Add new SentenceTransformer model

7f852f0 verified 29 days ago

15 kB

	---
	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) <!-- at revision 6f68f4dc2d28aadefa038c79023dc7dfd51f6495 -->
	- Maximum Sequence Length: 128 tokens
	- Output Dimensionality: 768 dimensions
	- 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': 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]
	```

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	## Evaluation

	### Metrics

	#### Semantic Similarity

	* Dataset: `validation`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.148 \|
	\| spearman_cosine \| 0.1537 \|

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	## Training Details

	### Training Dataset

	#### Unnamed Dataset


	* Size: 100,000 training samples
	* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence_0 \| sentence_1 \| label \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 6 tokens</li><li>mean: 37.81 tokens</li><li>max: 65 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 38.01 tokens</li><li>max: 67 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.56</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| label \|
	\|:----------------------------------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------\|
	\| <code>Early lead for #SUI over #HON thanks to Shaqiri taking a page out of Robben's book. He paid attention during Bayern practices. #ShaqAttaq ⚽️</code> \| <code>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…</code> \| <code>0.0</code> \|
	\| <code>RT @RTEsoccer: Group E result: #HON 0-3 #SUI. Shaqiri the hat-trick hero as the Swiss progress: http://t.co/fZYw9NFghO #rteworldcup http://…</code> \| <code>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…</code> \| <code>1.0</code> \|
	\| <code>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…</code> \| <code>Yup Honduras say goodbye lll</code> \| <code>0.0</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

	- `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
	<details><summary>Click to expand</summary>

	- `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

	</details>

	### 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",
	}
	```

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