| | ---
|
| | base_model: sentence-transformers/all-mpnet-base-v2
|
| | datasets: []
|
| | language: []
|
| | 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
|
| | pipeline_tag: sentence-similarity
|
| | tags:
|
| | - sentence-transformers
|
| | - sentence-similarity
|
| | - feature-extraction
|
| | - generated_from_trainer
|
| | - dataset_size:13063
|
| | - loss:CosineSimilarityLoss
|
| | widget:
|
| | - source_sentence: I cant wait to leave Chicago
|
| | sentences:
|
| | - This is the shit Chicago needs to be recognized for not Keef
|
| | - is candice singing again tonight
|
| | - half time Chelsea were losing 10
|
| | - source_sentence: Andre miller best lobbing pg in the game
|
| | sentences:
|
| | - Am I the only one who dont get Amber alert
|
| | - Backstrom hurt in warmup Harding could start
|
| | - Andre miller is even slower in person
|
| | - source_sentence: Bayless couldve dunked that from the free throw
|
| | sentences:
|
| | - but what great finger roll by Bayless
|
| | - Wow Bayless has to make EspnSCTop with that end of 3rd
|
| | - i mean calum u didnt follow
|
| | - source_sentence: Backstrom Hurt in warmups Harding gets the start
|
| | sentences:
|
| | - Should I go to Nashville or Chicago for my 17th birthday
|
| | - I hate Chelsea possibly more than most
|
| | - Of course Backstrom would get injured during warmups
|
| | - source_sentence: Calum I love you plz follow me
|
| | sentences:
|
| | - CALUM PLEASE BE MY FIRST CELEBRITY TO FOLLOW ME
|
| | - Walking around downtown Chicago in a dress and listening to the new Iggy Pop
|
| | - I think Candice has what it takes to win American Idol AND Angie too
|
| | model-index:
|
| | - name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
|
| | results:
|
| | - task:
|
| | type: semantic-similarity
|
| | name: Semantic Similarity
|
| | dataset:
|
| | name: Unknown
|
| | type: unknown
|
| | metrics:
|
| | - type: pearson_cosine
|
| | value: 0.6949485250178733
|
| | name: Pearson Cosine
|
| | - type: spearman_cosine
|
| | value: 0.6626359968437283
|
| | name: Spearman Cosine
|
| | - type: pearson_manhattan
|
| | value: 0.688092975176289
|
| | name: Pearson Manhattan
|
| | - type: spearman_manhattan
|
| | value: 0.6630998028133662
|
| | name: Spearman Manhattan
|
| | - type: pearson_euclidean
|
| | value: 0.6880277270034267
|
| | name: Pearson Euclidean
|
| | - type: spearman_euclidean
|
| | value: 0.6626358741747785
|
| | name: Spearman Euclidean
|
| | - type: pearson_dot
|
| | value: 0.694948520847878
|
| | name: Pearson Dot
|
| | - type: spearman_dot
|
| | value: 0.6626359082695851
|
| | name: Spearman Dot
|
| | - type: pearson_max
|
| | value: 0.6949485250178733
|
| | name: Pearson Max
|
| | - type: spearman_max
|
| | value: 0.6630998028133662
|
| | name: Spearman Max
|
| | ---
|
| |
|
| | # SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
|
| |
|
| | This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 84f2bcc00d77236f9e89c8a360a00fb1139bf47d -->
|
| | - **Maximum Sequence Length:** 384 tokens
|
| | - **Output Dimensionality:** 768 tokens
|
| | - **Similarity Function:** Cosine Similarity
|
| | <!-- - **Training Dataset:** Unknown -->
|
| | <!-- - **Language:** Unknown -->
|
| | <!-- - **License:** Unknown -->
|
| |
|
| | ### Model Sources
|
| |
|
| | - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
|
| | - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
|
| | - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
|
| |
|
| | ### Full Model Architecture
|
| |
|
| | ```
|
| | SentenceTransformer(
|
| | (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
|
| | (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})
|
| | (2): Normalize()
|
| | )
|
| | ```
|
| |
|
| | ## Usage
|
| |
|
| | ### Direct Usage (Sentence Transformers)
|
| |
|
| | First install the Sentence Transformers library:
|
| |
|
| | ```bash
|
| | pip install -U sentence-transformers
|
| | ```
|
| |
|
| | Then you can load this model and run inference.
|
| | ```python
|
| | from sentence_transformers import SentenceTransformer
|
| |
|
| | # Download from the 🤗 Hub
|
| | model = SentenceTransformer("mspy/twitter-paraphrase-embeddings")
|
| | # Run inference
|
| | sentences = [
|
| | 'Calum I love you plz follow me',
|
| | 'CALUM PLEASE BE MY FIRST CELEBRITY TO FOLLOW ME',
|
| | 'Walking around downtown Chicago in a dress and listening to the new Iggy Pop',
|
| | ]
|
| | 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]
|
| | ```
|
| |
|
| | <!--
|
| | ### Direct Usage (Transformers)
|
| |
|
| | <details><summary>Click to see the direct usage in Transformers</summary>
|
| |
|
| | </details>
|
| | -->
|
| |
|
| | <!--
|
| | ### Downstream Usage (Sentence Transformers)
|
| |
|
| | You can finetune this model on your own dataset.
|
| |
|
| | <details><summary>Click to expand</summary>
|
| |
|
| | </details>
|
| | -->
|
| |
|
| | <!--
|
| | ### Out-of-Scope Use
|
| |
|
| | *List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
| | -->
|
| |
|
| | ## Evaluation
|
| |
|
| | ### Metrics
|
| |
|
| | #### Semantic Similarity
|
| |
|
| | * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
|
| |
|
| | | Metric | Value |
|
| | |:--------------------|:-----------|
|
| | | pearson_cosine | 0.6949 |
|
| | | **spearman_cosine** | **0.6626** |
|
| | | pearson_manhattan | 0.6881 |
|
| | | spearman_manhattan | 0.6631 |
|
| | | pearson_euclidean | 0.688 |
|
| | | spearman_euclidean | 0.6626 |
|
| | | pearson_dot | 0.6949 |
|
| | | spearman_dot | 0.6626 |
|
| | | pearson_max | 0.6949 |
|
| | | spearman_max | 0.6631 |
|
| |
|
| | <!--
|
| | ## Bias, Risks and Limitations
|
| |
|
| | *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
| | -->
|
| |
|
| | <!--
|
| | ### Recommendations
|
| |
|
| | *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
| | -->
|
| |
|
| | ## Training Details
|
| |
|
| | ### Training Dataset
|
| |
|
| | #### Unnamed Dataset
|
| |
|
| |
|
| | * Size: 13,063 training samples
|
| | * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
|
| | * Approximate statistics based on the first 1000 samples:
|
| | | | sentence1 | sentence2 | label |
|
| | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
|
| | | type | string | string | float |
|
| | | details | <ul><li>min: 7 tokens</li><li>mean: 11.16 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 12.31 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.33</li><li>max: 1.0</li></ul> |
|
| | * Samples:
|
| | | sentence1 | sentence2 | label |
|
| | |:------------------------------------------------------|:-------------------------------------------------------------------|:-----------------|
|
| | | <code>EJ Manuel the 1st QB to go in this draft</code> | <code>But my bro from the 757 EJ Manuel is the 1st QB gone</code> | <code>1.0</code> |
|
| | | <code>EJ Manuel the 1st QB to go in this draft</code> | <code>Can believe EJ Manuel went as the 1st QB in the draft</code> | <code>1.0</code> |
|
| | | <code>EJ Manuel the 1st QB to go in this draft</code> | <code>EJ MANUEL IS THE 1ST QB what</code> | <code>0.6</code> |
|
| | * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
|
| | ```json
|
| | {
|
| | "loss_fct": "torch.nn.modules.loss.MSELoss"
|
| | }
|
| | ```
|
| |
|
| | ### Evaluation Dataset
|
| |
|
| | #### Unnamed Dataset
|
| |
|
| |
|
| | * Size: 4,727 evaluation samples
|
| | * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
|
| | * Approximate statistics based on the first 1000 samples:
|
| | | | sentence1 | sentence2 | label |
|
| | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
|
| | | type | string | string | float |
|
| | | details | <ul><li>min: 7 tokens</li><li>mean: 10.04 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 12.22 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.33</li><li>max: 1.0</li></ul> |
|
| | * Samples:
|
| | | sentence1 | sentence2 | label |
|
| | |:---------------------------------------------------------------|:------------------------------------------------------------------|:-----------------|
|
| | | <code>A Walk to Remember is the definition of true love</code> | <code>A Walk to Remember is on and Im in town and Im upset</code> | <code>0.2</code> |
|
| | | <code>A Walk to Remember is the definition of true love</code> | <code>A Walk to Remember is the cutest thing</code> | <code>0.6</code> |
|
| | | <code>A Walk to Remember is the definition of true love</code> | <code>A walk to remember is on ABC family youre welcome</code> | <code>0.2</code> |
|
| | * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
|
| | ```json
|
| | {
|
| | "loss_fct": "torch.nn.modules.loss.MSELoss"
|
| | }
|
| | ```
|
| |
|
| | ### Training Hyperparameters
|
| | #### Non-Default Hyperparameters
|
| |
|
| | - `eval_strategy`: steps
|
| | - `gradient_accumulation_steps`: 2
|
| | - `learning_rate`: 2e-05
|
| | - `num_train_epochs`: 4
|
| | - `warmup_ratio`: 0.1
|
| | - `fp16`: True
|
| |
|
| | #### All Hyperparameters
|
| | <details><summary>Click to expand</summary>
|
| |
|
| | - `overwrite_output_dir`: False
|
| | - `do_predict`: False
|
| | - `eval_strategy`: steps
|
| | - `prediction_loss_only`: True
|
| | - `per_device_train_batch_size`: 8
|
| | - `per_device_eval_batch_size`: 8
|
| | - `per_gpu_train_batch_size`: None
|
| | - `per_gpu_eval_batch_size`: None
|
| | - `gradient_accumulation_steps`: 2
|
| | - `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`: 4
|
| | - `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`: True
|
| | - `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
|
| | - `eval_use_gather_object`: False
|
| | - `batch_sampler`: batch_sampler
|
| | - `multi_dataset_batch_sampler`: proportional
|
| |
|
| | </details>
|
| |
|
| | ### Training Logs
|
| | | Epoch | Step | Training Loss | loss | spearman_cosine |
|
| | |:------:|:----:|:-------------:|:------:|:---------------:|
|
| | | 0.1225 | 100 | - | 0.0729 | 0.6058 |
|
| | | 0.2449 | 200 | - | 0.0646 | 0.6340 |
|
| | | 0.3674 | 300 | - | 0.0627 | 0.6397 |
|
| | | 0.4899 | 400 | - | 0.0621 | 0.6472 |
|
| | | 0.6124 | 500 | 0.0627 | 0.0626 | 0.6496 |
|
| | | 0.7348 | 600 | - | 0.0621 | 0.6446 |
|
| | | 0.8573 | 700 | - | 0.0593 | 0.6695 |
|
| | | 0.9798 | 800 | - | 0.0636 | 0.6440 |
|
| | | 1.1023 | 900 | - | 0.0618 | 0.6525 |
|
| | | 1.2247 | 1000 | 0.0383 | 0.0604 | 0.6639 |
|
| | | 1.3472 | 1100 | - | 0.0608 | 0.6590 |
|
| | | 1.4697 | 1200 | - | 0.0620 | 0.6504 |
|
| | | 1.5922 | 1300 | - | 0.0617 | 0.6467 |
|
| | | 1.7146 | 1400 | - | 0.0615 | 0.6574 |
|
| | | 1.8371 | 1500 | 0.0293 | 0.0622 | 0.6536 |
|
| | | 1.9596 | 1600 | - | 0.0609 | 0.6599 |
|
| | | 2.0821 | 1700 | - | 0.0605 | 0.6658 |
|
| | | 2.2045 | 1800 | - | 0.0615 | 0.6588 |
|
| | | 2.3270 | 1900 | - | 0.0615 | 0.6575 |
|
| | | 2.4495 | 2000 | 0.0215 | 0.0614 | 0.6598 |
|
| | | 2.5720 | 2100 | - | 0.0603 | 0.6681 |
|
| | | 2.6944 | 2200 | - | 0.0606 | 0.6669 |
|
| | | 2.8169 | 2300 | - | 0.0605 | 0.6642 |
|
| | | 2.9394 | 2400 | - | 0.0606 | 0.6630 |
|
| | | 3.0618 | 2500 | 0.018 | 0.0611 | 0.6616 |
|
| | | 3.1843 | 2600 | - | 0.0611 | 0.6619 |
|
| | | 3.3068 | 2700 | - | 0.0611 | 0.6608 |
|
| | | 3.4293 | 2800 | - | 0.0608 | 0.6632 |
|
| | | 3.5517 | 2900 | - | 0.0608 | 0.6623 |
|
| | | 3.6742 | 3000 | 0.014 | 0.0615 | 0.6596 |
|
| | | 3.7967 | 3100 | - | 0.0612 | 0.6616 |
|
| | | 3.9192 | 3200 | - | 0.0610 | 0.6626 |
|
| |
|
| |
|
| | ### Framework Versions
|
| | - Python: 3.10.14
|
| | - Sentence Transformers: 3.0.1
|
| | - Transformers: 4.43.3
|
| | - PyTorch: 2.4.0+cu121
|
| | - Accelerate: 0.33.0
|
| | - Datasets: 2.20.0
|
| | - Tokenizers: 0.19.1
|
| |
|
| | ## Citation
|
| |
|
| | ### BibTeX
|
| |
|
| | #### Sentence Transformers
|
| | ```bibtex
|
| | @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",
|
| | }
|
| | ```
|
| |
|
| | <!--
|
| | ## Glossary
|
| |
|
| | *Clearly define terms in order to be accessible across audiences.*
|
| | -->
|
| |
|
| | <!--
|
| | ## Model Card Authors
|
| |
|
| | *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
| | -->
|
| |
|
| | <!--
|
| | ## Model Card Contact
|
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| | *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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