000 | 0.6855 | - | - |
0.1395 | 11100 | 0.6702 | - | - |
0.1408 | 11200 | 0.6851 | - | - |
0.1421 | 11300 | 0.6107 | - | - |
0.1433 | 11400 | 0.6615 | - | - |
0.1446 | 11500 | 0.6703 | - | - |
0.1458 | 11600 | 0.6318 | - | - |
0.1471 | 11700 | 0.6277 | - | - |
0.1483 | 11800 | 0.6321 | - | - |
0.1496 | 11900 | 0.6411 | - | - |
0.1509 | 12000 | 0.6231 | - | - |
0.1521 | 12100 | 0.6163 | - | - |
0.1534 | 12200 | 0.6087 | - | - |
0.1546 | 12300 | 0.6068 | - | - |
0.1559 | 12400 | 0.6223 | - | - |
0.1571 | 12500 | 0.6165 | - | - |
0.1584 | 12600 | 0.6345 | - | - |
0.1597 | 12700 | 0.6153 | - | - |
0.1609 | 12800 | 0.5805 | - | - |
0.1622 | 12900 | 0.5997 | - | - |
0.1634 | 13000 | 0.6038 | - | - |
0.1647 | 13100 | 0.6061 | - | - |
0.1659 | 13200 | 0.5828 | - | - |
0.1672 | 13300 | 0.5964 | - | - |
0.1685 | 13400 | 0.5842 | - | - |
0.1697 | 13500 | 0.6142 | - | - |
0.1710 | 13600 | 0.5633 | - | - |
0.1722 | 13700 | 0.5828 | - | - |
0.1735 | 13800 | 0.5728 | - | - |
0.1747 | 13900 | 0.5335 | - | - |
0.1760 | 14000 | 0.5474 | - | - |
0.1773 | 14100 | 0.5593 | - | - |
0.1785 | 14200 | 0.5765 | - | - |
0.1798 | 14300 | 0.565 | - | - |
0.1810 | 14400 | 0.5969 | - | - |
0.1823 | 14500 | 0.5569 | - | - |
0.1835 | 14600 | 0.5344 | - | - |
0.1848 | 14700 | 0.5891 | - | - |
0.1861 | 14800 | 0.5743 | - | - |
0.1873 | 14900 | 0.5224 | - | - |
0.1886 | 15000 | 0.5168 | - | - |
0.1898 | 15100 | 0.5408 | - | - |
0.1911 | 15200 | 0.5385 | - | - |
0.1923 | 15300 | 0.5284 | - | - |
0.1936 | 15400 | 0.5415 | - | - |
0.1949 | 15500 | 0.5191 | - | - |
0.1961 | 15600 | 0.4924 | - | - |
0.1974 | 15700 | 0.527 | - | - |
0.1986 | 15800 | 0.5164 | - | - |
0.1999 | 15900 | 0.5011 | - | - |
0.2011 | 16000 | 0.5328 | - | - |
0.2024 | 16100 | 0.535 | - | - |
0.2037 | 16200 | 0.5348 | - | - |
0.2049 | 16300 | 0.499 | - | - |
0.2062 | 16400 | 0.5208 | - | - |
0.2074 | 16500 | 0.4906 | - | - |
0.2087 | 16600 | 0.4906 | - | - |
0.2099 | 16700 | 0.473 | - | - |
0.2112 | 16800 | 0.4769 | - | - |
0.2125 | 16900 | 0.5211 | - | - |
0.2137 | 17000 | 0.5148 | - | - |
0.2150 | 17100 | 0.5263 | - | - |
0.2162 | 17200 | 0.5052 | - | - |
0.2175 | 17300 | 0.4957 | - | - |
0.2187 | 17400 | 0.4649 | - | - |
0.2200 | 17500 | 0.4991 | - | - |
0.2213 | 17600 | 0.4833 | - | - |
0.2225 | 17700 | 0.4675 | - | - |
0.2238 | 17800 | 0.4643 | - | - |
0.2250 | 17900 | 0.4769 | - | - |
0.2263 | 18000 | 0.4652 | - | - |
0.2275 | 18100 | 0.4372 | - | - |
0.2288 | 18200 | 0.4966 | - | - |
0.2301 | 18300 | 0.4431 | - | - |
0.2313 | 18400 | 0.4685 | - | - |
0.2326 | 18500 | 0.4708 | - | - |
0.2338 | 18600 | 0.4846 | - | - |
0.2351 | 18700 | 0.4399 | - | - |
0.2363 | 18800 | 0.4382 | - | - |
0.2376 | 18900 | 0.4746 | - | - |
0.2389 | 19000 | 0.4654 | - | - |
0.2401 | 19100 | 0.4834 | - | - |
0.2414 | 19200 | 0.4456 | - | - |
0.2426 | 19300 | 0.4778 | - | - |
0.2439 | 19400 | 0.4367 | - | - |
0.2451 | 19500 | 0.4287 | - | - |
0.2464 | 19600 | 0.4494 | - | - |
0.2477 | 19700 | 0.4381 | - | - |
0.2489 | 19800 | 0.4593 | - | - |
0.2502 | 19900 | 0.4304 | - | - |
0.2514 | 20000 | 0.4415 | 0.3788 | 1.0000 |
0.2527 | 20100 | 0.4636 | - | - |
0.2539 | 20200 | 0.4549 | - | - |
0.2552 | 20300 | 0.4422 | - | - |
0.2565 | 20400 | 0.4791 | - | - |
0.2577 | 20500 | 0.4369 | - | - |
0.2590 | 20600 | 0.4386 | - | - |
0.2602 | 20700 | 0.4481 | - | - |
0.2615 | 20800 | 0.4132 | - | - |
0.2627 | 20900 | 0.4408 | - | - |
0.2640 | 21000 | 0.4138 | - | - |
0.2653 | 21100 | 0.415 | - | - |
0.2665 | 21200 | 0.4444 | - | - |
0.2678 | 21300 | 0.3727 | - | - |
0.2690 | 21400 | 0.4074 | - | - |
0.2703 | 21500 | 0.4292 | - | - |
0.2715 | 21600 | 0.4366 | - | - |
0.2728 | 21700 | 0.4325 | - | - |
0.2741 | 21800 | 0.448 | - | - |
0.2753 | 21900 | 0.4308 | - | - |
0.2766 | 22000 | 0.392 | - | - |
0.2778 | 22100 | 0.3922 | - | - |
0.2791 | 22200 | 0.44 | - | - |
0.2803 | 22300 | 0.4091 | - | - |
0.2816 | 22400 | 0.4219 | - | - |
0.2829 | 22500 | 0.4235 | - | - |
0.2841 | 22600 | 0.3979 | - | - |
0.2854 | 22700 | 0.4073 | - | - |
0.2866 | 22800 | 0.3857 | - | - |
0.2879 | 22900 | 0.4143 | - | - |
0.2891 | 23000 | 0.3682 | - | - |
0.2904 | 23100 | 0.4224 | - | - |
0.2917 | 23200 | 0.3771 | - | - |
0.2929 | 23300 | 0.3845 | - | - |
0.2942 | 23400 | 0.4116 | - | - |
0.2954 | 23500 | 0.3954 | - | - |
0.2967 | 23600 | 0.4193 | - | - |
0.2979 | 23700 | 0.3837 | - | - |
0.2992 | 23800 | 0.3794 | - | - |
0.3005 | 23900 | 0.3788 | - | - |
0.3017 | 24000 | 0.372 | - | - |
0.3030 | 24100 | 0.4016 | - | - |
0.3042 | 24200 | 0.3936 | - | - |
0.3055 | 24300 | 0.3957 | - | - |
0.3067 | 24400 | 0.3742 | - | - |
0.3080 | 24500 | 0.4021 | - | - |
0.3093 | 24600 | 0.3876 | - | - |
0.3105 | 24700 | 0.3903 | - | - |
0.3118 | 24800 | 0.3402 | - | - |
0.3130 | 24900 | 0.3606 | - | - |
0.3143 | 25000 | 0.3578 | - | - |
0.3155 | 25100 | 0.3874 | - | - |
0.3168 | 25200 | 0.3545 | - | - |
0.3181 | 25300 | 0.3586 | - | - |
0.3193 | 25400 | 0.3566 | - | - |
0.3206 | 25500 | 0.3552 | - | - |
0.3218 | 25600 | 0.3576 | - | - |
0.3231 | 25700 | 0.3764 | - | - |
0.3243 | 25800 | 0.3779 | - | - |
0.3256 | 25900 | 0.3711 | - | - |
0.3269 | 26000 | 0.3449 | - | - |
0.3281 | 26100 | 0.3715 | - | - |
0.3294 | 26200 | 0.3683 | - | - |
0.3306 | 26300 | 0.3712 | - | - |
0.3319 | 26400 | 0.3393 | - | - |
0.3331 | 26500 | 0.3236 | - | - |
0.3344 | 26600 | 0.36 | - | - |
0.3357 | 26700 | 0.3531 | - | - |
0.3369 | 26800 | 0.3711 | - | - |
0.3382 | 26900 | 0.3483 | - | - |
0.3394 | 27000 | 0.3541 | - | - |
0.3407 | 27100 | 0.3455 | - | - |
0.3419 | 27200 | 0.3782 | - | - |
0.3432 | 27300 | 0.3228 | - | - |
0.3445 | 27400 | 0.3561 | - | - |
0.3457 | 27500 | 0.3439 | - | - |
0.3470 | 27600 | 0.3356 | - | - |
0.3482 | 27700 | 0.3348 | - | - |
0.3495 | 27800 | 0.3256 | - | - |
0.3507 | 27900 | 0.3453 | - | - |
0.3520 | 28000 | 0.3563 | - | - |
0.3533 | 28100 | 0.354 | - | - |
0.3545 | 28200 | 0.3328 | - | - |
0.3558 | 28300 | 0.3278 | - | - |
0.3570 | 28400 | 0.3693 | - | - |
0.3583 | 28500 | 0.3379 | - | - |
0.3595 | 28600 | 0.3123 | - | - |
0.3608 | 28700 | 0.3599 | - | - |
0.3621 | 28800 | 0.3312 | - | - |
0.3633 | 28900 | 0.3335 | - | - |
0.3646 | 29000 | 0.3189 | - | - |
0.3658 | 29100 | 0.3326 | - | - |
0.3671 | 29200 | 0.3278 | - | - |
0.3683 | 29300 | 0.3408 | - | - |
0.3696 | 29400 | 0.3216 | - | - |
0.3709 | 29500 | 0.3233 | - | - |
0.3721 | 29600 | 0.3192 | - | - |
0.3734 | 29700 | 0.3459 | - | - |
0.3746 | 29800 | 0.3341 | - | - |
0.3759 | 29900 | 0.319 | - | - |
0.3771 | 30000 | 0.3091 | 0.2659 | 1.0000 |
0.3784 | 30100 | 0.3207 | - | - |
0.3797 | 30200 | 0.3114 | - | - |
0.3809 | 30300 | 0.3074 | - | - |
0.3822 | 30400 | 0.3252 | - | - |
0.3834 | 30500 | 0.3058 | - | - |
0.3847 | 30600 | 0.3244 | - | - |
0.3859 | 30700 | 0.3343 | - | - |
0.3872 | 30800 | 0.3279 | - | - |
0.3885 | 30900 | 0.2983 | - | - |
0.3897 | 31000 | 0.3198 | - | - |
0.3910 | 31100 | 0.3102 | - | - |
0.3922 | 31200 | 0.3407 | - | - |
0.3935 | 31300 | 0.323 | - | - |
0.3947 | 31400 | 0.2921 | - | - |
0.3960 | 31500 | 0.2928 | - | - |
0.3973 | 31600 | 0.309 | - | - |
0.3985 | 31700 | 0.2867 | - | - |
0.3998 | 31800 | 0.2973 | - | - |
0.4010 | 31900 | 0.2889 | - | - |
0.4023 | 32000 | 0.3163 | - | - |
0.4035 | 32100 | 0.3139 | - | - |
0.4048 | 32200 | 0.3081 | - | - |
0.4061 | 32300 | 0.3155 | - | - |
0.4073 | 32400 | 0.2674 | - | - |
0.4086 | 32500 | 0.2979 | - | - |
0.4098 | 32600 | 0.2653 | - | - |
0.4111 | 32700 | 0.2758 | - | - |
0.4123 | 32800 | 0.3062 | - | - |
0.4136 | 32900 | 0.2832 | - | - |
0.4149 | 33000 | 0.2866 | - | - |
0.4161 | 33100 | 0.2925 | - | - |
0.4174 | 33200 | 0.2784 | - | - |
0.4186 | 33300 | 0.2859 | - | - |
0.4199 | 33400 | 0.3033 | - | - |
0.4211 | 33500 | 0.2755 | - | - |
0.4224 | 33600 | 0.2803 | - | - |
0.4237 | 33700 | 0.2968 | - | - |
0.4249 | 33800 | 0.2972 | - | - |
0.4262 | 33900 | 0.2774 | - | - |
0.4274 | 34000 | 0.2859 | - | - |
0.4287 | 34100 | 0.2884 | - | - |
0.4299 | 34200 | 0.2657 | - | - |
0.4312 | 34300 | 0.2839 | - | - |
0.4325 | 34400 | 0.2843 | - | - |
0.4337 | 34500 | 0.3019 | - | - |
0.4350 | 34600 | 0.2755 | - | - |
0.4362 | 34700 | 0.3062 | - | - |
0.4375 | 34800 | 0.2848 | - | - |
0.4387 | 34900 | 0.296 | - | - |
0.4400 | 35000 | 0.2859 | - | - |
0.4413 | 35100 | 0.2678 | - | - |
0.4425 | 35200 | 0.2723 | - | - |
0.4438 | 35300 | 0.2761 | - | - |
0.4450 | 35400 | 0.3142 | - | - |
0.4463 | 35500 | 0.2643 | - | - |
0.4475 | 35600 | 0.3025 | - | - |
0.4488 | 35700 | 0.254 | - | - |
0.4501 | 35800 | 0.2831 | - | - |
0.4513 | 35900 | 0.274 | - | - |
0.4526 | 36000 | 0.2979 | - | - |
0.4538 | 36100 | 0.292 | - | - |
0.4551 | 36200 | 0.2674 | - | - |
0.4563 | 36300 | 0.274 | - | - |
0.4576 | 36400 | 0.2781 | - | - |
0.4589 | 36500 | 0.279 | - | - |
0.4601 | 36600 | 0.2781 | - | - |
0.4614 | 36700 | 0.2512 | - | - |
0.4626 | 36800 | 0.2775 | - | - |
0.4639 | 36900 | 0.2726 | - | - |
0.4651 | 37000 | 0.2495 | - | - |
0.4664 | 37100 | 0.2659 | - | - |
0.4677 | 37200 | 0.2885 | - | - |
0.4689 | 37300 | 0.2613 | - | - |
0.4702 | 37400 | 0.2508 | - | - |
0.4714 | 37500 | 0.2451 | - | - |
0.4727 | 37600 | 0.2644 | - | - |
0.4739 | 37700 | 0.2713 | - | - |
0.4752 | 37800 | 0.2697 | - | - |
0.4765 | 37900 | 0.2661 | - | - |
0.4777 | 38000 | 0.2538 | - | - |
0.4790 | 38100 | 0.2543 | - | - |
0.4802 | 38200 | 0.2545 | - | - |
0.4815 | 38300 | 0.2507 | - | - |
0.4827 | 38400 | 0.2698 | - | - |
0.4840 | 38500 | 0.2475 | - | - |
0.4853 | 38600 | 0.2579 | - | - |
0.4865 | 38700 | 0.2483 | - | - |
0.4878 | 38800 | 0.2387 | - | - |
0.4890 | 38900 | 0.2689 | - | - |
0.4903 | 39000 | 0.2544 | - | - |
0.4915 | 39100 | 0.258 | - | - |
0.4928 | 39200 | 0.2519 | - | - |
0.4941 | 39300 | 0.2493 | - | - |
0.4953 | 39400 | 0.2465 | - | - |
0.4966 | 39500 | 0.2424 | - | - |
0.4978 | 39600 | 0.2567 | - | - |
0.4991 | 39700 | 0.2399 | - | - |
0.5003 | 39800 | 0.2291 | - | - |
0.5016 | 39900 | 0.2715 | - | - |
0.5029 | 40000 | 0.239 | 0.1931 | 1.0000 |
0.5041 | 40100 | 0.2294 | - | - |
0.5054 | 40200 | 0.2457 | - | - |
0.5066 | 40300 | 0.2481 | - | - |
0.5079 | 40400 | 0.2348 | - | - |
0.5091 | 40500 | 0.2378 | - | - |
0.5104 | 40600 | 0.2486 | - | - |
0.5117 | 40700 | 0.2374 | - | - |
0.5129 | 40800 | 0.2498 | - | - |
0.5142 | 40900 | 0.222 | - | - |
0.5154 | 41000 | 0.2321 | - | - |
0.5167 | 41100 | 0.2435 | - | - |
0.5179 | 41200 | 0.2383 | - | - |
0.5192 | 41300 | 0.2484 | - | - |
0.5205 | 41400 | 0.2437 | - | - |
0.5217 | 41500 | 0.2326 | - | - |
0.5230 | 41600 | 0.2635 | - | - |
0.5242 | 41700 | 0.2524 | - | - |
0.5255 | 41800 | 0.239 | - | - |
0.5267 | 41900 | 0.2532 | - | - |
0.5280 | 42000 | 0.2279 | - | - |
0.5293 | 42100 | 0.2336 | - | - |
0.5305 | 42200 | 0.2388 | - | - |
0.5318 | 42300 | 0.241 | - | - |
0.5330 | 42400 | 0.2164 | - | - |
0.5343 | 42500 | 0.2369 | - | - |
0.5355 | 42600 | 0.2352 | - | - |
0.5368 | 42700 | 0.2678 | - | - |
0.5381 | 42800 | 0.221 | - | - |
0.5393 | 42900 | 0.2312 | - | - |
0.5406 | 43000 | 0.2272 | - | - |
0.5418 | 43100 | 0.2306 | - | - |
0.5431 | 43200 | 0.2334 | - | - |
0.5443 | 43300 | 0.2287 | - | - |
0.5456 | 43400 | 0.2258 | - | - |
0.5469 | 43500 | 0.2179 | - | - |
0.5481 | 43600 | 0.1964 | - | - |
0.5494 | 43700 | 0.2178 | - | - |
0.5506 | 43800 | 0.2339 | - | - |
0.5519 | 43900 | 0.2349 | - | - |
0.5531 | 44000 | 0.2141 | - | - |
0.5544 | 44100 | 0.2324 | - | - |
0.5557 | 44200 | 0.2422 | - | - |
0.5569 | 44300 | 0.2281 | - | - |
0.5582 | 44400 | 0.2359 | - | - |
0.5594 | 44500 | 0.1989 | - | - |
0.5607 | 44600 | 0.2168 | - | - |
0.5619 | 44700 | 0.2332 | - | - |
0.5632 | 44800 | 0.2121 | - | - |
0.5645 | 44900 | 0.2216 | - | - |
0.5657 | 45000 | 0.2059 | - | - |
0.5670 | 45100 | 0.2392 | - | - |
0.5682 | 45200 | 0.2358 | - | - |
0.5695 | 45300 | 0.2438 | - | - |
0.5707 | 45400 | 0.2115 | - | - |
0.5720 | 45500 | 0.2231 | - | - |
0.5733 | 45600 | 0.2294 | - | - |
0.5745 | 45700 | 0.2172 | - | - |
0.5758 | 45800 | 0.2154 | - | - |
0.5770 | 45900 | 0.233 | - | - |
0.5783 | 46000 | 0.2369 | - | - |
0.5795 | 46100 | 0.2186 | - | - |
0.5808 | 46200 | 0.2125 | - | - |
0.5821 | 46300 | 0.2098 | - | - |
0.5833 | 46400 | 0.2263 | - | - |
0.5846 | 46500 | 0.2264 | - | - |
0.5858 | 46600 | 0.2132 | - | - |
0.5871 | 46700 | 0.2283 | - | - |
0.5883 | 46800 | 0.2115 | - | - |
0.5896 | 46900 | 0.202 | - | - |
0.5909 | 47000 | 0.1838 | - | - |
0.5921 | 47100 | 0.21 | - | - |
0.5934 | 47200 | 0.2137 | - | - |
0.5946 | 47300 | 0.1944 | - | - |
0.5959 | 47400 | 0.2158 | - | - |
0.5971 | 47500 | 0.2004 | - | - |
0.5984 | 47600 | 0.1946 | - | - |
0.5997 | 47700 | 0.1893 | - | - |
0.6009 | 47800 | 0.191 | - | - |
0.6022 | 47900 | 0.2018 | - | - |
0.6034 | 48000 | 0.1847 | - | - |
0.6047 | 48100 | 0.1804 | - | - |
0.6059 | 48200 | 0.2164 | - | - |
0.6072 | 48300 | 0.1923 | - | - |
0.6085 | 48400 | 0.2005 | - | - |
0.6097 | 48500 | 0.2005 | - | - |
0.6110 | 48600 | 0.1935 | - | - |
0.6122 | 48700 | 0.1834 | - | - |
0.6135 | 48800 | 0.2091 | - | - |
0.6147 | 48900 | 0.1928 | - | - |
0.6160 | 49000 | 0.1989 | - | - |
0.6173 | 49100 | 0.1953 | - | - |
0.6185 | 49200 | 0.2105 | - | - |
0.6198 | 49300 | 0.2004 | - | - |
0.6210 | 49400 | 0.1817 | - | - |
0.6223 | 49500 | 0.2062 | - | - |
0.6235 | 49600 | 0.216 | - | - |
0.6248 | 49700 | 0.209 | - | - |
0.6261 | 49800 | 0.1902 | - | - |
0.6273 | 49900 | 0.212 | - | - |
0.6286 | 50000 | 0.2267 | 0.1532 | 1.0 |
0.6298 | 50100 | 0.2134 | - | - |
0.6311 | 50200 | 0.2255 | - | - |
0.6323 | 50300 | 0.2125 | - | - |
0.6336 | 50400 | 0.2113 | - | - |
0.6349 | 50500 | 0.2082 | - | - |
0.6361 | 50600 | 0.1949 | - | - |
0.6374 | 50700 | 0.2024 | - | - |
0.6386 | 50800 | 0.2228 | - | - |
0.6399 | 50900 | 0.2057 | - | - |
0.6411 | 51000 | 0.1882 | - | - |
0.6424 | 51100 | 0.2015 | - | - |
0.6437 | 51200 | 0.207 | - | - |
0.6449 | 51300 | 0.1934 | - | - |
0.6462 | 51400 | 0.1743 | - | - |
0.6474 | 51500 | 0.2046 | - | - |
0.6487 | 51600 | 0.2059 | - | - |
0.6499 | 51700 | 0.1878 | - | - |
0.6512 | 51800 | 0.2189 | - | - |
0.6525 | 51900 | 0.1949 | - | - |
0.6537 | 52000 | 0.1982 | - | - |
0.6550 | 52100 | 0.1746 | - | - |
0.6562 | 52200 | 0.1887 | - | - |
0.6575 | 52300 | 0.1856 | - | - |
0.6587 | 52400 | 0.1955 | - | - |
0.6600 | 52500 | 0.1937 | - | - |
0.6613 | 52600 | 0.1912 | - | - |
0.6625 | 52700 | 0.1987 | - | - |
0.6638 | 52800 | 0.201 | - | - |
0.6650 | 52900 | 0.1955 | - | - |
0.6663 | 53000 | 0.1995 | - | - |
0.6675 | 53100 | 0.1829 | - | - |
0.6688 | 53200 | 0.1836 | - | - |
0.6701 | 53300 | 0.1972 | - | - |
0.6713 | 53400 | 0.2064 | - | - |
0.6726 | 53500 | 0.1966 | - | - |
0.6738 | 53600 | 0.191 | - | - |
0.6751 | 53700 | 0.1834 | - | - |
0.6763 | 53800 | 0.1976 | - | - |
0.6776 | 53900 | 0.2128 | - | - |
0.6789 | 54000 | 0.1948 | - | - |
0.6801 | 54100 | 0.1801 | - | - |
0.6814 | 54200 | 0.1904 | - | - |
0.6826 | 54300 | 0.1979 | - | - |
0.6839 | 54400 | 0.1916 | - | - |
0.6851 | 54500 | 0.1963 | - | - |
0.6864 | 54600 | 0.1809 | - | - |
0.6877 | 54700 | 0.2013 | - | - |
0.6889 | 54800 | 0.1796 | - | - |
0.6902 | 54900 | 0.1855 | - | - |
0.6914 | 55000 | 0.1742 | - | - |
0.6927 | 55100 | 0.1937 | - | - |
0.6939 | 55200 | 0.1819 | - | - |
0.6952 | 55300 | 0.1909 | - | - |
0.6965 | 55400 | 0.1737 | - | - |
0.6977 | 55500 | 0.1882 | - | - |
0.6990 | 55600 | 0.1733 | - | - |
0.7002 | 55700 | 0.1672 | - | - |
0.7015 | 55800 | 0.1879 | - | - |
0.7027 | 55900 | 0.1705 | - | - |
0.7040 | 56000 | 0.1928 | - | - |
0.7053 | 56100 | 0.1834 | - | - |
0.7065 | 56200 | 0.2112 | - | - |
0.7078 | 56300 | 0.1718 | - | - |
0.7090 | 56400 | 0.1767 | - | - |
0.7103 | 56500 | 0.1932 | - | - |
0.7115 | 56600 | 0.1767 | - | - |
0.7128 | 56700 | 0.1769 | - | - |
0.7141 | 56800 | 0.1656 | - | - |
0.7153 | 56900 | 0.1795 | - | - |
0.7166 | 57000 | 0.1869 | - | - |
0.7178 | 57100 | 0.1868 | - | - |
0.7191 | 57200 | 0.1754 | - | - |
0.7203 | 57300 | 0.1986 | - | - |
0.7216 | 57400 | 0.185 | - | - |
0.7229 | 57500 | 0.1752 | - | - |
0.7241 | 57600 | 0.174 | - | - |
0.7254 | 57700 | 0.1758 | - | - |
0.7266 | 57800 | 0.1751 | - | - |
0.7279 | 57900 | 0.1766 | - | - |
0.7291 | 58000 | 0.1659 | - | - |
0.7304 | 58100 | 0.1752 | - | - |
0.7317 | 58200 | 0.1786 | - | - |
0.7329 | 58300 | 0.1798 | - | - |
0.7342 | 58400 | 0.187 | - | - |
0.7354 | 58500 | 0.1894 | - | - |
0.7367 | 58600 | 0.1825 | - | - |
0.7379 | 58700 | 0.1734 | - | - |
0.7392 | 58800 | 0.1736 | - | - |
0.7405 | 58900 | 0.1721 | - | - |
0.7417 | 59000 | 0.1789 | - | - |
0.7430 | 59100 | 0.1737 | - | - |
0.7442 | 59200 | 0.1795 | - | - |
0.7455 | 59300 | 0.1723 | - | - |
0.7467 | 59400 | 0.1608 | - | - |
0.7480 | 59500 | 0.1637 | - | - |
0.7493 | 59600 | 0.1724 | - | - |
0.7505 | 59700 | 0.1684 | - | - |
0.7518 | 59800 | 0.1708 | - | - |
0.7530 | 59900 | 0.1578 | - | - |
0.7543 | 60000 | 0.1761 | 0.1251 | 1.0 |
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.0+cu118
- Accelerate: 0.31.0
- Datasets: 2.20.0
- Tokenizers: 0.19.1
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",
}
MultipleNegativesRankingLoss
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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Evaluation results
- Cosine Accuracy on Unknownself-reported1.000
- Cosine Accuracy Threshold on Unknownself-reported-0.090
- Cosine F1 on Unknownself-reported1.000
- Cosine F1 Threshold on Unknownself-reported-0.090
- Cosine Precision on Unknownself-reported1.000
- Cosine Recall on Unknownself-reported1.000
- Cosine Ap on Unknownself-reported1.000
- Dot Accuracy on Unknownself-reported1.000
- Dot Accuracy Threshold on Unknownself-reported-3.499
- Dot F1 on Unknownself-reported1.000