metadata

base_model: google/electra-large-discriminator
datasets:
  - PiC/phrase_similarity
language:
  - en
library_name: sentence-transformers
metrics:
  - cosine_accuracy
  - cosine_accuracy_threshold
  - cosine_f1
  - cosine_f1_threshold
  - cosine_precision
  - cosine_recall
  - cosine_ap
  - dot_accuracy
  - dot_accuracy_threshold
  - dot_f1
  - dot_f1_threshold
  - dot_precision
  - dot_recall
  - dot_ap
  - manhattan_accuracy
  - manhattan_accuracy_threshold
  - manhattan_f1
  - manhattan_f1_threshold
  - manhattan_precision
  - manhattan_recall
  - manhattan_ap
  - euclidean_accuracy
  - euclidean_accuracy_threshold
  - euclidean_f1
  - euclidean_f1_threshold
  - euclidean_precision
  - euclidean_recall
  - euclidean_ap
  - max_accuracy
  - max_accuracy_threshold
  - max_f1
  - max_f1_threshold
  - max_precision
  - max_recall
  - max_ap
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:7004
  - loss:SoftmaxLoss
widget:
  - source_sentence: >-
      Google SEO expert Matt Cutts had a similar experience, of the eight
      magazines and newspapers Cutts tried to order, he received zero.
    sentences:
      - >-
        He dissolved the services of her guards and her court attendants and
        seized an expansive reach of properties belonging to her.
      - >-
        Google SEO expert Matt Cutts had a comparable occurrence, of the eight
        magazines and newspapers Cutts tried to order, he received zero.
      - >-
        bill's newest solo play, "all over the map", premiered off broadway in
        april 2016, produced by all for an individual cinema.
  - source_sentence: >-
      Shula said that Namath "beat our blitz" with his fast release, which let
      him quickly dump the football off to a receiver.
    sentences:
      - >-
        Shula said that Namath "beat our blitz" with his quick throw, which let
        him quickly dump the football off to a receiver.
      - >-
        it elects a single component of parliament (mp) by the first past the
        post system of election.
      - >-
        Matt Groening said that West was one of the most widely known group to
        ever come to the studio.
  - source_sentence: >-
      When Angel calls out her name, Cordelia suddenly appears from the opposite
      side of the room saying, "Yep, that chick's in rough shape.
    sentences:
      - >-
        The ruined row of text, part of the Florida East Coast Railway, was
        repaired by 2014 renewing freight train access to the port.
      - >-
        When Angel calls out her name, Cordelia suddenly appears from the
        opposite side of the room saying, "Yep, that chick's in approximate
        form.
      - >-
        Chaplin's films introduced a moderated kind of comedy than the typical
        Keystone farce, and he developed a large fan base.
  - source_sentence: >-
      The following table shows the distances traversed by National Route 11 in
      each different department, showing cities and towns that it passes by (or
      near).
    sentences:
      - >-
        The following table shows the distances traversed by National Route 11
        in each separate city authority, showing cities and towns that it passes
        by (or near).
      - >-
        Similarly, indigenous communities and leaders practice as the main rule
        of law on local native lands and reserves.
      - >-
        later, sylvan mixed gary numan's albums "replicas" (with numan's
        previous band tubeway army) and "the quest for instant gratification".
  - source_sentence: She wants to write about Keima but suffers a major case of writer's block.
    sentences:
      - >-
        In some countries, new extremist parties on the extreme opposite of left
        of the political spectrum arose, motivated through issues of
        immigration, multiculturalism and integration.
      - >-
        specific medical status of movement and the general condition of
        movement both are conditions under which contradictions can move.
      - >-
        She wants to write about Keima but suffers a huge occurrence of writer's
        block.
model-index:
  - name: SentenceTransformer based on google/electra-large-discriminator
    results:
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: quora duplicates dev
          type: quora-duplicates-dev
        metrics:
          - type: cosine_accuracy
            value: 0.746
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.957567036151886
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.7678410117434508
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.9061176776885986
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.700164744645799
            name: Cosine Precision
          - type: cosine_recall
            value: 0.85
            name: Cosine Recall
          - type: cosine_ap
            value: 0.8056677266949036
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.689
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 256.4343566894531
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.7481781376518218
            name: Dot F1
          - type: dot_f1_threshold
            value: 252.6839599609375
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.6285714285714286
            name: Dot Precision
          - type: dot_recall
            value: 0.924
            name: Dot Recall
          - type: dot_ap
            value: 0.6031219278518416
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.746
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 103.93816375732422
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.767172167707404
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 200.26837158203125
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.6924315619967794
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.86
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.8064055162262082
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.744
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 5.6128997802734375
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.7639639639639639
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 8.71990966796875
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.6950819672131148
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.848
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.8072457371353838
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.746
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 256.4343566894531
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.7678410117434508
            name: Max F1
          - type: max_f1_threshold
            value: 252.6839599609375
            name: Max F1 Threshold
          - type: max_precision
            value: 0.700164744645799
            name: Max Precision
          - type: max_recall
            value: 0.924
            name: Max Recall
          - type: max_ap
            value: 0.8072457371353838
            name: Max Ap

SentenceTransformer based on google/electra-large-discriminator

This is a sentence-transformers model finetuned from google/electra-large-discriminator on the PiC/phrase_similarity dataset. It maps sentences & paragraphs to a 1024-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: google/electra-large-discriminator
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- PiC/phrase_similarity
Language: en

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: ElectraModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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})
)

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("Deehan1866/finetuned-valloss-electra-large-discriminator")
# Run inference
sentences = [
    "She wants to write about Keima but suffers a major case of writer's block.",
    "She wants to write about Keima but suffers a huge occurrence of writer's block.",
    'specific medical status of movement and the general condition of movement both are conditions under which contradictions can move.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Binary Classification

Dataset: quora-duplicates-dev
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.746
cosine_accuracy_threshold	0.9576
cosine_f1	0.7678
cosine_f1_threshold	0.9061
cosine_precision	0.7002
cosine_recall	0.85
cosine_ap	0.8057
dot_accuracy	0.689
dot_accuracy_threshold	256.4344
dot_f1	0.7482
dot_f1_threshold	252.684
dot_precision	0.6286
dot_recall	0.924
dot_ap	0.6031
manhattan_accuracy	0.746
manhattan_accuracy_threshold	103.9382
manhattan_f1	0.7672
manhattan_f1_threshold	200.2684
manhattan_precision	0.6924
manhattan_recall	0.86
manhattan_ap	0.8064
euclidean_accuracy	0.744
euclidean_accuracy_threshold	5.6129
euclidean_f1	0.764
euclidean_f1_threshold	8.7199
euclidean_precision	0.6951
euclidean_recall	0.848
euclidean_ap	0.8072
max_accuracy	0.746
max_accuracy_threshold	256.4344
max_f1	0.7678
max_f1_threshold	252.684
max_precision	0.7002
max_recall	0.924
max_ap	0.8072

Training Details

Training Dataset

PiC/phrase_similarity

Dataset: PiC/phrase_similarity at fc67ce7
Size: 7,004 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string int
details
min: 12 tokens
mean: 26.35 tokens
max: 57 tokens

min: 12 tokens
mean: 26.89 tokens
max: 58 tokens

0: ~48.80%
1: ~51.20%

	sentence1	sentence2	label
type	string	string	int
details	min: 12 tokens mean: 26.35 tokens max: 57 tokens	min: 12 tokens mean: 26.89 tokens max: 58 tokens	0: ~48.80% 1: ~51.20%

Samples:

sentence1	sentence2	label
`newly formed camp is released from the membrane and diffuses across the intracellular space where it serves to activate pka.`	`recently made encampment is released from the membrane and diffuses across the intracellular space where it serves to activate pka.`	`0`
`According to one data, in 1910, on others – in 1915, the mansion became Natalya Dmitriyevna Shchuchkina's property.`	`According to a particular statistic, in 1910, on others – in 1915, the mansion became Natalya Dmitriyevna Shchuchkina's property.`	`1`
`Note that Fact 1 does not assume any particular structure on the set formula_65.`	`Note that Fact 1 does not assume any specific edifice on the set formula_65.`	`0`

Loss: SoftmaxLoss

Evaluation Dataset

PiC/phrase_similarity

Dataset: PiC/phrase_similarity at fc67ce7
Size: 1,000 evaluation samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string int
details
min: 9 tokens
mean: 26.21 tokens
max: 61 tokens

min: 10 tokens
mean: 26.8 tokens
max: 61 tokens

0: ~50.00%
1: ~50.00%

	sentence1	sentence2	label
type	string	string	int
details	min: 9 tokens mean: 26.21 tokens max: 61 tokens	min: 10 tokens mean: 26.8 tokens max: 61 tokens	0: ~50.00% 1: ~50.00%

Samples:

sentence1	sentence2	label
`after theo's apparent death, she decides to leave first colony and ends up traveling with the apostles.`	`after theo's apparent death, she decides to leave original settlement and ends up traveling with the apostles.`	`0`
`The guard assigned to Vivian leaves her to prevent the robbery, allowing her to connect to the bank's network.`	`The guard assigned to Vivian leaves her to prevent the robbery, allowing her to connect to the bank's locations.`	`0`
`Two days later Louis XVI banished Necker by a "lettre de cachet" for his very public exchange of pamphlets.`	`Two days later Louis XVI banished Necker by a "lettre de cachet" for his very free forum of pamphlets.`	`0`

Loss: SoftmaxLoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 2e-05
num_train_epochs: 100
warmup_ratio: 0.1
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_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: 100
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: False
fp16: 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}
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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
dispatch_batches: None
split_batches: 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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	quora-duplicates-dev_max_ap
0	0	-	-	0.6721
0.2283	100	-	0.6942	0.6722
0.4566	200	-	0.6932	0.6727
0.6849	300	-	0.6889	0.6730
0.9132	400	-	0.6813	0.6933
1.1416	500	0.6891	0.6562	0.7557
1.3699	600	-	0.5780	0.7861
1.5982	700	-	0.5417	0.7894
1.8265	800	-	0.5238	0.8072
2.0548	900	-	0.5261	0.8079
2.2831	1000	0.5426	0.5418	0.8144
2.5114	1100	-	0.5463	0.8161
2.7397	1200	-	0.5737	0.7916
2.9680	1300	-	0.5558	0.8038
3.1963	1400	-	0.5878	0.8175
3.4247	1500	0.4201	0.6230	0.8040
3.6530	1600	-	0.6078	0.8064
3.8813	1700	-	0.6065	0.8042
4.1096	1800	-	0.7494	0.8072

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.10
Sentence Transformers: 3.0.1
Transformers: 4.42.3
PyTorch: 2.2.1+cu121
Accelerate: 0.32.1
Datasets: 2.20.0
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers and SoftmaxLoss

@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",
}