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---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:700000
- loss:DenoisingAutoEncoderLoss
base_model: intfloat/e5-base-unsupervised
datasets: []
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: in Freeview no extra therefore minimal Also the is wide decent,
plus they and.
sentences:
- 'Pokémon-GX (Japanese: ポケモンGX Pokémon GX), officially written Pokémon-GX, are
a variant of Pokémon in the Pokémon Trading Card Game. They were first introduced
in the Sun & Moon expansion (the Collection Sun and Collection Moon expansions
in Japan). Pokémon-GX have a stylized. graphic on the card name.'
- 'The Cape Colony (Dutch: Kaapkolonie) was a Dutch East India Company colony in
Southern Africa, centered on the Cape of Good Hope, whence it derived its name.
The original colony and its successive states that the colony was incorporated
into occupied much of modern South Africa.'
- Avtex is expensive, but you get built in Freeview, Freesat and built in DVD player,
which means no extra boxes, and therefore minimal wiring. Also the viewing angle
is wide and a decent picture quality, plus they are light and designed for mobile
use.
- source_sentence: as power Yes can use transmission of power steering But, sure you
check the manufacturer's the a
sentences:
- Can you use transmission fluid as a substitute for power steering fluid? Yes,
you can use transmission fluid in place of a power steering fluid. But, make sure
you check the car manufacturer's recommendations before using the ATF as a substitute.
- how much kwh does an xbox one use?
- what is the difference between demerara cane sugar and turbinado cane sugar?
- source_sentence: '(number ''Step: Ensure date to (and number is set Date 2 formula
to add the number months start.'''
sentences:
- Being a medical doctor is really great. It's stimulating and interesting. Medical
doctors have a significant degree of autonomy over their schedules and time. Medical
doctors know that they get to help people solve problems every single day.
- how much is an air conditioner for a house?
- '[''=EDATE(start date, number of months)'', ''Step 1: Ensure the starting date
is properly formatted – go to Format Cells (press Ctrl + 1) and make sure the
number is set to Date.'', ''Step 2: Use the =EDATE(C3,C5) formula to add the number
of specified months to the start date.'']'
- source_sentence: many days can
sentences:
- how many days after can you have morning after pill?
- is gender an independent variable?
- The current standard is about 30 days, which means that some teachers and support
staff may be brought on board before the results of their criminal background
check are completed. The issue, as reported in this article, is the lag time between
state and federal background checks.
- source_sentence: ligand ion channels located?
sentences:
- Share on Pinterest Recent research suggests that chocolate may have some health
benefits. Chocolate receives a lot of bad press because of its high fat and sugar
content. Its consumption has been associated with acne, obesity, high blood pressure,
coronary artery disease, and diabetes.
- where are ligand gated ion channels located?
- Duvets tend to be warm but surprisingly lightweight. The duvet cover makes it
easier to change bedding looks and styles. You won't need to wash your duvet very
often, just wash the cover regularly. Additionally, duvets tend to be fluffier
than comforters, and can simplify bed making if you choose the European style.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on intfloat/e5-base-unsupervised
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.7651793859211248
name: Pearson Cosine
- type: spearman_cosine
value: 0.7524804428249002
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7393361318996702
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7326262473219208
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7402295162714656
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7335305408258518
name: Spearman Euclidean
- type: pearson_dot
value: 0.5002878735642248
name: Pearson Dot
- type: spearman_dot
value: 0.4986010870846151
name: Spearman Dot
- type: pearson_max
value: 0.7651793859211248
name: Pearson Max
- type: spearman_max
value: 0.7524804428249002
name: Spearman Max
---
# SentenceTransformer based on intfloat/e5-base-unsupervised
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/e5-base-unsupervised](https://huggingface.co/intfloat/e5-base-unsupervised). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [intfloat/e5-base-unsupervised](https://huggingface.co/intfloat/e5-base-unsupervised) <!-- at revision 6003a5b7ce770b0549203e41115b9fc683f16dad -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, '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("bobox/E5-base-unsupervised-TSDAE-2")
# Run inference
sentences = [
'ligand ion channels located?',
'where are ligand gated ion channels located?',
"Duvets tend to be warm but surprisingly lightweight. The duvet cover makes it easier to change bedding looks and styles. You won't need to wash your duvet very often, just wash the cover regularly. Additionally, duvets tend to be fluffier than comforters, and can simplify bed making if you choose the European style.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# 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
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7652 |
| **spearman_cosine** | **0.7525** |
| pearson_manhattan | 0.7393 |
| spearman_manhattan | 0.7326 |
| pearson_euclidean | 0.7402 |
| spearman_euclidean | 0.7335 |
| pearson_dot | 0.5003 |
| spearman_dot | 0.4986 |
| pearson_max | 0.7652 |
| spearman_max | 0.7525 |
<!--
## 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: 700,000 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 15.73 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 36.05 tokens</li><li>max: 131 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:---------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Quality such a has components with applicable high objective system measure component improvements</code> | <code>Quality in such a system has three components: high accuracy, compliance with applicable standards, and high customer satisfaction. The objective of the system is to measure each component and achieve improvements.</code> |
| <code>include</code> | <code>does qbi include capital gains?</code> |
| <code>They have a . parietal is in, as becomes and pigments after four to is believed and in circadian cycles</code> | <code>They have a third eye. The parietal eye is only visible in hatchlings, as it becomes covered in scales and pigments after four to six months. Its function is a subject of ongoing research, but it is believed to be useful in absorbing ultraviolet rays and in setting circadian and seasonal cycles.</code> |
* Loss: [<code>DenoisingAutoEncoderLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#denoisingautoencoderloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 2
- `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`: 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
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | sts-test_spearman_cosine |
|:------:|:-----:|:-------------:|:------------------------:|
| 0 | 0 | - | 0.7211 |
| 0.0114 | 500 | 9.4957 | - |
| 0.0229 | 1000 | 7.4063 | - |
| 0.0343 | 1500 | 7.0225 | - |
| 0.0457 | 2000 | 6.6991 | - |
| 0.0571 | 2500 | 6.4054 | - |
| 0.0686 | 3000 | 6.1933 | - |
| 0.08 | 3500 | 5.999 | - |
| 0.0914 | 4000 | 5.8471 | - |
| 0.1 | 4375 | - | 0.4610 |
| 0.1029 | 4500 | 5.6876 | - |
| 0.1143 | 5000 | 5.5934 | - |
| 0.1257 | 5500 | 5.4877 | - |
| 0.1371 | 6000 | 5.4034 | - |
| 0.1486 | 6500 | 5.3016 | - |
| 0.16 | 7000 | 5.2169 | - |
| 0.1714 | 7500 | 5.1351 | - |
| 0.1829 | 8000 | 5.0605 | - |
| 0.1943 | 8500 | 4.9851 | - |
| 0.2 | 8750 | - | 0.6490 |
| 0.2057 | 9000 | 4.9024 | - |
| 0.2171 | 9500 | 4.8722 | - |
| 0.2286 | 10000 | 4.7955 | - |
| 0.24 | 10500 | 4.7435 | - |
| 0.2514 | 11000 | 4.6742 | - |
| 0.2629 | 11500 | 4.6447 | - |
| 0.2743 | 12000 | 4.5964 | - |
| 0.2857 | 12500 | 4.5186 | - |
| 0.2971 | 13000 | 4.5024 | - |
| 0.3 | 13125 | - | 0.7121 |
| 0.3086 | 13500 | 4.4336 | - |
| 0.32 | 14000 | 4.3767 | - |
| 0.3314 | 14500 | 4.3454 | - |
| 0.3429 | 15000 | 4.3067 | - |
| 0.3543 | 15500 | 4.2627 | - |
| 0.3657 | 16000 | 4.2323 | - |
| 0.3771 | 16500 | 4.208 | - |
| 0.3886 | 17000 | 4.1622 | - |
| 0.4 | 17500 | 4.113 | 0.7375 |
| 0.4114 | 18000 | 4.1097 | - |
| 0.4229 | 18500 | 4.0666 | - |
| 0.4343 | 19000 | 4.0311 | - |
| 0.4457 | 19500 | 4.0241 | - |
| 0.4571 | 20000 | 3.9991 | - |
| 0.4686 | 20500 | 3.9873 | - |
| 0.48 | 21000 | 3.9439 | - |
| 0.4914 | 21500 | 3.9281 | - |
| 0.5 | 21875 | - | 0.7502 |
| 0.5029 | 22000 | 3.9047 | - |
| 0.5143 | 22500 | 3.89 | - |
| 0.5257 | 23000 | 3.8671 | - |
| 0.5371 | 23500 | 3.85 | - |
| 0.5486 | 24000 | 3.8336 | - |
| 0.56 | 24500 | 3.8081 | - |
| 0.5714 | 25000 | 3.8049 | - |
| 0.5829 | 25500 | 3.7587 | - |
| 0.5943 | 26000 | 3.769 | - |
| 0.6 | 26250 | - | 0.7530 |
| 0.6057 | 26500 | 3.7488 | - |
| 0.6171 | 27000 | 3.7218 | - |
| 0.6286 | 27500 | 3.7128 | - |
| 0.64 | 28000 | 3.7104 | - |
| 0.6514 | 28500 | 3.6706 | - |
| 0.6629 | 29000 | 3.6602 | - |
| 0.6743 | 29500 | 3.658 | - |
| 0.6857 | 30000 | 3.665 | - |
| 0.6971 | 30500 | 3.6439 | - |
| 0.7 | 30625 | - | 0.7561 |
| 0.7086 | 31000 | 3.6411 | - |
| 0.72 | 31500 | 3.6141 | - |
| 0.7314 | 32000 | 3.6172 | - |
| 0.7429 | 32500 | 3.5975 | - |
| 0.7543 | 33000 | 3.5827 | - |
| 0.7657 | 33500 | 3.5836 | - |
| 0.7771 | 34000 | 3.5484 | - |
| 0.7886 | 34500 | 3.5275 | - |
| 0.8 | 35000 | 3.5587 | 0.7553 |
| 0.8114 | 35500 | 3.5371 | - |
| 0.8229 | 36000 | 3.5334 | - |
| 0.8343 | 36500 | 3.5168 | - |
| 0.8457 | 37000 | 3.483 | - |
| 0.8571 | 37500 | 3.4755 | - |
| 0.8686 | 38000 | 3.4943 | - |
| 0.88 | 38500 | 3.4699 | - |
| 0.8914 | 39000 | 3.4732 | - |
| 0.9 | 39375 | - | 0.7560 |
| 0.9029 | 39500 | 3.4572 | - |
| 0.9143 | 40000 | 3.4518 | - |
| 0.9257 | 40500 | 3.4298 | - |
| 0.9371 | 41000 | 3.4215 | - |
| 0.9486 | 41500 | 3.4176 | - |
| 0.96 | 42000 | 3.4353 | - |
| 0.9714 | 42500 | 3.4137 | - |
| 0.9829 | 43000 | 3.4037 | - |
| 0.9943 | 43500 | 3.4157 | - |
| 1.0 | 43750 | - | 0.7554 |
| 1.0057 | 44000 | 3.393 | - |
| 1.0171 | 44500 | 3.4092 | - |
| 1.0286 | 45000 | 3.3861 | - |
| 1.04 | 45500 | 3.3976 | - |
| 1.0514 | 46000 | 3.3769 | - |
| 1.0629 | 46500 | 3.3444 | - |
| 1.0743 | 47000 | 3.3598 | - |
| 1.0857 | 47500 | 3.3556 | - |
| 1.0971 | 48000 | 3.3548 | - |
| 1.1 | 48125 | - | 0.7549 |
| 1.1086 | 48500 | 3.3278 | - |
| 1.12 | 49000 | 3.3309 | - |
| 1.1314 | 49500 | 3.3459 | - |
| 1.1429 | 50000 | 3.3353 | - |
| 1.1543 | 50500 | 3.3192 | - |
| 1.1657 | 51000 | 3.3022 | - |
| 1.1771 | 51500 | 3.3189 | - |
| 1.1886 | 52000 | 3.301 | - |
| 1.2 | 52500 | 3.2785 | 0.7542 |
| 1.2114 | 53000 | 3.2996 | - |
| 1.2229 | 53500 | 3.2863 | - |
| 1.2343 | 54000 | 3.2916 | - |
| 1.2457 | 54500 | 3.272 | - |
| 1.2571 | 55000 | 3.2896 | - |
| 1.2686 | 55500 | 3.2694 | - |
| 1.28 | 56000 | 3.2848 | - |
| 1.2914 | 56500 | 3.2528 | - |
| 1.3 | 56875 | - | 0.7554 |
| 1.3029 | 57000 | 3.2622 | - |
| 1.3143 | 57500 | 3.2515 | - |
| 1.3257 | 58000 | 3.2385 | - |
| 1.3371 | 58500 | 3.2341 | - |
| 1.3486 | 59000 | 3.2275 | - |
| 1.3600 | 59500 | 3.2538 | - |
| 1.3714 | 60000 | 3.2329 | - |
| 1.3829 | 60500 | 3.2322 | - |
| 1.3943 | 61000 | 3.2039 | - |
| 1.4 | 61250 | - | 0.7530 |
| 1.4057 | 61500 | 3.212 | - |
| 1.4171 | 62000 | 3.2127 | - |
| 1.4286 | 62500 | 3.1956 | - |
| 1.44 | 63000 | 3.202 | - |
| 1.4514 | 63500 | 3.2046 | - |
| 1.4629 | 64000 | 3.2105 | - |
| 1.4743 | 64500 | 3.1915 | - |
| 1.4857 | 65000 | 3.176 | - |
| 1.4971 | 65500 | 3.1852 | - |
| 1.5 | 65625 | - | 0.7541 |
| 1.5086 | 66000 | 3.1988 | - |
| 1.52 | 66500 | 3.1714 | - |
| 1.5314 | 67000 | 3.1816 | - |
| 1.5429 | 67500 | 3.1745 | - |
| 1.5543 | 68000 | 3.1674 | - |
| 1.5657 | 68500 | 3.1887 | - |
| 1.5771 | 69000 | 3.1567 | - |
| 1.5886 | 69500 | 3.1775 | - |
| 1.6 | 70000 | 3.1696 | 0.7535 |
| 1.6114 | 70500 | 3.154 | - |
| 1.6229 | 71000 | 3.1553 | - |
| 1.6343 | 71500 | 3.1675 | - |
| 1.6457 | 72000 | 3.1516 | - |
| 1.6571 | 72500 | 3.1569 | - |
| 1.6686 | 73000 | 3.1403 | - |
| 1.6800 | 73500 | 3.1667 | - |
| 1.6914 | 74000 | 3.1545 | - |
| 1.7 | 74375 | - | 0.7529 |
| 1.7029 | 74500 | 3.1736 | - |
| 1.7143 | 75000 | 3.1447 | - |
| 1.7257 | 75500 | 3.1567 | - |
| 1.7371 | 76000 | 3.1682 | - |
| 1.7486 | 76500 | 3.149 | - |
| 1.76 | 77000 | 3.1522 | - |
| 1.7714 | 77500 | 3.1412 | - |
| 1.7829 | 78000 | 3.1268 | - |
| 1.7943 | 78500 | 3.1476 | - |
| 1.8 | 78750 | - | 0.7524 |
| 1.8057 | 79000 | 3.1669 | - |
| 1.8171 | 79500 | 3.1432 | - |
| 1.8286 | 80000 | 3.1603 | - |
| 1.8400 | 80500 | 3.1347 | - |
| 1.8514 | 81000 | 3.1209 | - |
| 1.8629 | 81500 | 3.1302 | - |
| 1.8743 | 82000 | 3.1423 | - |
| 1.8857 | 82500 | 3.1481 | - |
| 1.8971 | 83000 | 3.1262 | - |
| 1.9 | 83125 | - | 0.7525 |
| 1.9086 | 83500 | 3.1484 | - |
| 1.92 | 84000 | 3.1331 | - |
| 1.9314 | 84500 | 3.122 | - |
| 1.9429 | 85000 | 3.1272 | - |
| 1.9543 | 85500 | 3.1435 | - |
| 1.9657 | 86000 | 3.1431 | - |
| 1.9771 | 86500 | 3.1457 | - |
| 1.9886 | 87000 | 3.1286 | - |
| 2.0 | 87500 | 3.1352 | 0.7525 |
</details>
### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2
- Accelerate: 0.31.0
- Datasets: 2.19.2
- Tokenizers: 0.19.1
## 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",
}
```
#### DenoisingAutoEncoderLoss
```bibtex
@inproceedings{wang-2021-TSDAE,
title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
pages = "671--688",
url = "https://arxiv.org/abs/2104.06979",
}
```
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