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Add new SentenceTransformer model.
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metadata
language: []
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dataset_size:10K<n<100K
  - loss:MultipleNegativesSymmetricRankingLoss
base_model: distilbert/distilbert-base-uncased
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
widget:
  - source_sentence: what is GOGO
    sentences:
      - What is Viasat
      - are we flying into Tel Aviv
      - how do i correct a name in term
  - source_sentence: What is EU 261
    sentences:
      - is puj a EU compensation country
      - can i take my bicycle on af
      - flight delays over 6 hours
  - source_sentence: Can i get wifi
    sentences:
      - which aircrafts do not have wifi
      - military traveling with pet
      - baggage delay to carousel
  - source_sentence: austin airport
    sentences:
      - What time is IAH open
      - amex card free checked bag
      - what is upgrade companion
  - source_sentence: pets in cargo
    sentences:
      - can a pet travel in cargo
      - baggage exceptions for Amex
      - how do I get sky priority
pipeline_tag: sentence-similarity
model-index:
  - name: SentenceTransformer based on distilbert/distilbert-base-uncased
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: eval examples
          type: eval_examples
        metrics:
          - type: pearson_cosine
            value: .nan
            name: Pearson Cosine
          - type: spearman_cosine
            value: .nan
            name: Spearman Cosine
          - type: pearson_manhattan
            value: .nan
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: .nan
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: .nan
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: .nan
            name: Spearman Euclidean
          - type: pearson_dot
            value: .nan
            name: Pearson Dot
          - type: spearman_dot
            value: .nan
            name: Spearman Dot
          - type: pearson_max
            value: .nan
            name: Pearson Max
          - type: spearman_max
            value: .nan
            name: Spearman Max

SentenceTransformer based on distilbert/distilbert-base-uncased

This is a sentence-transformers model finetuned from distilbert/distilbert-base-uncased. 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: distilbert/distilbert-base-uncased
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 768 tokens
  • Similarity Function: Cosine Similarity

Model Sources

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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("pjbhaumik/biencoder-finetune-model-v9")
# Run inference
sentences = [
    'pets in cargo',
    'can a pet travel in cargo',
    'baggage exceptions for Amex',
]
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

Semantic Similarity

Metric Value
pearson_cosine nan
spearman_cosine nan
pearson_manhattan nan
spearman_manhattan nan
pearson_euclidean nan
spearman_euclidean nan
pearson_dot nan
spearman_dot nan
pearson_max nan
spearman_max nan

Training Details

Training Dataset

Unnamed Dataset

  • Size: 15,488 training samples
  • Columns: sentence_0, sentence_1, and label
  • Approximate statistics based on the first 1000 samples:
    sentence_0 sentence_1 label
    type string string int
    details
    • min: 4 tokens
    • mean: 10.4 tokens
    • max: 47 tokens
    • min: 4 tokens
    • mean: 10.14 tokens
    • max: 37 tokens
    • 1: 100.00%
  • Samples:
    sentence_0 sentence_1 label
    how to use a companion certificate on delta.com SHOPPING ON DELTA.COM FOR AMEX CERT 1
    is jamaica can be booked with companion certificate what areas can the American Express companion certificate be applied to 1
    how do i book award travel on klm can you book an air france ticket with miles 1
  • Loss: MultipleNegativesSymmetricRankingLoss with these parameters:
    {
        "scale": 20.0,
        "similarity_fct": "cos_sim"
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • num_train_epochs: 12
  • multi_dataset_batch_sampler: round_robin

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: 5e-05
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1
  • num_train_epochs: 12
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.0
  • 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
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: round_robin

Training Logs

Click to expand
Epoch Step Training Loss eval_examples_spearman_max
0.1033 100 - nan
0.2066 200 - nan
0.3099 300 - nan
0.4132 400 - nan
0.5165 500 0.7655 nan
0.6198 600 - nan
0.7231 700 - nan
0.8264 800 - nan
0.9298 900 - nan
1.0 968 - nan
1.0331 1000 0.3727 nan
1.1364 1100 - nan
1.2397 1200 - nan
1.3430 1300 - nan
1.4463 1400 - nan
1.5496 1500 0.2686 nan
1.6529 1600 - nan
1.7562 1700 - nan
1.8595 1800 - nan
1.9628 1900 - nan
2.0 1936 - nan
2.0661 2000 0.2709 nan
2.1694 2100 - nan
2.2727 2200 - nan
2.3760 2300 - nan
2.4793 2400 - nan
2.5826 2500 0.231 nan
2.6860 2600 - nan
2.7893 2700 - nan
2.8926 2800 - nan
2.9959 2900 - nan
3.0 2904 - nan
3.0992 3000 0.2461 nan
3.2025 3100 - nan
3.3058 3200 - nan
3.4091 3300 - nan
3.5124 3400 - nan
3.6157 3500 0.2181 nan
3.7190 3600 - nan
3.8223 3700 - nan
3.9256 3800 - nan
4.0 3872 - nan
4.0289 3900 - nan
4.1322 4000 0.2288 nan
4.2355 4100 - nan
4.3388 4200 - nan
4.4421 4300 - nan
4.5455 4400 - nan
4.6488 4500 0.2123 nan
4.7521 4600 - nan
4.8554 4700 - nan
4.9587 4800 - nan
5.0 4840 - nan
5.0620 4900 - nan
5.1653 5000 0.2254 nan
5.2686 5100 - nan
5.3719 5200 - nan
5.4752 5300 - nan
5.5785 5400 - nan
5.6818 5500 0.2077 nan
5.7851 5600 - nan
5.8884 5700 - nan
5.9917 5800 - nan
6.0 5808 - nan
6.0950 5900 - nan
6.1983 6000 0.218 nan
6.3017 6100 - nan
6.4050 6200 - nan
6.5083 6300 - nan
6.6116 6400 - nan
6.7149 6500 0.206 nan
6.8182 6600 - nan
6.9215 6700 - nan
7.0 6776 - nan
7.0248 6800 - nan
7.1281 6900 - nan
7.2314 7000 0.2126 nan
7.3347 7100 - nan
7.4380 7200 - nan
7.5413 7300 - nan
7.6446 7400 - nan
7.7479 7500 0.2065 nan
7.8512 7600 - nan
7.9545 7700 - nan
8.0 7744 - nan
8.0579 7800 - nan
8.1612 7900 - nan
8.2645 8000 0.2068 nan
8.3678 8100 - nan
8.4711 8200 - nan
8.5744 8300 - nan
8.6777 8400 - nan
8.7810 8500 0.2014 nan
8.8843 8600 - nan
8.9876 8700 - nan
9.0 8712 - nan
9.0909 8800 - nan
9.1942 8900 - nan
9.2975 9000 0.2057 nan
9.4008 9100 - nan
9.5041 9200 - nan
9.6074 9300 - nan
9.7107 9400 - nan
9.8140 9500 0.1969 nan
9.9174 9600 - nan
10.0 9680 - nan
10.0207 9700 - nan
10.1240 9800 - nan
10.2273 9900 - nan
10.3306 10000 0.2023 nan
10.4339 10100 - nan
10.5372 10200 - nan
10.6405 10300 - nan
10.7438 10400 - nan
10.8471 10500 0.1946 nan
10.9504 10600 - nan
11.0 10648 - nan
11.0537 10700 - nan
11.1570 10800 - nan
11.2603 10900 - nan
11.3636 11000 0.1982 nan
11.4669 11100 - nan
11.5702 11200 - nan
11.6736 11300 - nan
11.7769 11400 - nan
11.8802 11500 0.1919 nan
11.9835 11600 - nan
12.0 11616 - nan

Framework Versions

  • Python: 3.10.14
  • Sentence Transformers: 3.0.0
  • Transformers: 4.41.2
  • PyTorch: 2.1.0
  • Accelerate: 0.30.1
  • Datasets: 2.19.1
  • 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",
}