--- base_model: whaleloops/phrase-bert library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:100000 - loss:CosineSimilarityLoss widget: - source_sentence: 'RT @AnfieldBond: Xherdan Shaqiri, who has been linked with a summer move to Liverpool, has just scored a hat-trick against Honduras. #LFC' sentences: - Honduras is fucking it up for ecuador - Some strike Shakira. Just need a couple more one from Honduras. - "RT @2014WorIdCup: HALF TIME: France and Ecuador 0-0. \nSwitzerland leads Honduras\ \ 2-0." - source_sentence: Yall watching the Honduras game when im watching france😂😂 Honduras poo sentences: - 'I’m following Honduras versus Switzerland in the FIFA Global Stadium #HONSUI #worldcup #joinin' - 'RT @SportsCenter: That''s it for Group E! France wins group after 0-0 tie, Switzerland advances thanks to 3-0 win. Ecuador and Honduras are …' - 'RT @worldsoccershop: HAT TRICK FOR @XS_11official! #HON 0-3 #SUI. #WorldCup2014' - source_sentence: 'RT @rffuk: Xherdan Shaqiri just scored this absolute wonder goal to put #SWI 1-0 ahead v #HON. What a strike son! https://t.co/vHuIPCucpV' sentences: - 'RT @trueSCRlife: If #Shaqiri scores vs #HON we''ll give away a pair of Magistas. Follow & RT to enter. Winner DMed! #HONvsSUI http://t.co/EG…' - 'RT @soccerdotcom: Los Catrachos! Follow @soccerdotcom and RT for the chance to win a Joma #HON Jersey signed by the team! http://t.co/2NTfw…' - 'Shaqiri has 2 goals in the first half! Can he score the first hat trick of the #WorldCup? #HON #SUI http://t.co/M21zGv0qw4' - source_sentence: Honduras copped the fendi sentences: - 'RT @worldsoccershop: If #Costly scores for #HON we''ll give away a pair of adidas #Nitrocharge. Follow & RT to enter! #allin or nothing. htt…' - '#SUI get a second against #HON. Shaqiri scores once again! #iMOTM?' - 'RT @soccerdotcom: Los Catrachos! Follow @soccerdotcom and RT for the chance to win a Joma #HON Jersey signed by the team! http://t.co/2NTfw…' - source_sentence: Honduras is technically still in the World Cup and Italy plus England are out means Honduras is better than them😂 sentences: - wtf Honduras has to win 😩 - 'Honduras still better than the #CGHS JV Female Soccer Team 😂😂' - 'RT @iambolar: FT:Honduras 0-3 Switzerland. Shaqiri nets d 50th hat trick in #WorldCup history as Switzerland qualify 4d next round. http://…' model-index: - name: SentenceTransformer based on whaleloops/phrase-bert results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: validation type: validation metrics: - type: pearson_cosine value: 0.14803022870400553 name: Pearson Cosine - type: spearman_cosine value: 0.1536611594776976 name: Spearman Cosine --- # SentenceTransformer based on whaleloops/phrase-bert This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [whaleloops/phrase-bert](https://huggingface.co/whaleloops/phrase-bert). 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:** [whaleloops/phrase-bert](https://huggingface.co/whaleloops/phrase-bert) - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity ### 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': 128, 'do_lower_case': None}) with Transformer model: BertModel (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: ```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("peulsilva/sentence-transformer-trained-tweet") # Run inference sentences = [ 'Honduras is technically still in the World Cup and Italy plus England are out means Honduras is better than them😂', 'RT @iambolar: FT:Honduras 0-3 Switzerland. Shaqiri nets d 50th hat trick in #WorldCup history as Switzerland qualify 4d next round. http://…', 'Honduras still better than the #CGHS JV Female Soccer Team 😂😂', ] 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 * Dataset: `validation` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.148 | | **spearman_cosine** | **0.1537** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 100,000 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 | float | | details | | | | * Samples: | sentence_0 | sentence_1 | label | |:----------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| | Early lead for #SUI over #HON thanks to Shaqiri taking a page out of Robben's book. He paid attention during Bayern practices. #ShaqAttaq ⚽️ | RT @soccerdotcom: Los Catrachos! Follow @soccerdotcom and RT for the chance to win a Joma #HON Jersey signed by the team! http://t.co/2NTfw… | 0.0 | | RT @RTEsoccer: Group E result: #HON 0-3 #SUI. Shaqiri the hat-trick hero as the Swiss progress: http://t.co/fZYw9NFghO #rteworldcup http://… | RT @trueSCRlife: If #Shaqiri scores vs #HON we'll give away a pair of Magistas. Follow & RT to enter. Winner DMed! #HONvsSUI http://t.co/EG… | 1.0 | | RT @TheSCRLife: If #HON wins we’ll give away a pair of Superflys. FOLLOW & RETWEET. Not following?Won’t win. (I’m checking). http://t.co/xw… | Yup Honduras say goodbye lll | 0.0 | * Loss: [CosineSimilarityLoss](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 - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `num_train_epochs`: 1 - `fp16`: True - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `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 - `num_train_epochs`: 1 - `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`: 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 - `use_liger_kernel`: False - `eval_use_gather_object`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin
### Training Logs | Epoch | Step | Training Loss | validation_spearman_cosine | |:------:|:----:|:-------------:|:--------------------------:| | 0.6394 | 500 | 0.2429 | - | | 1.0 | 782 | - | 0.1537 | ### Framework Versions - Python: 3.11.9 - Sentence Transformers: 3.3.0 - Transformers: 4.45.0.dev0 - PyTorch: 2.4.1+cu121 - Accelerate: 0.34.2 - 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", } ```