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foodex-facet-descriptors-retriever

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:1225740
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
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foodex-facet-descriptors-retriever / README.md
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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1225740
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-m3
widget:
- source_sentence: yoghurt cow chocolate chip sugar reduced half skimmed in plastic
container commercial supermarket shop organic shop </s> This facet allows recording
the place where the food was prepared for consumption. Only one descriptor from
this facet can be added to each entry.
sentences:
- Product obtained during the processing of screened, dehusked barley into pearl
barley, semolina or flour. It consists principally of particles of endosperm with
fine fragments of the outer skins and some grain screenings.
- Produced by industry in the form it arrives to the final consumer
- Tree nuts from the plant classified under the species Corylus avellana L., commonly
known as Hazelnuts or Cobnuts or Common hazelnut. The part consumed/analysed is
not specified. When relevant, information on the part consumed/analysed has to
be reported with additional facet descriptors. In case of data collections related
to legislations, the default part consumed/analysed is the one defined in the
applicable legislation.
- source_sentence: sauce cold liquid preservation method onion mint croutons sweet
pepper prepared at a restaurant </s> This facet collects ingredients and/or flavour
note. Regarding ingredients this facet serves the purpose of providing information
on ingredients of a composite food being important from some point of view, like
allergic reactions, hazards, but also aspect, taste. The descriptors for this
facet are taken from a selected subset of the main list (actually a relevant part
of the food list). More (none contradicting) descriptors can be applied to each
entry.
sentences:
- Spices from the fruits of the plant classified under the species Piper cubeba
L. f., commonly known as Cubeb fruit or Tailed pepper. The part consumed/analysed
is not specified. When relevant, information on the part consumed/analysed has
to be reported with additional facet descriptors. In case of data collections
related to legislations, the default part consumed/analysed is the one defined
in the applicable legislation.
- Tree nuts from the plant classified under the genus Juglans L. spp., commonly
known as Walnuts or Walnut Black or Walnut English or Walnut Persian. The part
consumed/analysed is not specified. When relevant, information on the part consumed/analysed
has to be reported with additional facet descriptors. In case of data collections
related to legislations, the default part consumed/analysed is the one defined
in the applicable legislation.
- Fruiting vegetables from the plant classified under the species Capsicum annuum
var. grossum (L.) Sendtner or Capsicum annuum var. longum Bailey, commonly known
as Sweet peppers or Bell peppers or Paprika or PeppersLong or Pimento or Pimiento.
The part consumed/analysed is not specified. When relevant, information on the
part consumed/analysed has to be reported with additional facet descriptors. In
case of data collections related to legislations, the default part consumed/analysed
is the one defined in the applicable legislation.
- source_sentence: yoghurt with fruits cow passion fruit sweetened with sugar sucrose
fat content in plastic container commercial supermarket shop organic shop </s>
This facet provides some principal claims related to important nutrients-ingredients,
like fat, sugar etc. It is not intended to include health claims or similar. The
present guidance provides a limited list, to be eventually improved during the
evolution of the system. More than one descriptor can be applied to each entry,
provided they are not contradicting each other.
sentences:
- Product where all or part of the sugar has been added during processing and is
not naturally contained
- Infusion materials from flowers of the plant classified under the genus Rosa L.
spp., commonly known as Rose infusion flowers. The part consumed/analysed is not
specified. When relevant, information on the part consumed/analysed has to be
reported with additional facet descriptors. In case of data collections related
to legislations, the default part consumed/analysed is the one defined in the
applicable legislation.
- Molecules providing intensive sweet sensation, used to substitute natural sugars
in food formulas
- source_sentence: pepper sweet green facets desc physical state form as quantified
grated cooking method stir fried sauted preservation method fresh </s> This facet
describes the form (physical aspect) of the food as reported by the consumer (as
estimated during interview or as registered in the diary) (Consumption Data) or
as expressed in the analysis results in the laboratory (Occurrence Data). Only
one descriptor from this facet can be added to each entry, apart from the specification
“with solid particles”. This facet should only be used in case of raw foods and
ingredients (not for composite foods).
sentences:
- Unprocessed and not stored over any long period
- Paste coarsely divided, where particles are still recognisable at naked eye
- The food item is considered in its form with skin
- source_sentence: tome des bauges raw milk aoc in plastic container brand product
name </s> This facet allows recording whether the food list code was chosen because
of lack of information on the food item or because the proper entry in the food
list was missing. Only one descriptor from this facet can be added to each entry.
sentences:
- The food list item has been chosen because none of the more detailed items corresponded
to the available information. Please consider the eventual addition of a new term
in the list
- The food item has a fat content which, when rounded with the standard rules of
rounding, equals 25 % (weight/weight)
- 'Deprecated term that must NOT be used for any purpose. Its original scopenote
was: The group includes any type of Other fruiting vegetables (exposure). The
part consumed/analysed is by default unspecified. When relevant, information on
the part consumed/analysed has to be reported with additional facet descriptors.'
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-m3
results:
- task:
type: device-aware-information-retrieval
name: Device Aware Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.9849655460430152
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9989559406974317
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9997911881394863
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9849655460430152
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.41713649335282244
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.25370641052411774
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.12752140321570266
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8690666019440294
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.993924343214383
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.998536283094646
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9999462151268373
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9936056206465634
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9919155008004455
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9909164791232326
name: Cosine Map@100
---
# SentenceTransformer based on BAAI/bge-m3
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). 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-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
- **Maximum Sequence Length:** 96 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': 96, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(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})
)
```
## 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("disi-unibo-nlp/foodex-facet-descriptors-retriever")
# Run inference
sentences = [
'tome des bauges raw milk aoc in plastic container brand product name </s> This facet allows recording whether the food list code was chosen because of lack of information on the food item or because the proper entry in the food list was missing. Only one descriptor from this facet can be added to each entry.',
'The food list item has been chosen because none of the more detailed items corresponded to the available information. Please consider the eventual addition of a new term in the list',
'Deprecated term that must NOT be used for any purpose. Its original scopenote was: The group includes any type of Other fruiting vegetables (exposure). The part consumed/analysed is by default unspecified. When relevant, information on the part consumed/analysed has to be reported with additional facet descriptors.',
]
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
#### Device Aware Information Retrieval
* Evaluated with <code>src.utils.eval_functions.DeviceAwareInformationRetrievalEvaluator</code>
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.985 |
| cosine_accuracy@3 | 0.999 |
| cosine_accuracy@5 | 0.9998 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.985 |
| cosine_precision@3 | 0.4171 |
| cosine_precision@5 | 0.2537 |
| cosine_precision@10 | 0.1275 |
| cosine_recall@1 | 0.8691 |
| cosine_recall@3 | 0.9939 |
| cosine_recall@5 | 0.9985 |
| cosine_recall@10 | 0.9999 |
| **cosine_ndcg@10** | **0.9936** |
| cosine_mrr@10 | 0.9919 |
| cosine_map@100 | 0.9909 |
<!--
## 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: 1,225,740 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | sentence_2 |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 37 tokens</li><li>mean: 89.82 tokens</li><li>max: 96 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 39.38 tokens</li><li>max: 96 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 39.59 tokens</li><li>max: 96 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 | sentence_2 |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>peach fresh flesh baked with skin </s> This facet allows recording different characteristics of the food: preservation treatments a food item underwent, technological steps or treatments applied while producing a food item, the way a food item has been heat treated before consumption and the way a food item has been prepared for final consumption (particularly needed for consumption surveys and includes preparation (like battering or breading) as well as heat treatment steps). More (none contradicting) descriptors can be applied to each entry.</code> | <code>Cooking by dry heat in or as if in an oven</code> | <code>Previously cooked or heat-treated fodd, heated again in order to raise its temperature (all different techniques)</code> |
| <code>turkey breast with bones frozen barbecued without skin </s> This facet allows recording different characteristics of the food: preservation treatments a food item underwent, technological steps or treatments applied while producing a food item, the way a food item has been heat treated before consumption and the way a food item has been prepared for final consumption (particularly needed for consumption surveys and includes preparation (like battering or breading) as well as heat treatment steps). More (none contradicting) descriptors can be applied to each entry.</code> | <code>Preserving by freezing sufficiently rapidly to avoid spoilage and microbial growth</code> | <code>Drying to a water content low enough to guarantee microbiological stability, but still keeping a relatively soft structure (often used for fruit)</code> |
| <code>yoghurt flavoured cow blueberry sweetened with sugar sucrose whole in glass commercial supermarket shop organic shop brand product name </s> This facet provides some principal claims related to important nutrients-ingredients, like fat, sugar etc. It is not intended to include health claims or similar. The present guidance provides a limited list, to be eventually improved during the evolution of the system. More than one descriptor can be applied to each entry, provided they are not contradicting each other.</code> | <code>The food item has all the natural (or average expected )fat content (for milk, at least the value defined in legislation, when available). In the case of cheese, the fat on the dry matter is 45-60%</code> | <code>The food item has an almost completely reduced amount of fat, with respect to the expected natural fat content (for milk, at least the value defined in legislation, when available). For meat, this is the entry for what is commercially intended as 'lean' meat, where fat is not visible.In the case of cheese, the fat on the dry matter is 10-25%</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`: steps
- `per_device_train_batch_size`: 48
- `per_device_eval_batch_size`: 48
- `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`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 48
- `per_device_eval_batch_size`: 48
- `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`: 1.0
- `num_train_epochs`: 3
- `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`: 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`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | cosine_ndcg@10 |
|:------:|:-----:|:-------------:|:--------------:|
| 0 | 0 | - | 0.0266 |
| 0.0196 | 500 | 1.5739 | - |
| 0.0392 | 1000 | 0.9043 | - |
| 0.0587 | 1500 | 0.8234 | - |
| 0.0783 | 2000 | 0.7861 | - |
| 0.0979 | 2500 | 0.7628 | - |
| 0.1175 | 3000 | 0.7348 | - |
| 0.1371 | 3500 | 0.7184 | - |
| 0.1566 | 4000 | 0.7167 | - |
| 0.1762 | 4500 | 0.7002 | - |
| 0.1958 | 5000 | 0.6791 | 0.9264 |
| 0.2154 | 5500 | 0.6533 | - |
| 0.2350 | 6000 | 0.6628 | - |
| 0.2545 | 6500 | 0.6637 | - |
| 0.2741 | 7000 | 0.639 | - |
| 0.2937 | 7500 | 0.6395 | - |
| 0.3133 | 8000 | 0.6358 | - |
| 0.3329 | 8500 | 0.617 | - |
| 0.3524 | 9000 | 0.6312 | - |
| 0.3720 | 9500 | 0.6107 | - |
| 0.3916 | 10000 | 0.6083 | 0.9518 |
| 0.4112 | 10500 | 0.6073 | - |
| 0.4307 | 11000 | 0.601 | - |
| 0.4503 | 11500 | 0.6047 | - |
| 0.4699 | 12000 | 0.5986 | - |
| 0.4895 | 12500 | 0.5913 | - |
| 0.5091 | 13000 | 0.5992 | - |
| 0.5286 | 13500 | 0.5911 | - |
| 0.5482 | 14000 | 0.5923 | - |
| 0.5678 | 14500 | 0.5816 | - |
| 0.5874 | 15000 | 0.582 | 0.9628 |
| 0.6070 | 15500 | 0.5815 | - |
| 0.6265 | 16000 | 0.5827 | - |
| 0.6461 | 16500 | 0.5885 | - |
| 0.6657 | 17000 | 0.5737 | - |
| 0.6853 | 17500 | 0.577 | - |
| 0.7049 | 18000 | 0.5687 | - |
| 0.7244 | 18500 | 0.5744 | - |
| 0.7440 | 19000 | 0.5774 | - |
| 0.7636 | 19500 | 0.5792 | - |
| 0.7832 | 20000 | 0.5645 | 0.9739 |
| 0.8028 | 20500 | 0.5769 | - |
| 0.8223 | 21000 | 0.5659 | - |
| 0.8419 | 21500 | 0.5635 | - |
| 0.8615 | 22000 | 0.5677 | - |
| 0.8811 | 22500 | 0.5693 | - |
| 0.9007 | 23000 | 0.5666 | - |
| 0.9202 | 23500 | 0.5526 | - |
| 0.9398 | 24000 | 0.5591 | - |
| 0.9594 | 24500 | 0.563 | - |
| 0.9790 | 25000 | 0.555 | 0.9808 |
| 0.9986 | 25500 | 0.5585 | - |
| 1.0 | 25537 | - | 0.9811 |
| 1.0181 | 26000 | 0.5595 | - |
| 1.0377 | 26500 | 0.5507 | - |
| 1.0573 | 27000 | 0.5582 | - |
| 1.0769 | 27500 | 0.5543 | - |
| 1.0964 | 28000 | 0.5598 | - |
| 1.1160 | 28500 | 0.5613 | - |
| 1.1356 | 29000 | 0.5457 | - |
| 1.1552 | 29500 | 0.5524 | - |
| 1.1748 | 30000 | 0.5324 | 0.9836 |
| 1.1943 | 30500 | 0.5531 | - |
| 1.2139 | 31000 | 0.5505 | - |
| 1.2335 | 31500 | 0.5623 | - |
| 1.2531 | 32000 | 0.5505 | - |
| 1.2727 | 32500 | 0.5583 | - |
| 1.2922 | 33000 | 0.548 | - |
| 1.3118 | 33500 | 0.5485 | - |
| 1.3314 | 34000 | 0.5509 | - |
| 1.3510 | 34500 | 0.54 | - |
| 1.3706 | 35000 | 0.5478 | 0.9835 |
| 1.3901 | 35500 | 0.5416 | - |
| 1.4097 | 36000 | 0.5438 | - |
| 1.4293 | 36500 | 0.543 | - |
| 1.4489 | 37000 | 0.547 | - |
| 1.4685 | 37500 | 0.5362 | - |
| 1.4880 | 38000 | 0.5536 | - |
| 1.5076 | 38500 | 0.5356 | - |
| 1.5272 | 39000 | 0.5382 | - |
| 1.5468 | 39500 | 0.5481 | - |
| 1.5664 | 40000 | 0.5302 | 0.9880 |
| 1.5859 | 40500 | 0.5275 | - |
| 1.6055 | 41000 | 0.5327 | - |
| 1.6251 | 41500 | 0.5414 | - |
| 1.6447 | 42000 | 0.5354 | - |
| 1.6643 | 42500 | 0.536 | - |
| 1.6838 | 43000 | 0.5364 | - |
| 1.7034 | 43500 | 0.5391 | - |
| 1.7230 | 44000 | 0.5342 | - |
| 1.7426 | 44500 | 0.5369 | - |
| 1.7621 | 45000 | 0.5387 | 0.9894 |
| 1.7817 | 45500 | 0.5312 | - |
| 1.8013 | 46000 | 0.5297 | - |
| 1.8209 | 46500 | 0.5222 | - |
| 1.8405 | 47000 | 0.5255 | - |
| 1.8600 | 47500 | 0.5379 | - |
| 1.8796 | 48000 | 0.5317 | - |
| 1.8992 | 48500 | 0.5312 | - |
| 1.9188 | 49000 | 0.5307 | - |
| 1.9384 | 49500 | 0.5375 | - |
| 1.9579 | 50000 | 0.527 | 0.9908 |
| 1.9775 | 50500 | 0.538 | - |
| 1.9971 | 51000 | 0.5312 | - |
| 2.0 | 51074 | - | 0.9911 |
| 2.0167 | 51500 | 0.5346 | - |
| 2.0363 | 52000 | 0.5279 | - |
| 2.0558 | 52500 | 0.517 | - |
| 2.0754 | 53000 | 0.5193 | - |
| 2.0950 | 53500 | 0.5286 | - |
| 2.1146 | 54000 | 0.5229 | - |
| 2.1342 | 54500 | 0.5183 | - |
| 2.1537 | 55000 | 0.5194 | 0.9915 |
| 2.1733 | 55500 | 0.5362 | - |
| 2.1929 | 56000 | 0.5186 | - |
| 2.2125 | 56500 | 0.5202 | - |
| 2.2321 | 57000 | 0.5276 | - |
| 2.2516 | 57500 | 0.5266 | - |
| 2.2712 | 58000 | 0.5334 | - |
| 2.2908 | 58500 | 0.5206 | - |
| 2.3104 | 59000 | 0.5229 | - |
| 2.3300 | 59500 | 0.5111 | - |
| 2.3495 | 60000 | 0.5175 | 0.9928 |
| 2.3691 | 60500 | 0.5235 | - |
| 2.3887 | 61000 | 0.5127 | - |
| 2.4083 | 61500 | 0.5291 | - |
| 2.4278 | 62000 | 0.5122 | - |
| 2.4474 | 62500 | 0.5196 | - |
| 2.4670 | 63000 | 0.5159 | - |
| 2.4866 | 63500 | 0.5207 | - |
| 2.5062 | 64000 | 0.5157 | - |
| 2.5257 | 64500 | 0.5094 | - |
| 2.5453 | 65000 | 0.5283 | 0.9937 |
| 2.5649 | 65500 | 0.5256 | - |
| 2.5845 | 66000 | 0.524 | - |
| 2.6041 | 66500 | 0.5324 | - |
| 2.6236 | 67000 | 0.5132 | - |
| 2.6432 | 67500 | 0.5203 | - |
| 2.6628 | 68000 | 0.5224 | - |
| 2.6824 | 68500 | 0.5255 | - |
| 2.7020 | 69000 | 0.5132 | - |
| 2.7215 | 69500 | 0.525 | - |
| 2.7411 | 70000 | 0.5257 | 0.9936 |
| 2.7607 | 70500 | 0.5206 | - |
| 2.7803 | 71000 | 0.514 | - |
| 2.7999 | 71500 | 0.5175 | - |
| 2.8194 | 72000 | 0.5245 | - |
| 2.8390 | 72500 | 0.5144 | - |
| 2.8586 | 73000 | 0.5246 | - |
| 2.8782 | 73500 | 0.5227 | - |
| 2.8978 | 74000 | 0.5199 | - |
| 2.9173 | 74500 | 0.5216 | - |
| 2.9369 | 75000 | 0.5253 | 0.9936 |
| 2.9565 | 75500 | 0.5303 | - |
| 2.9761 | 76000 | 0.5148 | - |
| 2.9957 | 76500 | 0.5248 | - |
| 3.0 | 76611 | - | 0.9936 |
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- Accelerate: 1.4.0
- Datasets: 3.3.1
- 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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