|
|
---
|
|
|
language:
|
|
|
- en
|
|
|
license: apache-2.0
|
|
|
tags:
|
|
|
- sentence-transformers
|
|
|
- sentence-similarity
|
|
|
- feature-extraction
|
|
|
- dense
|
|
|
- generated_from_trainer
|
|
|
- dataset_size:10000
|
|
|
- loss:MultipleNegativesRankingLoss
|
|
|
base_model: google/siglip2-base-patch16-512
|
|
|
widget:
|
|
|
- source_sentence: A man standing next to a little girl riding a horse.
|
|
|
sentences:
|
|
|
- The woman is working on her computer at the desk.
|
|
|
- A young man holding an umbrella next to a herd of cattle.
|
|
|
- 'a person sitting at a desk with a keyboard and monitor '
|
|
|
- source_sentence: 'A car at an intersection while a man is crossing the street. '
|
|
|
sentences:
|
|
|
- A plane that is flying in the air.
|
|
|
- a small girl sitting on a chair holding a white bear
|
|
|
- A young toddler walks across the grass in a park.
|
|
|
- source_sentence: A lady riding her bicycle on the side of a street.
|
|
|
sentences:
|
|
|
- Flowers hang from a small decorative post in a yard.
|
|
|
- Flowers in a clear vase sitting on a table.
|
|
|
- The toilet is near the door in the bathroom.
|
|
|
- source_sentence: 'A group of zebras standing beside each other in the desert. '
|
|
|
sentences:
|
|
|
- The bathroom is clean and ready for us to use.
|
|
|
- A woman throwing a frisbee as a child looks on.
|
|
|
- a bird with a pink eye is sitting on a branch in the woods.
|
|
|
- source_sentence: A large desk by a window is neatly arranged.
|
|
|
sentences:
|
|
|
- An old toilet sits in dirt with a helmet on top.
|
|
|
- A lady sitting at an enormous dining table with lots of food.
|
|
|
- A long hot dog on a plate on a table.
|
|
|
datasets:
|
|
|
- jxie/coco_captions
|
|
|
pipeline_tag: sentence-similarity
|
|
|
library_name: sentence-transformers
|
|
|
metrics:
|
|
|
- cosine_accuracy@1
|
|
|
- cosine_accuracy@3
|
|
|
- cosine_accuracy@5
|
|
|
- cosine_accuracy@10
|
|
|
- cosine_precision@1
|
|
|
- cosine_precision@3
|
|
|
- cosine_precision@5
|
|
|
- cosine_precision@10
|
|
|
- cosine_recall@1
|
|
|
- cosine_recall@3
|
|
|
- cosine_recall@5
|
|
|
- cosine_recall@10
|
|
|
- cosine_ndcg@10
|
|
|
- cosine_mrr@10
|
|
|
- cosine_map@100
|
|
|
co2_eq_emissions:
|
|
|
emissions: 14.931381819429657
|
|
|
energy_consumed: 0.0557928041021652
|
|
|
source: codecarbon
|
|
|
training_type: fine-tuning
|
|
|
on_cloud: false
|
|
|
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
|
|
|
ram_total_size: 31.777088165283203
|
|
|
hours_used: 0.175
|
|
|
hardware_used: 1 x NVIDIA GeForce RTX 3090
|
|
|
model-index:
|
|
|
- name: Google SigLIP2 (512x512 resolution) model trained on COCO Captions
|
|
|
results:
|
|
|
- task:
|
|
|
type: information-retrieval
|
|
|
name: Information Retrieval
|
|
|
dataset:
|
|
|
name: coco eval
|
|
|
type: coco-eval
|
|
|
metrics:
|
|
|
- type: cosine_accuracy@1
|
|
|
value: 0.627
|
|
|
name: Cosine Accuracy@1
|
|
|
- type: cosine_accuracy@3
|
|
|
value: 0.892
|
|
|
name: Cosine Accuracy@3
|
|
|
- type: cosine_accuracy@5
|
|
|
value: 0.948
|
|
|
name: Cosine Accuracy@5
|
|
|
- type: cosine_accuracy@10
|
|
|
value: 0.98
|
|
|
name: Cosine Accuracy@10
|
|
|
- type: cosine_precision@1
|
|
|
value: 0.627
|
|
|
name: Cosine Precision@1
|
|
|
- type: cosine_precision@3
|
|
|
value: 0.2973333333333333
|
|
|
name: Cosine Precision@3
|
|
|
- type: cosine_precision@5
|
|
|
value: 0.18960000000000002
|
|
|
name: Cosine Precision@5
|
|
|
- type: cosine_precision@10
|
|
|
value: 0.09800000000000002
|
|
|
name: Cosine Precision@10
|
|
|
- type: cosine_recall@1
|
|
|
value: 0.627
|
|
|
name: Cosine Recall@1
|
|
|
- type: cosine_recall@3
|
|
|
value: 0.892
|
|
|
name: Cosine Recall@3
|
|
|
- type: cosine_recall@5
|
|
|
value: 0.948
|
|
|
name: Cosine Recall@5
|
|
|
- type: cosine_recall@10
|
|
|
value: 0.98
|
|
|
name: Cosine Recall@10
|
|
|
- type: cosine_ndcg@10
|
|
|
value: 0.8151978294992689
|
|
|
name: Cosine Ndcg@10
|
|
|
- type: cosine_mrr@10
|
|
|
value: 0.7606325396825407
|
|
|
name: Cosine Mrr@10
|
|
|
- type: cosine_map@100
|
|
|
value: 0.7613973675346634
|
|
|
name: Cosine Map@100
|
|
|
- task:
|
|
|
type: information-retrieval
|
|
|
name: Information Retrieval
|
|
|
dataset:
|
|
|
name: coco test
|
|
|
type: coco-test
|
|
|
metrics:
|
|
|
- type: cosine_accuracy@1
|
|
|
value: 0.65
|
|
|
name: Cosine Accuracy@1
|
|
|
- type: cosine_accuracy@3
|
|
|
value: 0.874
|
|
|
name: Cosine Accuracy@3
|
|
|
- type: cosine_accuracy@5
|
|
|
value: 0.939
|
|
|
name: Cosine Accuracy@5
|
|
|
- type: cosine_accuracy@10
|
|
|
value: 0.98
|
|
|
name: Cosine Accuracy@10
|
|
|
- type: cosine_precision@1
|
|
|
value: 0.65
|
|
|
name: Cosine Precision@1
|
|
|
- type: cosine_precision@3
|
|
|
value: 0.2913333333333333
|
|
|
name: Cosine Precision@3
|
|
|
- type: cosine_precision@5
|
|
|
value: 0.18780000000000002
|
|
|
name: Cosine Precision@5
|
|
|
- type: cosine_precision@10
|
|
|
value: 0.09800000000000002
|
|
|
name: Cosine Precision@10
|
|
|
- type: cosine_recall@1
|
|
|
value: 0.65
|
|
|
name: Cosine Recall@1
|
|
|
- type: cosine_recall@3
|
|
|
value: 0.874
|
|
|
name: Cosine Recall@3
|
|
|
- type: cosine_recall@5
|
|
|
value: 0.939
|
|
|
name: Cosine Recall@5
|
|
|
- type: cosine_recall@10
|
|
|
value: 0.98
|
|
|
name: Cosine Recall@10
|
|
|
- type: cosine_ndcg@10
|
|
|
value: 0.8236324365873424
|
|
|
name: Cosine Ndcg@10
|
|
|
- type: cosine_mrr@10
|
|
|
value: 0.772338095238096
|
|
|
name: Cosine Mrr@10
|
|
|
- type: cosine_map@100
|
|
|
value: 0.7730665827767428
|
|
|
name: Cosine Map@100
|
|
|
---
|
|
|
|
|
|
# Google SigLIP2 (512x512 resolution) model trained on COCO Captions
|
|
|
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/siglip2-base-patch16-512](https://huggingface.co/google/siglip2-base-patch16-512) on the [coco_captions](https://huggingface.co/datasets/jxie/coco_captions) dataset. It maps sentences & paragraphs to a None-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:** [google/siglip2-base-patch16-512](https://huggingface.co/google/siglip2-base-patch16-512) <!-- at revision a89f5c5093f902bf39d3cd4d81d2c09867f0724b -->
|
|
|
- **Maximum Sequence Length:** None tokens
|
|
|
- **Output Dimensionality:** None dimensions
|
|
|
- **Similarity Function:** Cosine Similarity
|
|
|
- **Training Dataset:**
|
|
|
- [coco_captions](https://huggingface.co/datasets/jxie/coco_captions)
|
|
|
- **Language:** en
|
|
|
- **License:** apache-2.0
|
|
|
|
|
|
### 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({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'get_text_features', 'method_output_name': None}, 'image': {'method': 'get_image_features', 'method_output_name': None}}, 'module_output_name': 'sentence_embedding', 'architecture': 'SiglipModel'})
|
|
|
)
|
|
|
```
|
|
|
|
|
|
## 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("tomaarsen/google-siglip2-base-512-coco")
|
|
|
# Run inference
|
|
|
sentences = [
|
|
|
'A large desk by a window is neatly arranged.',
|
|
|
'A long hot dog on a plate on a table.',
|
|
|
'A lady sitting at an enormous dining table with lots of food.',
|
|
|
]
|
|
|
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, 0.1125, 0.2752],
|
|
|
# [0.1125, 1.0000, 0.3446],
|
|
|
# [0.2752, 0.3446, 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.*
|
|
|
-->
|
|
|
|
|
|
## Evaluation
|
|
|
|
|
|
### Metrics
|
|
|
|
|
|
#### Information Retrieval
|
|
|
|
|
|
* Datasets: `coco-eval` and `coco-test`
|
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
|
|
|
|
|
| Metric | coco-eval | coco-test |
|
|
|
|:--------------------|:-----------|:-----------|
|
|
|
| cosine_accuracy@1 | 0.627 | 0.65 |
|
|
|
| cosine_accuracy@3 | 0.892 | 0.874 |
|
|
|
| cosine_accuracy@5 | 0.948 | 0.939 |
|
|
|
| cosine_accuracy@10 | 0.98 | 0.98 |
|
|
|
| cosine_precision@1 | 0.627 | 0.65 |
|
|
|
| cosine_precision@3 | 0.2973 | 0.2913 |
|
|
|
| cosine_precision@5 | 0.1896 | 0.1878 |
|
|
|
| cosine_precision@10 | 0.098 | 0.098 |
|
|
|
| cosine_recall@1 | 0.627 | 0.65 |
|
|
|
| cosine_recall@3 | 0.892 | 0.874 |
|
|
|
| cosine_recall@5 | 0.948 | 0.939 |
|
|
|
| cosine_recall@10 | 0.98 | 0.98 |
|
|
|
| **cosine_ndcg@10** | **0.8152** | **0.8236** |
|
|
|
| cosine_mrr@10 | 0.7606 | 0.7723 |
|
|
|
| cosine_map@100 | 0.7614 | 0.7731 |
|
|
|
|
|
|
<!--
|
|
|
## 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
|
|
|
|
|
|
#### coco_captions
|
|
|
|
|
|
* Dataset: [coco_captions](https://huggingface.co/datasets/jxie/coco_captions) at [a2ed90d](https://huggingface.co/datasets/jxie/coco_captions/tree/a2ed90d49b61dd13dd71f399c70f5feb897f8bec)
|
|
|
* Size: 10,000 training samples
|
|
|
* Columns: <code>image</code> and <code>caption</code>
|
|
|
* Approximate statistics based on the first 1000 samples:
|
|
|
| | image | caption |
|
|
|
|:--------|:----------------------------------|:------------------------------------------------------------------------------------------------|
|
|
|
| type | PIL.JpegImagePlugin.JpegImageFile | string |
|
|
|
| details | <ul><li></li></ul> | <ul><li>min: 28 characters</li><li>mean: 52.56 characters</li><li>max: 156 characters</li></ul> |
|
|
|
* Samples:
|
|
|
| image | caption |
|
|
|
|:----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------|
|
|
|
| <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x20DAA211010></code> | <code>A woman wearing a net on her head cutting a cake. </code> |
|
|
|
| <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x226133D1850></code> | <code>A woman cutting a large white sheet cake.</code> |
|
|
|
| <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x226133D2050></code> | <code>A woman wearing a hair net cutting a large sheet cake.</code> |
|
|
|
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
|
|
|
```json
|
|
|
{
|
|
|
"scale": 20.0,
|
|
|
"similarity_fct": "cos_sim",
|
|
|
"gather_across_devices": false
|
|
|
}
|
|
|
```
|
|
|
|
|
|
### Evaluation Dataset
|
|
|
|
|
|
#### coco_captions
|
|
|
|
|
|
* Dataset: [coco_captions](https://huggingface.co/datasets/jxie/coco_captions) at [a2ed90d](https://huggingface.co/datasets/jxie/coco_captions/tree/a2ed90d49b61dd13dd71f399c70f5feb897f8bec)
|
|
|
* Size: 1,000 evaluation samples
|
|
|
* Columns: <code>image</code> and <code>caption</code>
|
|
|
* Approximate statistics based on the first 1000 samples:
|
|
|
| | image | caption |
|
|
|
|:--------|:----------------------------------|:------------------------------------------------------------------------------------------------|
|
|
|
| type | PIL.JpegImagePlugin.JpegImageFile | string |
|
|
|
| details | <ul><li></li></ul> | <ul><li>min: 27 characters</li><li>mean: 52.45 characters</li><li>max: 151 characters</li></ul> |
|
|
|
* Samples:
|
|
|
| image | caption |
|
|
|
|:----------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
|
|
|
| <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x336 at 0x2261341DF50></code> | <code>A child holding a flowered umbrella and petting a yak.</code> |
|
|
|
| <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x336 at 0x20D01377750></code> | <code>A young man holding an umbrella next to a herd of cattle.</code> |
|
|
|
| <code><PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x336 at 0x226133D2110></code> | <code>a young boy barefoot holding an umbrella touching the horn of a cow</code> |
|
|
|
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
|
|
|
```json
|
|
|
{
|
|
|
"scale": 20.0,
|
|
|
"similarity_fct": "cos_sim",
|
|
|
"gather_across_devices": false
|
|
|
}
|
|
|
```
|
|
|
|
|
|
### Training Hyperparameters
|
|
|
#### Non-Default Hyperparameters
|
|
|
|
|
|
- `eval_strategy`: steps
|
|
|
- `per_device_train_batch_size`: 16
|
|
|
- `per_device_eval_batch_size`: 16
|
|
|
- `learning_rate`: 2e-05
|
|
|
- `num_train_epochs`: 1
|
|
|
- `warmup_ratio`: 0.1
|
|
|
- `bf16`: True
|
|
|
- `batch_sampler`: no_duplicates
|
|
|
|
|
|
#### 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`: 16
|
|
|
- `per_device_eval_batch_size`: 16
|
|
|
- `gradient_accumulation_steps`: 1
|
|
|
- `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`: 1
|
|
|
- `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
|
|
|
- `use_cpu`: False
|
|
|
- `seed`: 42
|
|
|
- `data_seed`: None
|
|
|
- `jit_mode_eval`: False
|
|
|
- `bf16`: True
|
|
|
- `fp16`: False
|
|
|
- `half_precision_backend`: None
|
|
|
- `bf16_full_eval`: False
|
|
|
- `fp16_full_eval`: False
|
|
|
- `tf32`: None
|
|
|
- `local_rank`: 0
|
|
|
- `ddp_backend`: None
|
|
|
- `tpu_num_cores`: None
|
|
|
- `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_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
|
|
- `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
|
|
|
- `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_for_metrics`: []
|
|
|
- `eval_do_concat_batches`: True
|
|
|
- `mp_parameters`:
|
|
|
- `auto_find_batch_size`: False
|
|
|
- `full_determinism`: False
|
|
|
- `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`: no
|
|
|
- `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`: True
|
|
|
- `prompts`: None
|
|
|
- `batch_sampler`: no_duplicates
|
|
|
- `multi_dataset_batch_sampler`: proportional
|
|
|
- `router_mapping`: {}
|
|
|
- `learning_rate_mapping`: {}
|
|
|
|
|
|
</details>
|
|
|
|
|
|
### Training Logs
|
|
|
| Epoch | Step | Training Loss | Validation Loss | coco-eval_cosine_ndcg@10 | coco-test_cosine_ndcg@10 |
|
|
|
|:------:|:----:|:-------------:|:---------------:|:------------------------:|:------------------------:|
|
|
|
| -1 | -1 | - | - | 0.1900 | - |
|
|
|
| 0.0112 | 7 | 2.4513 | - | - | - |
|
|
|
| 0.0224 | 14 | 2.4538 | - | - | - |
|
|
|
| 0.0336 | 21 | 2.4508 | - | - | - |
|
|
|
| 0.0448 | 28 | 2.3998 | - | - | - |
|
|
|
| 0.056 | 35 | 2.3021 | - | - | - |
|
|
|
| 0.0672 | 42 | 2.2435 | - | - | - |
|
|
|
| 0.0784 | 49 | 1.5821 | - | - | - |
|
|
|
| 0.0896 | 56 | 1.5381 | - | - | - |
|
|
|
| 0.1008 | 63 | 1.652 | 1.4376 | 0.3890 | - |
|
|
|
| 0.112 | 70 | 1.175 | - | - | - |
|
|
|
| 0.1232 | 77 | 1.2468 | - | - | - |
|
|
|
| 0.1344 | 84 | 0.9952 | - | - | - |
|
|
|
| 0.1456 | 91 | 0.9492 | - | - | - |
|
|
|
| 0.1568 | 98 | 0.9434 | - | - | - |
|
|
|
| 0.168 | 105 | 0.8598 | - | - | - |
|
|
|
| 0.1792 | 112 | 0.6562 | - | - | - |
|
|
|
| 0.1904 | 119 | 0.8115 | - | - | - |
|
|
|
| 0.2016 | 126 | 0.5666 | 0.6434 | 0.6437 | - |
|
|
|
| 0.2128 | 133 | 0.4335 | - | - | - |
|
|
|
| 0.224 | 140 | 0.3687 | - | - | - |
|
|
|
| 0.2352 | 147 | 0.52 | - | - | - |
|
|
|
| 0.2464 | 154 | 0.5098 | - | - | - |
|
|
|
| 0.2576 | 161 | 0.5707 | - | - | - |
|
|
|
| 0.2688 | 168 | 0.5349 | - | - | - |
|
|
|
| 0.28 | 175 | 0.5768 | - | - | - |
|
|
|
| 0.2912 | 182 | 0.3053 | - | - | - |
|
|
|
| 0.3024 | 189 | 0.293 | 0.4603 | 0.6986 | - |
|
|
|
| 0.3136 | 196 | 0.4413 | - | - | - |
|
|
|
| 0.3248 | 203 | 0.4706 | - | - | - |
|
|
|
| 0.336 | 210 | 0.3618 | - | - | - |
|
|
|
| 0.3472 | 217 | 0.4465 | - | - | - |
|
|
|
| 0.3584 | 224 | 0.3968 | - | - | - |
|
|
|
| 0.3696 | 231 | 0.3413 | - | - | - |
|
|
|
| 0.3808 | 238 | 0.3039 | - | - | - |
|
|
|
| 0.392 | 245 | 0.2978 | - | - | - |
|
|
|
| 0.4032 | 252 | 0.4956 | 0.3713 | 0.7410 | - |
|
|
|
| 0.4144 | 259 | 0.3441 | - | - | - |
|
|
|
| 0.4256 | 266 | 0.4604 | - | - | - |
|
|
|
| 0.4368 | 273 | 0.4723 | - | - | - |
|
|
|
| 0.448 | 280 | 0.2957 | - | - | - |
|
|
|
| 0.4592 | 287 | 0.262 | - | - | - |
|
|
|
| 0.4704 | 294 | 0.2205 | - | - | - |
|
|
|
| 0.4816 | 301 | 0.2546 | - | - | - |
|
|
|
| 0.4928 | 308 | 0.3489 | - | - | - |
|
|
|
| 0.504 | 315 | 0.2028 | 0.3304 | 0.7648 | - |
|
|
|
| 0.5152 | 322 | 0.2659 | - | - | - |
|
|
|
| 0.5264 | 329 | 0.356 | - | - | - |
|
|
|
| 0.5376 | 336 | 0.3322 | - | - | - |
|
|
|
| 0.5488 | 343 | 0.1783 | - | - | - |
|
|
|
| 0.56 | 350 | 0.3221 | - | - | - |
|
|
|
| 0.5712 | 357 | 0.2213 | - | - | - |
|
|
|
| 0.5824 | 364 | 0.303 | - | - | - |
|
|
|
| 0.5936 | 371 | 0.2349 | - | - | - |
|
|
|
| 0.6048 | 378 | 0.309 | 0.2987 | 0.7756 | - |
|
|
|
| 0.616 | 385 | 0.2494 | - | - | - |
|
|
|
| 0.6272 | 392 | 0.1605 | - | - | - |
|
|
|
| 0.6384 | 399 | 0.21 | - | - | - |
|
|
|
| 0.6496 | 406 | 0.1258 | - | - | - |
|
|
|
| 0.6608 | 413 | 0.2092 | - | - | - |
|
|
|
| 0.672 | 420 | 0.2701 | - | - | - |
|
|
|
| 0.6832 | 427 | 0.181 | - | - | - |
|
|
|
| 0.6944 | 434 | 0.2653 | - | - | - |
|
|
|
| 0.7056 | 441 | 0.3197 | 0.2817 | 0.7883 | - |
|
|
|
| 0.7168 | 448 | 0.2991 | - | - | - |
|
|
|
| 0.728 | 455 | 0.175 | - | - | - |
|
|
|
| 0.7392 | 462 | 0.159 | - | - | - |
|
|
|
| 0.7504 | 469 | 0.1689 | - | - | - |
|
|
|
| 0.7616 | 476 | 0.2212 | - | - | - |
|
|
|
| 0.7728 | 483 | 0.1601 | - | - | - |
|
|
|
| 0.784 | 490 | 0.1509 | - | - | - |
|
|
|
| 0.7952 | 497 | 0.2051 | - | - | - |
|
|
|
| 0.8064 | 504 | 0.2972 | 0.2615 | 0.8024 | - |
|
|
|
| 0.8176 | 511 | 0.1601 | - | - | - |
|
|
|
| 0.8288 | 518 | 0.2681 | - | - | - |
|
|
|
| 0.84 | 525 | 0.1635 | - | - | - |
|
|
|
| 0.8512 | 532 | 0.1868 | - | - | - |
|
|
|
| 0.8624 | 539 | 0.1284 | - | - | - |
|
|
|
| 0.8736 | 546 | 0.1414 | - | - | - |
|
|
|
| 0.8848 | 553 | 0.1474 | - | - | - |
|
|
|
| 0.896 | 560 | 0.1482 | - | - | - |
|
|
|
| 0.9072 | 567 | 0.1718 | 0.2459 | 0.8152 | - |
|
|
|
| 0.9184 | 574 | 0.1705 | - | - | - |
|
|
|
| 0.9296 | 581 | 0.1761 | - | - | - |
|
|
|
| 0.9408 | 588 | 0.232 | - | - | - |
|
|
|
| 0.952 | 595 | 0.1619 | - | - | - |
|
|
|
| 0.9632 | 602 | 0.1088 | - | - | - |
|
|
|
| 0.9744 | 609 | 0.1874 | - | - | - |
|
|
|
| 0.9856 | 616 | 0.1502 | - | - | - |
|
|
|
| 0.9968 | 623 | 0.22 | - | - | - |
|
|
|
| -1 | -1 | - | - | - | 0.8236 |
|
|
|
|
|
|
|
|
|
### Environmental Impact
|
|
|
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
|
|
|
- **Energy Consumed**: 0.056 kWh
|
|
|
- **Carbon Emitted**: 0.015 kg of CO2
|
|
|
- **Hours Used**: 0.175 hours
|
|
|
|
|
|
### Training Hardware
|
|
|
- **On Cloud**: No
|
|
|
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
|
|
|
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
|
|
|
- **RAM Size**: 31.78 GB
|
|
|
|
|
|
### Framework Versions
|
|
|
- Python: 3.11.6
|
|
|
- Sentence Transformers: 5.2.0.dev0
|
|
|
- Transformers: 4.57.0.dev0
|
|
|
- PyTorch: 2.8.0+cu128
|
|
|
- Accelerate: 1.6.0
|
|
|
- Datasets: 3.6.0
|
|
|
- Tokenizers: 0.22.1
|
|
|
|
|
|
## 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",
|
|
|
}
|
|
|
```
|
|
|
|
|
|
#### MultipleNegativesRankingLoss
|
|
|
```bibtex
|
|
|
@misc{henderson2017efficient,
|
|
|
title={Efficient Natural Language Response Suggestion for Smart Reply},
|
|
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
|
|
|
year={2017},
|
|
|
eprint={1705.00652},
|
|
|
archivePrefix={arXiv},
|
|
|
primaryClass={cs.CL}
|
|
|
}
|
|
|
```
|
|
|
|
|
|
<!--
|
|
|
## 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.*
|
|
|
--> |
