sub-version a: Hyper Tunning for "full stack", "back end" and "front end"

f861129 verified 6 months ago

27.5 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:910013
	- loss:CosineSimilarityLoss
	base_model: intfloat/multilingual-e5-small
	widget:
	- source_sentence: business healing
	sentences:
	- modify ict system capacity
	- objetividade, inovadora,estudiosa,pesquisadora e organizada
	- business consulting
	- source_sentence: architecture acoustics
	sentences:
	- disicpline leader
	- 生产工艺开发及优化
	- data analysis
	- source_sentence: arbitru natatie
	sentences:
	- criação cinematográfica
	- quarterly distribution
	- улучшение путешествий клиентов с помощью дополненной реальности
	- source_sentence: configuración de software antivirus
	sentences:
	- protocol & coordination
	- laurea magistrale biologia
	- deploy anti-virus software
	- source_sentence: child maltreatment counselling
	sentences:
	- book covers, flyers, posters, banners
	- tool and die making
	- cmc
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- pearson_cosine
	- spearman_cosine
	model-index:
	- name: SentenceTransformer based on intfloat/multilingual-e5-small
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev
	type: sts-dev
	metrics:
	- type: pearson_cosine
	value: 0.9579653395486292
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8788941637037295
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test
	type: sts-test
	metrics:
	- type: pearson_cosine
	value: 0.9579215714676803
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8795799743051839
	name: Spearman Cosine
	---

	# SentenceTransformer based on intfloat/multilingual-e5-small

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). 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: [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision c007d7ef6fd86656326059b28395a7a03a7c5846 -->
	- Maximum Sequence Length: 30 tokens
	- Output Dimensionality: 384 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': 30, '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:

	```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("sentence_transformers_model_id")
	# Run inference
	sentences = [
	'child maltreatment counselling',
	'cmc',
	'book covers, flyers, posters, banners',
	]
	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]
	```

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	You can finetune this model on your own dataset.

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	## Evaluation

	### Metrics

	#### Semantic Similarity

	* Datasets: `sts-dev` and `sts-test`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| sts-dev \| sts-test \|
	\|:--------------------\|:-----------\|:-----------\|
	\| pearson_cosine \| 0.958 \| 0.9579 \|
	\| spearman_cosine \| 0.8789 \| 0.8796 \|

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	## Training Details

	### Training Dataset

	#### Unnamed Dataset

	* Size: 910,013 training samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 3 tokens</li><li>mean: 8.91 tokens</li><li>max: 30 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 8.83 tokens</li><li>max: 30 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.52</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:----------------------------------------------------------------------------------------------\|:--------------------------------------------------\|:------------------\|
	\| <code>edición de fotografias, fondos</code> \| <code>material selection and cognition</code> \| <code>0.0</code> \|
	\| <code>professional alarm installer,service tech.,customer service relations,sales,cctv</code> \| <code>quantity surveying & reading charts</code> \| <code>0.1</code> \|
	\| <code>diagnostico ecografico</code> \| <code>waste identification system downtime</code> \| <code>0.19</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: 113,751 evaluation samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 8.89 tokens</li><li>max: 30 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 8.96 tokens</li><li>max: 30 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:----------------------------------------------------------\|:-------------------------\|:------------------\|
	\| <code>a2 dutch</code> \| <code>a2 dutch</code> \| <code>0.98</code> \|
	\| <code>design of mine dumps</code> \| <code>设计矿山废料堆</code> \| <code>1.0</code> \|
	\| <code>create soil and plant improvement programmes</code> \| <code>创建土壤和植物改良计划</code> \| <code>1.0</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

	- `eval_strategy`: epoch
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `learning_rate`: 1e-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`: epoch
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `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`: 1e-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}
	- `tp_size`: 0
	- `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`: None
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `include_for_metrics`: []
	- `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
	- `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
	- `average_tokens_across_devices`: False
	- `prompts`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	<details><summary>Click to expand</summary>

	\| Epoch \| Step \| Training Loss \| Validation Loss \| sts-dev_spearman_cosine \| sts-test_spearman_cosine \|
	\|:------:\|:-----:\|:-------------:\|:---------------:\|:-----------------------:\|:------------------------:\|
	\| 0.0352 \| 500 \| 0.1991 \| - \| - \| - \|
	\| 0.0703 \| 1000 \| 0.0513 \| - \| - \| - \|
	\| 0.1055 \| 1500 \| 0.0362 \| - \| - \| - \|
	\| 0.1407 \| 2000 \| 0.0331 \| - \| - \| - \|
	\| 0.1758 \| 2500 \| 0.0305 \| - \| - \| - \|
	\| 0.2110 \| 3000 \| 0.029 \| - \| - \| - \|
	\| 0.2461 \| 3500 \| 0.0273 \| - \| - \| - \|
	\| 0.2813 \| 4000 \| 0.0268 \| - \| - \| - \|
	\| 0.3165 \| 4500 \| 0.0255 \| - \| - \| - \|
	\| 0.3516 \| 5000 \| 0.0245 \| - \| - \| - \|
	\| 0.3868 \| 5500 \| 0.0238 \| - \| - \| - \|
	\| 0.4220 \| 6000 \| 0.0236 \| - \| - \| - \|
	\| 0.4571 \| 6500 \| 0.0233 \| - \| - \| - \|
	\| 0.4923 \| 7000 \| 0.0222 \| - \| - \| - \|
	\| 0.5275 \| 7500 \| 0.0225 \| - \| - \| - \|
	\| 0.5626 \| 8000 \| 0.0219 \| - \| - \| - \|
	\| 0.5978 \| 8500 \| 0.0212 \| - \| - \| - \|
	\| 0.6330 \| 9000 \| 0.0215 \| - \| - \| - \|
	\| 0.6681 \| 9500 \| 0.0207 \| - \| - \| - \|
	\| 0.7033 \| 10000 \| 0.0204 \| - \| - \| - \|
	\| 0.7384 \| 10500 \| 0.0203 \| - \| - \| - \|
	\| 0.7736 \| 11000 \| 0.0203 \| - \| - \| - \|
	\| 0.8088 \| 11500 \| 0.0202 \| - \| - \| - \|
	\| 0.8439 \| 12000 \| 0.0202 \| - \| - \| - \|
	\| 0.8791 \| 12500 \| 0.0196 \| - \| - \| - \|
	\| 0.9143 \| 13000 \| 0.0193 \| - \| - \| - \|
	\| 0.9494 \| 13500 \| 0.0193 \| - \| - \| - \|
	\| 0.9846 \| 14000 \| 0.0193 \| - \| - \| - \|
	\| 1.0 \| 14219 \| - \| 0.0170 \| 0.8694 \| - \|
	\| 1.0198 \| 14500 \| 0.0188 \| - \| - \| - \|
	\| 1.0549 \| 15000 \| 0.0178 \| - \| - \| - \|
	\| 1.0901 \| 15500 \| 0.0179 \| - \| - \| - \|
	\| 1.1253 \| 16000 \| 0.0178 \| - \| - \| - \|
	\| 1.1604 \| 16500 \| 0.0178 \| - \| - \| - \|
	\| 1.1956 \| 17000 \| 0.0172 \| - \| - \| - \|
	\| 1.2307 \| 17500 \| 0.0172 \| - \| - \| - \|
	\| 1.2659 \| 18000 \| 0.0175 \| - \| - \| - \|
	\| 1.3011 \| 18500 \| 0.0178 \| - \| - \| - \|
	\| 1.3362 \| 19000 \| 0.0174 \| - \| - \| - \|
	\| 1.3714 \| 19500 \| 0.0175 \| - \| - \| - \|
	\| 1.4066 \| 20000 \| 0.0171 \| - \| - \| - \|
	\| 1.4417 \| 20500 \| 0.0175 \| - \| - \| - \|
	\| 1.4769 \| 21000 \| 0.0173 \| - \| - \| - \|
	\| 1.5121 \| 21500 \| 0.0171 \| - \| - \| - \|
	\| 1.5472 \| 22000 \| 0.0174 \| - \| - \| - \|
	\| 1.5824 \| 22500 \| 0.0172 \| - \| - \| - \|
	\| 1.6176 \| 23000 \| 0.0168 \| - \| - \| - \|
	\| 1.6527 \| 23500 \| 0.0165 \| - \| - \| - \|
	\| 1.6879 \| 24000 \| 0.0169 \| - \| - \| - \|
	\| 1.7230 \| 24500 \| 0.0169 \| - \| - \| - \|
	\| 1.7582 \| 25000 \| 0.0171 \| - \| - \| - \|
	\| 1.7934 \| 25500 \| 0.0165 \| - \| - \| - \|
	\| 1.8285 \| 26000 \| 0.0165 \| - \| - \| - \|
	\| 1.8637 \| 26500 \| 0.0165 \| - \| - \| - \|
	\| 1.8989 \| 27000 \| 0.0165 \| - \| - \| - \|
	\| 1.9340 \| 27500 \| 0.0164 \| - \| - \| - \|
	\| 1.9692 \| 28000 \| 0.0164 \| - \| - \| - \|
	\| 2.0 \| 28438 \| - \| 0.0153 \| 0.8751 \| - \|
	\| 2.0044 \| 28500 \| 0.0162 \| - \| - \| - \|
	\| 2.0395 \| 29000 \| 0.0156 \| - \| - \| - \|
	\| 2.0747 \| 29500 \| 0.0154 \| - \| - \| - \|
	\| 2.1099 \| 30000 \| 0.0157 \| - \| - \| - \|
	\| 2.1450 \| 30500 \| 0.016 \| - \| - \| - \|
	\| 2.1802 \| 31000 \| 0.015 \| - \| - \| - \|
	\| 2.2153 \| 31500 \| 0.0155 \| - \| - \| - \|
	\| 2.2505 \| 32000 \| 0.0154 \| - \| - \| - \|
	\| 2.2857 \| 32500 \| 0.0152 \| - \| - \| - \|
	\| 2.3208 \| 33000 \| 0.0152 \| - \| - \| - \|
	\| 2.3560 \| 33500 \| 0.0152 \| - \| - \| - \|
	\| 2.3912 \| 34000 \| 0.0154 \| - \| - \| - \|
	\| 2.4263 \| 34500 \| 0.0153 \| - \| - \| - \|
	\| 2.4615 \| 35000 \| 0.0154 \| - \| - \| - \|
	\| 2.4967 \| 35500 \| 0.015 \| - \| - \| - \|
	\| 2.5318 \| 36000 \| 0.0153 \| - \| - \| - \|
	\| 2.5670 \| 36500 \| 0.0149 \| - \| - \| - \|
	\| 2.6022 \| 37000 \| 0.015 \| - \| - \| - \|
	\| 2.6373 \| 37500 \| 0.0152 \| - \| - \| - \|
	\| 2.6725 \| 38000 \| 0.0152 \| - \| - \| - \|
	\| 2.7076 \| 38500 \| 0.015 \| - \| - \| - \|
	\| 2.7428 \| 39000 \| 0.0151 \| - \| - \| - \|
	\| 2.7780 \| 39500 \| 0.0155 \| - \| - \| - \|
	\| 2.8131 \| 40000 \| 0.0148 \| - \| - \| - \|
	\| 2.8483 \| 40500 \| 0.0149 \| - \| - \| - \|
	\| 2.8835 \| 41000 \| 0.0147 \| - \| - \| - \|
	\| 2.9186 \| 41500 \| 0.015 \| - \| - \| - \|
	\| 2.9538 \| 42000 \| 0.0148 \| - \| - \| - \|
	\| 2.9890 \| 42500 \| 0.0146 \| - \| - \| - \|
	\| 3.0 \| 42657 \| - \| 0.0146 \| 0.8775 \| - \|
	\| 3.0241 \| 43000 \| 0.0142 \| - \| - \| - \|
	\| 3.0593 \| 43500 \| 0.0144 \| - \| - \| - \|
	\| 3.0945 \| 44000 \| 0.0146 \| - \| - \| - \|
	\| 3.1296 \| 44500 \| 0.0142 \| - \| - \| - \|
	\| 3.1648 \| 45000 \| 0.0144 \| - \| - \| - \|
	\| 3.1999 \| 45500 \| 0.0141 \| - \| - \| - \|
	\| 3.2351 \| 46000 \| 0.0142 \| - \| - \| - \|
	\| 3.2703 \| 46500 \| 0.0142 \| - \| - \| - \|
	\| 3.3054 \| 47000 \| 0.0142 \| - \| - \| - \|
	\| 3.3406 \| 47500 \| 0.0145 \| - \| - \| - \|
	\| 3.3758 \| 48000 \| 0.0142 \| - \| - \| - \|
	\| 3.4109 \| 48500 \| 0.0143 \| - \| - \| - \|
	\| 3.4461 \| 49000 \| 0.0145 \| - \| - \| - \|
	\| 3.4813 \| 49500 \| 0.0142 \| - \| - \| - \|
	\| 3.5164 \| 50000 \| 0.014 \| - \| - \| - \|
	\| 3.5516 \| 50500 \| 0.0141 \| - \| - \| - \|
	\| 3.5868 \| 51000 \| 0.0144 \| - \| - \| - \|
	\| 3.6219 \| 51500 \| 0.0143 \| - \| - \| - \|
	\| 3.6571 \| 52000 \| 0.0143 \| - \| - \| - \|
	\| 3.6922 \| 52500 \| 0.0142 \| - \| - \| - \|
	\| 3.7274 \| 53000 \| 0.014 \| - \| - \| - \|
	\| 3.7626 \| 53500 \| 0.0142 \| - \| - \| - \|
	\| 3.7977 \| 54000 \| 0.0141 \| - \| - \| - \|
	\| 3.8329 \| 54500 \| 0.0141 \| - \| - \| - \|
	\| 3.8681 \| 55000 \| 0.014 \| - \| - \| - \|
	\| 3.9032 \| 55500 \| 0.0143 \| - \| - \| - \|
	\| 3.9384 \| 56000 \| 0.0142 \| - \| - \| - \|
	\| 3.9736 \| 56500 \| 0.0141 \| - \| - \| - \|
	\| 4.0 \| 56876 \| - \| 0.0146 \| 0.8789 \| - \|
	\| -1 \| -1 \| - \| - \| - \| 0.8796 \|

	</details>

	### Framework Versions
	- Python: 3.11.11
	- Sentence Transformers: 4.1.0
	- Transformers: 4.51.3
	- PyTorch: 2.6.0+cu124
	- Accelerate: 1.5.2
	- Datasets: 3.6.0
	- Tokenizers: 0.21.1

	## 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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