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spectrum-doc-fine-tuned

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:14737
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
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---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:14737
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-large-en-v1.5
widget:
- source_sentence: >-
Represent this sentence for searching relevant passages: What are some best
practices for ensuring images in horizontal cards are visually appealing
despite being cropped to fit a square format?
sentences:
- >
Tree view
Usage guidelines
Horizontal scrolling: If you have a layout that doesn't allow for users to
adjust the width of the container for a tree view, allow them to
horizontally scroll in order to see the full depth of the hierarchy.
Do: Allow horizontal scrolling in a fixed layout.
- >-
Cards
Options
Vertical or horizontal : Standard cards can be laid out vertically
(components are organized in a column) or horizontally (components are
organized in a row).
Horizontal cards always have a square preview, and the image is cropped to
fit inside the square. These can only be laid out in a tile grid where every
card is the same size.
- >-
Alert dialog
Behaviors
Button group overflow: An alert dialog can have up to 3 buttons. When
horizontal space is limited, button groups stack vertically. They should
appear in ascending order based on importance, with the most critical action
at the bottom.
- source_sentence: >-
Represent this sentence for searching relevant passages: Are there any
guidelines for the timing and smoothness of the fading effect when hovering
over a segment in a donut chart?
sentences:
- >-
Color for data visualization
Usage guidelines
Categorical colors are not ordered. Use these for categorical scales. Do not
use these for ordinal, interval, or ratio scales.
Sequential colors are ordered. Use these for ordinal and interval scales.
It’s also acceptable to use these for ratio scales. Do not use these for
categorical scales.
Diverging colors are ordered. Use these for ordinal and ratio scales,
especially when there is a meaningful middle value. These may also be used
for interval scales. Do not use these for categorical scales.
- >-
Action group
Options
Density: Action groups come in 2 densities: regular and compact. The compact
density retains the same font and icon sizes, but has tighter spacing. The
action buttons also become connected for non-quiet action groups.
- >-
Donut chart
Behaviors
Hover: Hovering over a segment of a donut chart causes all other segments to
fade back from the view. A tooltip displays the segment name, percentage of
total, and metric value.
- source_sentence: >-
Represent this sentence for searching relevant passages: Why is it important
to orient the legend to match the chart whenever possible?
sentences:
- >-
Breadcrumbs
Options
Multiline: The multiline variation places emphasis on the selected
breadcrumb item as a page title, helping a user to more clearly identify
their current location.
- >-
Cards
Layout
Card width: Cards are laid out in either a fluid card grid or have fixed
widths. Most cards can be organized within a grid where the width of each
card is fluid depending on the nature of the grid. In rare cases where cards
can’t be laid out in a card grid, they’ll have a fixed width that is defined
manually.
- >-
Legend
Options
Orientation: Legends can have horizontal or vertical orientation. Whenever
possible, orient the legend to match the chart.
- source_sentence: >-
Represent this sentence for searching relevant passages: What is the primary
use case for radio buttons according to the Adobe Spectrum Design
Documentation?
sentences:
- >+
Radio group
Usage guidelines
Use radio buttons for mutually exclusive options: Radio buttons and
[checkboxes](/page/checkbox) are not interchangeable. Radio buttons are best
used for selecting a single option from a list of mutually exclusive
options. Checkboxes are best used for selecting multiple options at once (or
no options).
- >
Additional resources: - [Human Interface Guidelines: iOS Tab
Bars](https://developer.apple.com/design/human-interface-guidelines/ios/bars/tab-bars/)
- [Human Interface Guidelines:
Accessibility](https://developer.apple.com/design/human-interface-guidelines/accessibility/overview/introduction/)
- >-
Picker
Options
Label position: Labels can be placed either on top or on the side. Top
labels are the default and are recommended because they work better with
long copy, localization, and responsive layouts. Side labels are most useful
when vertical space is limited.
- source_sentence: >-
Represent this sentence for searching relevant passages: How can a designer
balance the need for clear text links and the need for emphasized text in a
user interface?
sentences:
- >-
Meter
Options
Positive variant: The positive variant has a green fill to show the value.
This can be used to represent a positive semantic value, such as when
there’s a lot of space remaining.
- >-
Badge
Options
Size: Badges come in four different sizes: small, medium, large, and
extra-large. The small size is the default and most frequently used option.
Use the other sizes sparingly to create a hierarchy of importance on a page.
- >+
Typography
Usage guidelines
Don't use underlines for adding emphasis: Underlines are reserved for text
links only. They should not be used as a way for adding emphasis to words.
datasets:
- JianLiao/spectrum-design-docs
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
model-index:
- name: SentenceTransformer based on BAAI/bge-large-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: sds
type: sds
metrics:
- type: cosine_accuracy@1
value: 0.007462686567164179
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.015603799185888738
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.04748982360922659
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7815468113975577
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.007462686567164179
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.005201266395296246
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.009497964721845319
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07815468113975575
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.007462686567164179
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.015603799185888738
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.04748982360922659
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7815468113975577
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.25440066233238845
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.10778547737502948
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.11639203259428242
name: Cosine Map@100
license: mit
---
# SentenceTransformer based on BAAI/bge-large-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) on the [spectrum-design-docs](https://huggingface.co/datasets/JianLiao/spectrum-design-docs) dataset. 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:** [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [spectrum-design-docs](https://huggingface.co/datasets/JianLiao/spectrum-design-docs)
- **Language:** en
<!-- - **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': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("JianLiao/spectrum-doc-fine-tuned")
# Run inference
sentences = [
'Represent this sentence for searching relevant passages: How can a designer balance the need for clear text links and the need for emphasized text in a user interface?',
"Typography\nUsage guidelines\nDon't use underlines for adding emphasis: Underlines are reserved for text links only. They should not be used as a way for adding emphasis to words.\n\n",
'Meter\nOptions\nPositive variant: The positive variant has a green fill to show the value. This can be used to represent a positive semantic value, such as when there’s a lot of space remaining.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### 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
* Dataset: `sds`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.0075 |
| cosine_accuracy@3 | 0.0156 |
| cosine_accuracy@5 | 0.0475 |
| cosine_accuracy@10 | 0.7815 |
| cosine_precision@1 | 0.0075 |
| cosine_precision@3 | 0.0052 |
| cosine_precision@5 | 0.0095 |
| cosine_precision@10 | 0.0782 |
| cosine_recall@1 | 0.0075 |
| cosine_recall@3 | 0.0156 |
| cosine_recall@5 | 0.0475 |
| cosine_recall@10 | 0.7815 |
| **cosine_ndcg@10** | **0.2544** |
| cosine_mrr@10 | 0.1078 |
| cosine_map@100 | 0.1164 |
<!--
## 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
#### spectrum-design-docs
* Dataset: [spectrum-design-docs](https://huggingface.co/datasets/JianLiao/spectrum-design-docs) at [23f5565](https://huggingface.co/datasets/JianLiao/spectrum-design-docs/tree/23f5565f9fc1cfe31d1245ca9e5368f00fcaec00)
* Size: 14,737 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 20 tokens</li><li>mean: 30.87 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 97.17 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Represent this sentence for searching relevant passages: Are there any specific guidelines or best practices provided by the Spectrum team for integrating Spectrum CSS into a new or existing project?</code> | <code>Spectrum CSS: An open source CSS-only implementation of Spectrum, maintained by the Spectrum team. <br><div class="well-box">Dependency chain: Spectrum DNA → Spectrum CSS</div><br><br>[GitHub repository](https://github.com/adobe/spectrum-css/) <br>[Website](https://opensource.adobe.com/spectrum-css/) <br>[#spectrum_css](https://adobe.slack.com/archives/C5N154FEY)</code> |
| <code>Represent this sentence for searching relevant passages: How does the default setting for progress circles affect their behavior in a UI?</code> | <code>Progress circle<br>Options<br>Indeterminate: A progress circle can be either determinate or indeterminate. By default, progress circles are determinate. Use a determinate progress circle when progress can be calculated against a specific goal (e.g., downloading a file of a known size). Use an indeterminate progress circle when progress is happening but the time or effort to completion can’t be determined (e.g., attempting to reconnect to a server).</code> |
| <code>Represent this sentence for searching relevant passages: What tools or methods can designers use to test the effectiveness of wrapped legends in their designs?</code> | <code>Legend<br>Behaviors<br>Wrapping: When there isn’t enough space, wrap legends to ensure that dimension values are shown.</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"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 22
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 100
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `prompts`: {'anchor': 'Represent this sentence for searching relevant passages: '}
- `batch_sampler`: no_duplicates
#### 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`: 22
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `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`: 100
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `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`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `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`: True
- `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_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
- `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
- `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
- `average_tokens_across_devices`: False
- `prompts`: {'anchor': 'Represent this sentence for searching relevant passages: '}
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | sds_cosine_ndcg@10 |
|:--------:|:-------:|:-------------:|:------------------:|
| 1.0 | 7 | - | 0.2255 |
| 1.48 | 10 | 0.2646 | - |
| 2.0 | 14 | - | 0.2282 |
| 2.96 | 20 | 0.1412 | - |
| 3.0 | 21 | - | 0.2358 |
| 4.0 | 28 | - | 0.2397 |
| 4.32 | 30 | 0.0638 | - |
| 5.0 | 35 | - | 0.2430 |
| 5.8 | 40 | 0.0425 | - |
| 6.0 | 42 | - | 0.2449 |
| 7.0 | 49 | - | 0.2462 |
| 7.16 | 50 | 0.0237 | - |
| 8.0 | 56 | - | 0.2428 |
| 8.64 | 60 | 0.015 | - |
| 9.0 | 63 | - | 0.2456 |
| 10.0 | 70 | 0.0082 | 0.2456 |
| 11.0 | 77 | - | 0.2498 |
| 11.48 | 80 | 0.0052 | - |
| 12.0 | 84 | - | 0.2474 |
| 12.96 | 90 | 0.0035 | - |
| 13.0 | 91 | - | 0.2455 |
| 14.0 | 98 | - | 0.2475 |
| 14.32 | 100 | 0.0022 | - |
| 15.0 | 105 | - | 0.2472 |
| 15.8 | 110 | 0.002 | - |
| 16.0 | 112 | - | 0.2486 |
| 17.0 | 119 | - | 0.2506 |
| 17.16 | 120 | 0.0015 | - |
| 18.0 | 126 | - | 0.2490 |
| 18.64 | 130 | 0.0013 | - |
| 19.0 | 133 | - | 0.2489 |
| 20.0 | 140 | 0.0012 | 0.2491 |
| 21.0 | 147 | - | 0.2493 |
| 21.48 | 150 | 0.0011 | - |
| 22.0 | 154 | - | 0.2487 |
| 22.96 | 160 | 0.001 | - |
| 23.0 | 161 | - | 0.2486 |
| 24.0 | 168 | - | 0.2490 |
| 24.32 | 170 | 0.0008 | - |
| 25.0 | 175 | - | 0.2502 |
| 25.8 | 180 | 0.0008 | - |
| 26.0 | 182 | - | 0.2505 |
| 27.0 | 189 | - | 0.2523 |
| 27.16 | 190 | 0.0008 | - |
| 28.0 | 196 | - | 0.2516 |
| 28.64 | 200 | 0.0007 | - |
| 29.0 | 203 | - | 0.2509 |
| 30.0 | 210 | 0.0007 | 0.2522 |
| 31.0 | 217 | - | 0.2522 |
| 31.48 | 220 | 0.0006 | - |
| 32.0 | 224 | - | 0.2534 |
| 32.96 | 230 | 0.0007 | - |
| 33.0 | 231 | - | 0.2523 |
| 34.0 | 238 | - | 0.2524 |
| 34.32 | 240 | 0.0006 | - |
| 35.0 | 245 | - | 0.2518 |
| 35.8 | 250 | 0.0006 | - |
| 36.0 | 252 | - | 0.2529 |
| 37.0 | 259 | - | 0.2524 |
| 37.16 | 260 | 0.0006 | - |
| 38.0 | 266 | - | 0.2530 |
| 38.64 | 270 | 0.0005 | - |
| 39.0 | 273 | - | 0.2526 |
| 40.0 | 280 | 0.0006 | 0.2539 |
| 41.0 | 287 | - | 0.2529 |
| 41.48 | 290 | 0.0005 | - |
| 42.0 | 294 | - | 0.2545 |
| 42.96 | 300 | 0.0006 | - |
| 43.0 | 301 | - | 0.2534 |
| 44.0 | 308 | - | 0.2536 |
| 44.32 | 310 | 0.0004 | - |
| 45.0 | 315 | - | 0.2521 |
| 45.8 | 320 | 0.0005 | - |
| 46.0 | 322 | - | 0.2532 |
| 47.0 | 329 | - | 0.2519 |
| 47.16 | 330 | 0.0005 | - |
| 48.0 | 336 | - | 0.2525 |
| 48.64 | 340 | 0.0004 | - |
| 49.0 | 343 | - | 0.2535 |
| 50.0 | 350 | 0.0005 | 0.2542 |
| 51.0 | 357 | - | 0.2540 |
| 51.48 | 360 | 0.0004 | - |
| 52.0 | 364 | - | 0.2542 |
| 52.96 | 370 | 0.0005 | - |
| 53.0 | 371 | - | 0.2538 |
| 54.0 | 378 | - | 0.2533 |
| 54.32 | 380 | 0.0004 | - |
| 55.0 | 385 | - | 0.2544 |
| 55.8 | 390 | 0.0004 | - |
| 56.0 | 392 | - | 0.2539 |
| 57.0 | 399 | - | 0.2541 |
| 57.16 | 400 | 0.0005 | - |
| 58.0 | 406 | - | 0.2532 |
| 58.64 | 410 | 0.0004 | - |
| 59.0 | 413 | - | 0.2543 |
| 60.0 | 420 | 0.0004 | 0.2532 |
| 61.0 | 427 | - | 0.2541 |
| 61.48 | 430 | 0.0004 | - |
| 62.0 | 434 | - | 0.2542 |
| 62.96 | 440 | 0.0005 | - |
| 63.0 | 441 | - | 0.2546 |
| 64.0 | 448 | - | 0.2549 |
| 64.32 | 450 | 0.0003 | - |
| **65.0** | **455** | **-** | **0.2557** |
| 65.8 | 460 | 0.0004 | - |
| 66.0 | 462 | - | 0.2557 |
| 67.0 | 469 | - | 0.2539 |
| 67.16 | 470 | 0.0004 | - |
| 68.0 | 476 | - | 0.2538 |
| 68.64 | 480 | 0.0004 | - |
| 69.0 | 483 | - | 0.2538 |
| 70.0 | 490 | 0.0004 | 0.2542 |
| 71.0 | 497 | - | 0.2532 |
| 71.48 | 500 | 0.0004 | - |
| 72.0 | 504 | - | 0.2538 |
| 72.96 | 510 | 0.0004 | - |
| 73.0 | 511 | - | 0.2545 |
| 74.0 | 518 | - | 0.2531 |
| 74.32 | 520 | 0.0003 | - |
| 75.0 | 525 | - | 0.2534 |
| 75.8 | 530 | 0.0004 | - |
| 76.0 | 532 | - | 0.2541 |
| 77.0 | 539 | - | 0.2545 |
| 77.16 | 540 | 0.0004 | - |
| 78.0 | 546 | - | 0.2536 |
| 78.64 | 550 | 0.0004 | - |
| 79.0 | 553 | - | 0.2545 |
| 80.0 | 560 | 0.0004 | 0.2540 |
| 81.0 | 567 | - | 0.2545 |
| 81.48 | 570 | 0.0004 | - |
| 82.0 | 574 | - | 0.2541 |
| 82.96 | 580 | 0.0004 | - |
| 83.0 | 581 | - | 0.2545 |
| 84.0 | 588 | - | 0.2538 |
| 84.32 | 590 | 0.0004 | - |
| 85.0 | 595 | - | 0.2546 |
| 85.8 | 600 | 0.0004 | 0.2544 |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.12.8
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu124
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## 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",
}
```
#### 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}
}
```
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