|
|
--- |
|
|
tags: |
|
|
- sentence-transformers |
|
|
- sentence-similarity |
|
|
- feature-extraction |
|
|
- dense |
|
|
- generated_from_trainer |
|
|
- dataset_size:269012 |
|
|
- loss:CoSENTLoss |
|
|
base_model: intfloat/e5-large-v2 |
|
|
widget: |
|
|
- source_sentence: smart cutting machine for crafts |
|
|
sentences: |
|
|
- HyperX Cloud Alpha Wireless Gaming Headset |
|
|
- Rubbermaid Brilliance 20-Piece Food Storage Set |
|
|
- Men's Wick Short Sleeve Crew - Light Merino Wool Camo Hunting Shirt, UV Protection |
|
|
Moisture Management Base Layer |
|
|
- source_sentence: high capacity portable hard drive |
|
|
sentences: |
|
|
- Mr. Heater Big Buddy Portable Propane Heater |
|
|
- Samsung Galaxy Watch 5 Pro |
|
|
- Sun Bum Original SPF 45 Sunscreen Mist - Broad Spectrum Moisturizing Facial Sunscreen |
|
|
Spray with Vitamin E - Hawaii 104 Act Compliant (Made without Octinoxate & Oxybenzone) |
|
|
- Travel Friendly - 3.4 oz |
|
|
- source_sentence: fluid acrylics for pouring art |
|
|
sentences: |
|
|
- Linen Suit for Men 2 Pieces Slim Fit Casual Suits Groomsmen Tuxedos Wedding Party |
|
|
Blazer Pants Set Beige |
|
|
- Mejuri Small Hoop Earrings in Gold |
|
|
- Singer Start 1304 Sewing Machine |
|
|
- source_sentence: premium wireless gaming headset |
|
|
sentences: |
|
|
- Vornado MVH Whole Room Heater |
|
|
- Westinghouse 11000 Peak Watt Tri-Fuel Portable Inverter Generator, Remote Start, |
|
|
Transfer Switch Ready, Gas/Propane/Natural Gas Powered, Low THD, Safe for Electronics, |
|
|
Parallel Capable, CO Sensor |
|
|
- Rattaner Patio Wicker Furniture Set 6 Pieces Outdoor HDPE Wicker Conversation |
|
|
Couch Sectional Chair Sofa Set with Grey Cushions |
|
|
- source_sentence: travel system with stroller and car seat |
|
|
sentences: |
|
|
- Chemex Classic Series Pour-Over Glass Coffeemaker |
|
|
- David Yurman Cable Classic Bracelet |
|
|
- Legion Stonehenge Paper Pad |
|
|
pipeline_tag: sentence-similarity |
|
|
library_name: sentence-transformers |
|
|
--- |
|
|
|
|
|
# SentenceTransformer based on intfloat/e5-large-v2 |
|
|
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/e5-large-v2](https://huggingface.co/intfloat/e5-large-v2). It maps sentences & paragraphs to a 1024-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/e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) <!-- at revision f169b11e22de13617baa190a028a32f3493550b6 --> |
|
|
- **Maximum Sequence Length:** 512 tokens |
|
|
- **Output Dimensionality:** 1024 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': 512, 'do_lower_case': False, 'architecture': 'BertModel'}) |
|
|
(1): Pooling({'word_embedding_dimension': 1024, '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 = [ |
|
|
'travel system with stroller and car seat', |
|
|
'Chemex Classic Series Pour-Over Glass Coffeemaker', |
|
|
'Legion Stonehenge Paper Pad', |
|
|
] |
|
|
embeddings = model.encode(sentences) |
|
|
print(embeddings.shape) |
|
|
# [3, 1024] |
|
|
|
|
|
# Get the similarity scores for the embeddings |
|
|
similarities = model.similarity(embeddings, embeddings) |
|
|
print(similarities) |
|
|
# tensor([[1.0000, 0.5295, 0.5210], |
|
|
# [0.5295, 1.0000, 0.5429], |
|
|
# [0.5210, 0.5429, 1.0000]]) |
|
|
``` |
|
|
|
|
|
<!-- |
|
|
### 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.* |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
## 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: 269,012 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: 5 tokens</li><li>mean: 9.64 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 20.82 tokens</li><li>max: 99 tokens</li></ul> | <ul><li>min: -1.0</li><li>mean: 0.04</li><li>max: 0.99</li></ul> | |
|
|
* Samples: |
|
|
| sentence_0 | sentence_1 | label | |
|
|
|:---------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------| |
|
|
| <code>razor set with handle and blades</code> | <code>Hahnemühle Watercolor Journal</code> | <code>-0.8008412511835391</code> | |
|
|
| <code>mini perfume atomizer for refillable travel scent</code> | <code>LISAPACK Perfume Travel Refillable Bottle - Atomizer Cologne Spray for Men Portable - Mini Sprayer Empty for Refill - Small Size 8ML Striped (Grey, Black, Silver)</code> | <code>0.85625</code> | |
|
|
| <code>pour-over glass coffeemaker</code> | <code>Shark Navigator Lift-Away Professional NV356E Vacuum</code> | <code>0.131319533933279</code> | |
|
|
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"scale": 20.0, |
|
|
"similarity_fct": "pairwise_cos_sim" |
|
|
} |
|
|
``` |
|
|
|
|
|
### Training Hyperparameters |
|
|
#### Non-Default Hyperparameters |
|
|
|
|
|
- `per_device_train_batch_size`: 32 |
|
|
- `per_device_eval_batch_size`: 32 |
|
|
- `num_train_epochs`: 1 |
|
|
- `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`: 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`: 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`: 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} |
|
|
- `parallelism_config`: None |
|
|
- `deepspeed`: None |
|
|
- `label_smoothing_factor`: 0.0 |
|
|
- `optim`: adamw_torch_fused |
|
|
- `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 |
|
|
- `hub_revision`: None |
|
|
- `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 |
|
|
- `liger_kernel_config`: None |
|
|
- `eval_use_gather_object`: False |
|
|
- `average_tokens_across_devices`: False |
|
|
- `prompts`: None |
|
|
- `batch_sampler`: batch_sampler |
|
|
- `multi_dataset_batch_sampler`: round_robin |
|
|
- `router_mapping`: {} |
|
|
- `learning_rate_mapping`: {} |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
| Epoch | Step | Training Loss | |
|
|
|:------:|:----:|:-------------:| |
|
|
| 0.0595 | 500 | 5.606 | |
|
|
| 0.1189 | 1000 | 5.5059 | |
|
|
| 0.1784 | 1500 | 5.4614 | |
|
|
| 0.2379 | 2000 | 5.4299 | |
|
|
| 0.2974 | 2500 | 5.415 | |
|
|
| 0.3568 | 3000 | 5.4104 | |
|
|
| 0.4163 | 3500 | 5.3718 | |
|
|
| 0.4758 | 4000 | 5.3755 | |
|
|
| 0.5353 | 4500 | 5.3545 | |
|
|
| 0.5947 | 5000 | 5.3498 | |
|
|
| 0.6542 | 5500 | 5.3392 | |
|
|
| 0.7137 | 6000 | 5.3521 | |
|
|
| 0.7732 | 6500 | 5.3248 | |
|
|
| 0.8326 | 7000 | 5.3044 | |
|
|
| 0.8921 | 7500 | 5.2916 | |
|
|
| 0.9516 | 8000 | 5.2891 | |
|
|
|
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.12.11 |
|
|
- Sentence Transformers: 5.1.0 |
|
|
- Transformers: 4.56.0 |
|
|
- PyTorch: 2.8.0+cu126 |
|
|
- Accelerate: 1.10.1 |
|
|
- Datasets: 4.0.0 |
|
|
- Tokenizers: 0.22.0 |
|
|
|
|
|
## 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", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### CoSENTLoss |
|
|
```bibtex |
|
|
@online{kexuefm-8847, |
|
|
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, |
|
|
author={Su Jianlin}, |
|
|
year={2022}, |
|
|
month={Jan}, |
|
|
url={https://kexue.fm/archives/8847}, |
|
|
} |
|
|
``` |
|
|
|
|
|
<!-- |
|
|
## 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 |
|
|
|
|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
|
|
--> |
