metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:7960
  - loss:CoSENTLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
  - source_sentence: >-
      And your phone. Okay do you already have a phone in mind, what you wanted
      to upgrade to.
    sentences:
      - >-
        I'm now going to read out some terms and conditions to complete the
        order.
      - >-
        The same discounts you can have been added as an additional line and do
        into your account. It needs be entitled to % discount off of the costs.
      - Thank you and could you please confirm to me what is your full name.
  - source_sentence: >-
      So glad you're on the right plan. I will also check your average monthly
      usage for the past few months. Your usage is only ## gig of mobile data
      and then the highest one, it's around ##. Gig of mobile details. So
      definitely the ## gig of mobile data will if broken.
    sentences:
      - Thank you for calling over to my name is how can I help you.
      - So the phone that you currently have is that currently a Samsung?
      - >-
        So on that's something that you can they get that the shop and it's at a
        renewal for our insurance. So just in case like once you get back to the
        UK and you don't want to have the insurance anymore. You can possibly
        remove that. That and the full garbage insurance.
  - source_sentence: >-
      Okay, well, I just want to share with you that I'm happy to advise that
      you have an amazing offer on our secondary ninth. So there any family
      members like to join or to under your name with a same billing address so
      they will be getting a 20% desk.
    sentences:
      - >-
        Yes, that's correct for know. Our price is £ and then it won't go down
        to £ after you apply the discount.
      - Thank you for calling over to my name is how can I help you.
      - >-
        Checking your account I can see you are on the and you have been paying
        £ per month. Is that correct?
  - source_sentence: >-
      I just read to process this I just like to open your account here to see
      if we can get this eligible for your upgrade for the new iPhone ## so
      here.
    sentences:
      - >-
        I now need to read some insurance disclosures related to the Ultimate
        Plan you have chosen.
      - Thank you and could you please confirm to me what is your full name.
      - I can provide to you . Are you happy to go ahead with this?
  - source_sentence: Okay, and can you provide me your full name please.
    sentences:
      - >-
        So on that's something that you can they get that the shop and it's at a
        renewal for our insurance. So just in case like once you get back to the
        UK and you don't want to have the insurance anymore. You can possibly
        remove that. That and the full garbage insurance.
      - >-
        You. Okay, so for this one, how do you how do you normally use your
        mobile data.
      - >-
        You. Okay, so for this one, how do you how do you normally use your
        mobile data.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
model-index:
  - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts_dev
        metrics:
          - type: pearson_cosine
            value: 0.5177189921265649
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.2603983787734805
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.5608459921843345
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.2595766499932607
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.5641188480826617
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.26039837957858836
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.5177189925954635
            name: Pearson Dot
          - type: spearman_dot
            value: 0.26040366240168195
            name: Spearman Dot
          - type: pearson_max
            value: 0.5641188480826617
            name: Pearson Max
          - type: spearman_max
            value: 0.26040366240168195
            name: Spearman Max
          - type: pearson_cosine
            value: 0.4585915541798693
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.24734582807664446
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.5059296028724503
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.2466879170820096
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.506069567328991
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.24734582912817787
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.4585915495841867
            name: Pearson Dot
          - type: spearman_dot
            value: 0.24734582759867477
            name: Spearman Dot
          - type: pearson_max
            value: 0.506069567328991
            name: Pearson Max
          - type: spearman_max
            value: 0.24734582912817787
            name: Spearman Max

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: sentence-transformers/all-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("enochlev/xlm-similarity")
# Run inference
sentences = [
    'Okay, and can you provide me your full name please.',
    'You. Okay, so for this one, how do you how do you normally use your mobile data.',
    'You. Okay, so for this one, how do you how do you normally use your mobile data.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Evaluation

Metrics

Semantic Similarity

Dataset: sts_dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.5177
spearman_cosine	0.2604
pearson_manhattan	0.5608
spearman_manhattan	0.2596
pearson_euclidean	0.5641
spearman_euclidean	0.2604
pearson_dot	0.5177
spearman_dot	0.2604
pearson_max	0.5641
spearman_max	0.2604

Semantic Similarity

Dataset: sts_dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.4586
spearman_cosine	0.2473
pearson_manhattan	0.5059
spearman_manhattan	0.2467
pearson_euclidean	0.5061
spearman_euclidean	0.2473
pearson_dot	0.4586
spearman_dot	0.2473
pearson_max	0.5061
spearman_max	0.2473

Training Details

Training Dataset

Unnamed Dataset

Size: 7,960 training samples
Columns: text1, text2, and label
Approximate statistics based on the first 1000 samples:
text1 text2 label
type string string float
details
min: 5 tokens
mean: 21.6 tokens
max: 66 tokens

min: 13 tokens
mean: 28.35 tokens
max: 71 tokens

min: 0.2
mean: 0.22
max: 1.0

	text1	text2	label
type	string	string	float
details	min: 5 tokens mean: 21.6 tokens max: 66 tokens	min: 13 tokens mean: 28.35 tokens max: 71 tokens	min: 0.2 mean: 0.22 max: 1.0

Samples:

text1	text2	label
`Hello, welcome to O2. My name is __ How can I help you today?`	`Thank you for calling over to my name is how can I help you.`	`1.0`
`Hello, welcome to O2. My name is __ How can I help you today?`	`So, I'd look into our accessory so for the airbags the one that we have an ongoing promotion right now for the accessories is the airport second generation. So you can. And either by there's like a great if you want to or I can also make it as an instalment for you. If you want to.`	`0.2`
`Hello, welcome to O2. My name is __ How can I help you today?`	`So on that's something that you can they get that the shop and it's at a renewal for our insurance. So just in case like once you get back to the UK and you don't want to have the insurance anymore. You can possibly remove that. That and the full garbage insurance.`	`0.2`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

Evaluation Dataset

Unnamed Dataset

Size: 1,980 evaluation samples
Columns: text1, text2, and label
Approximate statistics based on the first 1000 samples:
text1 text2 label
type string string float
details
min: 7 tokens
mean: 39.04 tokens
max: 256 tokens

min: 13 tokens
mean: 28.35 tokens
max: 71 tokens

min: 0.2
mean: 0.22
max: 1.0

	text1	text2	label
type	string	string	float
details	min: 7 tokens mean: 39.04 tokens max: 256 tokens	min: 13 tokens mean: 28.35 tokens max: 71 tokens	min: 0.2 mean: 0.22 max: 1.0

Samples:

text1	text2	label
`Right perfect. Thank you for passenger security cyber. Now let me go ahead. Then I look for your option to do an upgrade. So you had mentioned that you're wanting to get an upgrade. Can you tell me is it for a devise or a single plan.`	`Are you planning to get a new sim only plan or a new phone?`	`1.0`
`Right perfect. Thank you for passenger security cyber. Now let me go ahead. Then I look for your option to do an upgrade. So you had mentioned that you're wanting to get an upgrade. Can you tell me is it for a devise or a single plan.`	`So, I'd look into our accessory so for the airbags the one that we have an ongoing promotion right now for the accessories is the airport second generation. So you can. And either by there's like a great if you want to or I can also make it as an instalment for you. If you want to.`	`0.2`
`Right perfect. Thank you for passenger security cyber. Now let me go ahead. Then I look for your option to do an upgrade. So you had mentioned that you're wanting to get an upgrade. Can you tell me is it for a devise or a single plan.`	`So on that's something that you can they get that the shop and it's at a renewal for our insurance. So just in case like once you get back to the UK and you don't want to have the insurance anymore. You can possibly remove that. That and the full garbage insurance.`	`0.2`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
num_train_epochs: 1
warmup_ratio: 0.1
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
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: 5e-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: 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: False
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
use_liger_kernel: False
eval_use_gather_object: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Validation Loss	sts_dev_spearman_max
4.0	128	0.4041	0.2604
1.0	32	0.6357	0.2473

Framework Versions

Python: 3.11.9
Sentence Transformers: 3.2.1
Transformers: 4.45.2
PyTorch: 2.5.1+cu124
Accelerate: 1.1.1
Datasets: 3.1.0
Tokenizers: 0.20.1

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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}