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---
language:
- en
tags:
- sentence-transformers
- cross-encoder
- generated_from_trainer
- dataset_size:78704
- loss:PListMLELoss
base_model: microsoft/MiniLM-L12-H384-uncased
datasets:
- microsoft/ms_marco
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
- map
- mrr@10
- ndcg@10
co2_eq_emissions:
emissions: 93.08788204215189
energy_consumed: 0.23948392867068316
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: 0.972
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: CrossEncoder based on microsoft/MiniLM-L12-H384-uncased
results:
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoMSMARCO R100
type: NanoMSMARCO_R100
metrics:
- type: map
value: 0.49
name: Map
- type: mrr@10
value: 0.4792
name: Mrr@10
- type: ndcg@10
value: 0.5526
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoNFCorpus R100
type: NanoNFCorpus_R100
metrics:
- type: map
value: 0.3317
name: Map
- type: mrr@10
value: 0.5575
name: Mrr@10
- type: ndcg@10
value: 0.3642
name: Ndcg@10
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoNQ R100
type: NanoNQ_R100
metrics:
- type: map
value: 0.5829
name: Map
- type: mrr@10
value: 0.5914
name: Mrr@10
- type: ndcg@10
value: 0.6488
name: Ndcg@10
- task:
type: cross-encoder-nano-beir
name: Cross Encoder Nano BEIR
dataset:
name: NanoBEIR R100 mean
type: NanoBEIR_R100_mean
metrics:
- type: map
value: 0.4682
name: Map
- type: mrr@10
value: 0.5427
name: Mrr@10
- type: ndcg@10
value: 0.5219
name: Ndcg@10
---
# CrossEncoder based on microsoft/MiniLM-L12-H384-uncased
This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
## Model Details
### Model Description
- **Model Type:** Cross Encoder
- **Base model:** [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) <!-- at revision 44acabbec0ef496f6dbc93adadea57f376b7c0ec -->
- **Maximum Sequence Length:** 512 tokens
- **Number of Output Labels:** 1 label
- **Training Dataset:**
- [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
## 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 CrossEncoder
# Download from the 🤗 Hub
model = CrossEncoder("tomaarsen/reranker-msmarco-v1.1-MiniLM-L12-H384-uncased-plistmle-seeded")
# Get scores for pairs of texts
pairs = [
['How many calories in an egg', 'There are on average between 55 and 80 calories in an egg depending on its size.'],
['How many calories in an egg', 'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.'],
['How many calories in an egg', 'Most of the calories in an egg come from the yellow yolk in the center.'],
]
scores = model.predict(pairs)
print(scores.shape)
# (3,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'How many calories in an egg',
[
'There are on average between 55 and 80 calories in an egg depending on its size.',
'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.',
'Most of the calories in an egg come from the yellow yolk in the center.',
]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
```
<!--
### 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
#### Cross Encoder Reranking
* Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100` and `NanoNQ_R100`
* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
```json
{
"at_k": 10,
"always_rerank_positives": true
}
```
| Metric | NanoMSMARCO_R100 | NanoNFCorpus_R100 | NanoNQ_R100 |
|:------------|:---------------------|:---------------------|:---------------------|
| map | 0.4900 (+0.0004) | 0.3317 (+0.0707) | 0.5829 (+0.1632) |
| mrr@10 | 0.4792 (+0.0017) | 0.5575 (+0.0577) | 0.5914 (+0.1647) |
| **ndcg@10** | **0.5526 (+0.0122)** | **0.3642 (+0.0391)** | **0.6488 (+0.1481)** |
#### Cross Encoder Nano BEIR
* Dataset: `NanoBEIR_R100_mean`
* Evaluated with [<code>CrossEncoderNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nfcorpus",
"nq"
],
"rerank_k": 100,
"at_k": 10,
"always_rerank_positives": true
}
```
| Metric | Value |
|:------------|:---------------------|
| map | 0.4682 (+0.0781) |
| mrr@10 | 0.5427 (+0.0747) |
| **ndcg@10** | **0.5219 (+0.0665)** |
<!--
## 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
#### ms_marco
* Dataset: [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco) at [a47ee7a](https://huggingface.co/datasets/microsoft/ms_marco/tree/a47ee7aae8d7d466ba15f9f0bfac3b3681087b3a)
* Size: 78,704 training samples
* Columns: <code>query</code>, <code>docs</code>, and <code>labels</code>
* Approximate statistics based on the first 1000 samples:
| | query | docs | labels |
|:--------|:-----------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
| type | string | list | list |
| details | <ul><li>min: 11 characters</li><li>mean: 33.61 characters</li><li>max: 85 characters</li></ul> | <ul><li>min: 2 elements</li><li>mean: 6.00 elements</li><li>max: 10 elements</li></ul> | <ul><li>min: 2 elements</li><li>mean: 6.00 elements</li><li>max: 10 elements</li></ul> |
* Samples:
| query | docs | labels |
|:------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------|
| <code>what does syllables mean</code> | <code>['A syllable is a unit of organization for a sequence of speech sounds. For example, the word water is composed of two syllables: wa and ter. A syllable is typically made up of a syllable nucleus (most often a vowel) with optional initial and final margins (typically, consonants). Syllables are often considered the phonological building blocks of words. They can influence the rhythm of a language, its prosody, its poetic meter and its stress patterns. The first syllable of a word is the initial syllable and the last syllable is the final syllable. In languages accented on one of the last three syllables, the last syllable is called the ultima, the next-to-last is called the penult, and the third syllable from the end is called the antepenult.', '1 A unit of pronunciation having one vowel sound, with or without surrounding consonants, forming the whole or a part of a word; for example, there are two syllables in water and three in inferno. Example sentences. 1 The vowels of the stresse...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
| <code>how long does it take to become a child psychiatrist</code> | <code>["The Path to Becoming a Psychologist. First, you will need a bachelor's degree (4 to 5 years), which teaches the fundamentals of psychology. After that, you will need a master's degree (2 to 3 years), which can qualify you to practice in the field as a case manager, employment specialist, or social worker.", 'For example, becoming a school psychologist can take a little as two years of graduate-level education, and only requires a master’s degree. On the other hand, if you want to become a child psychologist you will need to earn a doctorate degree, which can require up to seven additional years of psychologist schooling.', '1 During the first four years of medical school you take classes, do lab work, and learn about medical ethics. 2 You may not have the opportunity to do hands-on psychiatry work at this stage, but earning your medical degree is a requirement in the path to becoming a psychiatrist, so stick with it.', '1 Clinical Psychologist: Doctorate Degree in Psychology (4 to 7...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
| <code>how do great horned owls defend themselves</code> | <code>["Owls can't always successfully defend themselves from other animals, particularly their prey. Great horned owls, for example, are often found either dead or injured as a result of would-be prey like skunks and porcupines fighting back. Feet and Beak. Like other birds in the raptor group, owls of all species use their beaks and talons to defend themselves. An owl's feet are equipped with particularly long, sharp and curved claws, which he can dig into an adversary and use like hooks to tear and rip at flesh.", "Tom Brakefield/Stockbyte/Getty Images. Owls are raptors, birds of prey. They provide sustenance and defend themselves with strong, sharp breaks and talons. The owl's ability to avoid detection is perhaps the most important weapon in his defensive arsenal, since it allows him to avoid confrontation in the first place. Feet and Beak. Like other birds in the raptor group, owls of all species use their beaks and talons to defend themselves. An owl's feet are equipped with particula...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
* Loss: [<code>PListMLELoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#plistmleloss) with these parameters:
```json
{
"lambda_weight": "sentence_transformers.cross_encoder.losses.PListMLELoss.PListMLELambdaWeight",
"activation_fct": "torch.nn.modules.linear.Identity",
"mini_batch_size": 16,
"respect_input_order": true
}
```
### Evaluation Dataset
#### ms_marco
* Dataset: [ms_marco](https://huggingface.co/datasets/microsoft/ms_marco) at [a47ee7a](https://huggingface.co/datasets/microsoft/ms_marco/tree/a47ee7aae8d7d466ba15f9f0bfac3b3681087b3a)
* Size: 1,000 evaluation samples
* Columns: <code>query</code>, <code>docs</code>, and <code>labels</code>
* Approximate statistics based on the first 1000 samples:
| | query | docs | labels |
|:--------|:-----------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
| type | string | list | list |
| details | <ul><li>min: 12 characters</li><li>mean: 33.62 characters</li><li>max: 99 characters</li></ul> | <ul><li>min: 3 elements</li><li>mean: 6.50 elements</li><li>max: 10 elements</li></ul> | <ul><li>min: 3 elements</li><li>mean: 6.50 elements</li><li>max: 10 elements</li></ul> |
* Samples:
| query | docs | labels |
|:------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------|
| <code>what age do kids fly free?</code> | <code>["If you're taking a domestic flight with your infant, your airline will likely allow the baby to fly at no cost -- provided you hold him on your lap throughout the flight. Generally, American Airlines allows children younger than two years of age to fly for free with a parent or another adult over the age of 18. You'll save cash, though you'll likely be uncomfortable after a short time unless you're traveling with a partner or other adult who can take turns holding the baby. ", "Unaccompanied Minor Program. The Unaccompanied Minor Program is required for all children 5-14 years old when not traveling in the same compartment with an adult who is at least 18 years old or the child's parent/legal guardian. The program is optional for children 15-17 years old. ", 'Most airlines let under 2 fly for free (not under 3).If flying internationally,taxes or a small service fee usually 10% of adult fare will have to be paid. ANOTHER ANSWER I totally agree with answer #2. Whether you have a newbor...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
| <code>extensor muscles of the hand that are innervated by radial nerve</code> | <code>['Extrinsic muscles of the hand innervated by the radial nerve. extensor digitorum communis (EDC), extensor digiti minimi (EDM), extensor indicis, extensor pollicis longus (EPL), extensor pollicis brevis (EPB), abductor pollicis longus (APL).', 'The radial nerve contributed 1 to 3 branches to the brachialis in 10 of 20 specimens. In all specimens, the radial nerve innervated all of the extensor fore-arm muscles. In 2 of 20 specimens, there was an extensor medius proprius (EMP) muscle.', 'The thenar muscles are three short muscles located at the base of the thumb. The muscle bellies produce a bulge, known as the thenar eminence. They are responsible for the fine movements of the thumb. The median nerve innervates all the thenar muscles.', 'A total of 27 bones constitute the basic skeleton of the wrist and hand. The hand is innervated by 3 nerves — the median, ulnar, and radial nerves — each of which has sensory and motor components. The muscles of the hand are divided into intrinsic and...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
| <code>what does domestic limited liability company mean</code> | <code>['2. Domestic limited liability company means an entity that is an unincorporated association having one or more members and that is organized under ORS chapter 63. 4. Look beforeyou eat. Portland-area restaurant health scores. 1. Domestic limited liability company means an entity that is an unincorporated association having one or more members and that is organized under ORS chapter 63.', 'To register a Domestic Limited Liability Company in Hawaii, you must file the Articles of Organization for Limited Liability Company Form LLC-1 with the appropriate filing fee(s) . Use the links above to register and pay online or to access our fillable PDF forms which you can print and mail in with your payment. ', "I was talking to someone the other day who has a limited liability company (LLC). She is doing business in several states and she said she was told she must register as a foreign LLC in each state. She wondered why it was called a foreign LLC, since she wasn't doing business outside t...</code> | <code>[1, 0, 0, 0, 0, ...]</code> |
* Loss: [<code>PListMLELoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#plistmleloss) with these parameters:
```json
{
"lambda_weight": "sentence_transformers.cross_encoder.losses.PListMLELoss.PListMLELambdaWeight",
"activation_fct": "torch.nn.modules.linear.Identity",
"mini_batch_size": 16,
"respect_input_order": true
}
```
### 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
- `seed`: 12
- `bf16`: True
- `load_best_model_at_end`: True
#### 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
- `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`: 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
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 12
- `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`: 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`: 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
- `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`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_R100_ndcg@10 | NanoNFCorpus_R100_ndcg@10 | NanoNQ_R100_ndcg@10 | NanoBEIR_R100_mean_ndcg@10 |
|:----------:|:--------:|:-------------:|:---------------:|:------------------------:|:-------------------------:|:--------------------:|:--------------------------:|
| -1 | -1 | - | - | 0.0300 (-0.5104) | 0.2528 (-0.0723) | 0.0168 (-0.4839) | 0.0999 (-0.3555) |
| 0.0002 | 1 | 2.2023 | - | - | - | - | - |
| 0.0508 | 250 | 2.1003 | - | - | - | - | - |
| 0.1016 | 500 | 1.9606 | 1.9318 | 0.2069 (-0.3335) | 0.2496 (-0.0755) | 0.2308 (-0.2699) | 0.2291 (-0.2263) |
| 0.1525 | 750 | 1.8932 | - | - | - | - | - |
| 0.2033 | 1000 | 1.8711 | 1.8656 | 0.4275 (-0.1129) | 0.2878 (-0.0372) | 0.4897 (-0.0109) | 0.4017 (-0.0537) |
| 0.2541 | 1250 | 1.8597 | - | - | - | - | - |
| 0.3049 | 1500 | 1.8486 | 1.8518 | 0.5873 (+0.0469) | 0.3577 (+0.0327) | 0.5874 (+0.0868) | 0.5108 (+0.0555) |
| 0.3558 | 1750 | 1.8415 | - | - | - | - | - |
| 0.4066 | 2000 | 1.8338 | 1.8441 | 0.5467 (+0.0062) | 0.3619 (+0.0368) | 0.5936 (+0.0929) | 0.5007 (+0.0453) |
| 0.4574 | 2250 | 1.8189 | - | - | - | - | - |
| 0.5082 | 2500 | 1.8338 | 1.8293 | 0.5523 (+0.0119) | 0.3676 (+0.0426) | 0.6452 (+0.1446) | 0.5217 (+0.0664) |
| 0.5591 | 2750 | 1.8109 | - | - | - | - | - |
| 0.6099 | 3000 | 1.8291 | 1.8306 | 0.5489 (+0.0085) | 0.3649 (+0.0398) | 0.6360 (+0.1353) | 0.5166 (+0.0612) |
| 0.6607 | 3250 | 1.8124 | - | - | - | - | - |
| **0.7115** | **3500** | **1.8205** | **1.8301** | **0.5526 (+0.0122)** | **0.3642 (+0.0391)** | **0.6488 (+0.1481)** | **0.5219 (+0.0665)** |
| 0.7624 | 3750 | 1.8166 | - | - | - | - | - |
| 0.8132 | 4000 | 1.8223 | 1.8205 | 0.5512 (+0.0108) | 0.3578 (+0.0328) | 0.6173 (+0.1167) | 0.5088 (+0.0534) |
| 0.8640 | 4250 | 1.8129 | - | - | - | - | - |
| 0.9148 | 4500 | 1.8132 | 1.8214 | 0.5364 (-0.0040) | 0.3603 (+0.0353) | 0.6257 (+0.1251) | 0.5075 (+0.0521) |
| 0.9656 | 4750 | 1.8188 | - | - | - | - | - |
| -1 | -1 | - | - | 0.5526 (+0.0122) | 0.3642 (+0.0391) | 0.6488 (+0.1481) | 0.5219 (+0.0665) |
* The bold row denotes the saved checkpoint.
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.239 kWh
- **Carbon Emitted**: 0.093 kg of CO2
- **Hours Used**: 0.972 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: 3.5.0.dev0
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.1
- Datasets: 3.3.2
- 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",
}
```
#### PListMLELoss
```bibtex
@inproceedings{lan2014position,
title={Position-Aware ListMLE: A Sequential Learning Process for Ranking.},
author={Lan, Yanyan and Zhu, Yadong and Guo, Jiafeng and Niu, Shuzi and Cheng, Xueqi},
booktitle={UAI},
volume={14},
pages={449--458},
year={2014}
}
```
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