Hugging Face's logo

sobamchan
/
st5-base-mean-2000

Sentence Similarity
sentence-transformers
Safetensors
English
t5
feature-extraction
Generated from Trainer
dataset_size:557850
loss:MultipleNegativesRankingLoss
Inference Endpoints
Model card Files Community
st5-base-mean-2000 / README.md
sobamchan's picture
sobamchan
Upload folder using huggingface_hub
7ed39c5 verified
preview code
|
raw
history blame contribute delete
26.7 kB
---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:557850
- loss:MultipleNegativesRankingLoss
base_model: google-t5/t5-base
widget:
- source_sentence: A man is jumping unto his filthy bed.
sentences:
- A young male is looking at a newspaper while 2 females walks past him.
- The bed is dirty.
- The man is on the moon.
- source_sentence: A carefully balanced male stands on one foot near a clean ocean
beach area.
sentences:
- A man is ouside near the beach.
- Three policemen patrol the streets on bikes
- A man is sitting on his couch.
- source_sentence: The man is wearing a blue shirt.
sentences:
- Near the trashcan the man stood and smoked
- A man in a blue shirt leans on a wall beside a road with a blue van and red car
with water in the background.
- A man in a black shirt is playing a guitar.
- source_sentence: The girls are outdoors.
sentences:
- Two girls riding on an amusement part ride.
- a guy laughs while doing laundry
- Three girls are standing together in a room, one is listening, one is writing
on a wall and the third is talking to them.
- source_sentence: A construction worker peeking out of a manhole while his coworker
sits on the sidewalk smiling.
sentences:
- A worker is looking out of a manhole.
- A man is giving a presentation.
- The workers are both inside the manhole.
datasets:
- sentence-transformers/all-nli
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on google-t5/t5-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. 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:** [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) <!-- at revision a9723ea7f1b39c1eae772870f3b547bf6ef7e6c1 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
- **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': 256, 'do_lower_case': False}) with Transformer model: T5EncoderModel
(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})
(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("sentence_transformers_model_id")
# Run inference
sentences = [
'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.',
'A worker is looking out of a manhole.',
'The workers are both inside the manhole.',
]
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.*
-->
<!--
## 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
#### all-nli
* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 557,850 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 9.96 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.79 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.02 tokens</li><li>max: 57 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> |
| <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> |
| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</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"
}
```
### Evaluation Dataset
#### all-nli
* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 6,584 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 19.41 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.69 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.35 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
| <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</code> |
| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code> |
| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>A man selling donuts to a customer.</code> | <code>A woman drinks her coffee in a small cafe.</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`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 1e-05
- `warmup_ratio`: 0.1
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `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`: 1e-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`: 0.1
- `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`: 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`: 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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0011 | 10 | - | 1.8733 |
| 0.0023 | 20 | - | 1.8726 |
| 0.0034 | 30 | - | 1.8714 |
| 0.0046 | 40 | - | 1.8697 |
| 0.0057 | 50 | - | 1.8675 |
| 0.0069 | 60 | - | 1.8649 |
| 0.0080 | 70 | - | 1.8619 |
| 0.0092 | 80 | - | 1.8584 |
| 0.0103 | 90 | - | 1.8544 |
| 0.0115 | 100 | 3.1046 | 1.8499 |
| 0.0126 | 110 | - | 1.8451 |
| 0.0138 | 120 | - | 1.8399 |
| 0.0149 | 130 | - | 1.8343 |
| 0.0161 | 140 | - | 1.8283 |
| 0.0172 | 150 | - | 1.8223 |
| 0.0184 | 160 | - | 1.8159 |
| 0.0195 | 170 | - | 1.8091 |
| 0.0206 | 180 | - | 1.8016 |
| 0.0218 | 190 | - | 1.7938 |
| 0.0229 | 200 | 3.0303 | 1.7858 |
| 0.0241 | 210 | - | 1.7775 |
| 0.0252 | 220 | - | 1.7693 |
| 0.0264 | 230 | - | 1.7605 |
| 0.0275 | 240 | - | 1.7514 |
| 0.0287 | 250 | - | 1.7417 |
| 0.0298 | 260 | - | 1.7320 |
| 0.0310 | 270 | - | 1.7227 |
| 0.0321 | 280 | - | 1.7134 |
| 0.0333 | 290 | - | 1.7040 |
| 0.0344 | 300 | 2.9459 | 1.6941 |
| 0.0356 | 310 | - | 1.6833 |
| 0.0367 | 320 | - | 1.6725 |
| 0.0379 | 330 | - | 1.6614 |
| 0.0390 | 340 | - | 1.6510 |
| 0.0402 | 350 | - | 1.6402 |
| 0.0413 | 360 | - | 1.6296 |
| 0.0424 | 370 | - | 1.6187 |
| 0.0436 | 380 | - | 1.6073 |
| 0.0447 | 390 | - | 1.5962 |
| 0.0459 | 400 | 2.7813 | 1.5848 |
| 0.0470 | 410 | - | 1.5735 |
| 0.0482 | 420 | - | 1.5620 |
| 0.0493 | 430 | - | 1.5495 |
| 0.0505 | 440 | - | 1.5375 |
| 0.0516 | 450 | - | 1.5256 |
| 0.0528 | 460 | - | 1.5133 |
| 0.0539 | 470 | - | 1.5012 |
| 0.0551 | 480 | - | 1.4892 |
| 0.0562 | 490 | - | 1.4769 |
| 0.0574 | 500 | 2.6308 | 1.4640 |
| 0.0585 | 510 | - | 1.4513 |
| 0.0597 | 520 | - | 1.4391 |
| 0.0608 | 530 | - | 1.4262 |
| 0.0619 | 540 | - | 1.4130 |
| 0.0631 | 550 | - | 1.3998 |
| 0.0642 | 560 | - | 1.3874 |
| 0.0654 | 570 | - | 1.3752 |
| 0.0665 | 580 | - | 1.3620 |
| 0.0677 | 590 | - | 1.3485 |
| 0.0688 | 600 | 2.4452 | 1.3350 |
| 0.0700 | 610 | - | 1.3213 |
| 0.0711 | 620 | - | 1.3088 |
| 0.0723 | 630 | - | 1.2965 |
| 0.0734 | 640 | - | 1.2839 |
| 0.0746 | 650 | - | 1.2713 |
| 0.0757 | 660 | - | 1.2592 |
| 0.0769 | 670 | - | 1.2466 |
| 0.0780 | 680 | - | 1.2332 |
| 0.0792 | 690 | - | 1.2203 |
| 0.0803 | 700 | 2.2626 | 1.2077 |
| 0.0815 | 710 | - | 1.1959 |
| 0.0826 | 720 | - | 1.1841 |
| 0.0837 | 730 | - | 1.1725 |
| 0.0849 | 740 | - | 1.1619 |
| 0.0860 | 750 | - | 1.1516 |
| 0.0872 | 760 | - | 1.1416 |
| 0.0883 | 770 | - | 1.1320 |
| 0.0895 | 780 | - | 1.1227 |
| 0.0906 | 790 | - | 1.1138 |
| 0.0918 | 800 | 2.0044 | 1.1053 |
| 0.0929 | 810 | - | 1.0965 |
| 0.0941 | 820 | - | 1.0879 |
| 0.0952 | 830 | - | 1.0796 |
| 0.0964 | 840 | - | 1.0718 |
| 0.0975 | 850 | - | 1.0644 |
| 0.0987 | 860 | - | 1.0564 |
| 0.0998 | 870 | - | 1.0490 |
| 0.1010 | 880 | - | 1.0417 |
| 0.1021 | 890 | - | 1.0354 |
| 0.1032 | 900 | 1.8763 | 1.0296 |
| 0.1044 | 910 | - | 1.0239 |
| 0.1055 | 920 | - | 1.0180 |
| 0.1067 | 930 | - | 1.0123 |
| 0.1078 | 940 | - | 1.0065 |
| 0.1090 | 950 | - | 1.0008 |
| 0.1101 | 960 | - | 0.9950 |
| 0.1113 | 970 | - | 0.9894 |
| 0.1124 | 980 | - | 0.9840 |
| 0.1136 | 990 | - | 0.9793 |
| 0.1147 | 1000 | 1.7287 | 0.9752 |
| 0.1159 | 1010 | - | 0.9706 |
| 0.1170 | 1020 | - | 0.9659 |
| 0.1182 | 1030 | - | 0.9615 |
| 0.1193 | 1040 | - | 0.9572 |
| 0.1205 | 1050 | - | 0.9531 |
| 0.1216 | 1060 | - | 0.9494 |
| 0.1227 | 1070 | - | 0.9456 |
| 0.1239 | 1080 | - | 0.9415 |
| 0.1250 | 1090 | - | 0.9377 |
| 0.1262 | 1100 | 1.6312 | 0.9339 |
| 0.1273 | 1110 | - | 0.9303 |
| 0.1285 | 1120 | - | 0.9267 |
| 0.1296 | 1130 | - | 0.9232 |
| 0.1308 | 1140 | - | 0.9197 |
| 0.1319 | 1150 | - | 0.9162 |
| 0.1331 | 1160 | - | 0.9128 |
| 0.1342 | 1170 | - | 0.9097 |
| 0.1354 | 1180 | - | 0.9069 |
| 0.1365 | 1190 | - | 0.9040 |
| 0.1377 | 1200 | 1.5316 | 0.9010 |
| 0.1388 | 1210 | - | 0.8979 |
| 0.1400 | 1220 | - | 0.8947 |
| 0.1411 | 1230 | - | 0.8915 |
| 0.1423 | 1240 | - | 0.8888 |
| 0.1434 | 1250 | - | 0.8861 |
| 0.1445 | 1260 | - | 0.8833 |
| 0.1457 | 1270 | - | 0.8806 |
| 0.1468 | 1280 | - | 0.8779 |
| 0.1480 | 1290 | - | 0.8748 |
| 0.1491 | 1300 | 1.4961 | 0.8718 |
| 0.1503 | 1310 | - | 0.8690 |
| 0.1514 | 1320 | - | 0.8664 |
| 0.1526 | 1330 | - | 0.8635 |
| 0.1537 | 1340 | - | 0.8603 |
| 0.1549 | 1350 | - | 0.8574 |
| 0.1560 | 1360 | - | 0.8545 |
| 0.1572 | 1370 | - | 0.8521 |
| 0.1583 | 1380 | - | 0.8497 |
| 0.1595 | 1390 | - | 0.8474 |
| 0.1606 | 1400 | 1.451 | 0.8453 |
| 0.1618 | 1410 | - | 0.8429 |
| 0.1629 | 1420 | - | 0.8404 |
| 0.1640 | 1430 | - | 0.8380 |
| 0.1652 | 1440 | - | 0.8357 |
| 0.1663 | 1450 | - | 0.8336 |
| 0.1675 | 1460 | - | 0.8312 |
| 0.1686 | 1470 | - | 0.8289 |
| 0.1698 | 1480 | - | 0.8262 |
| 0.1709 | 1490 | - | 0.8236 |
| 0.1721 | 1500 | 1.4177 | 0.8213 |
| 0.1732 | 1510 | - | 0.8189 |
| 0.1744 | 1520 | - | 0.8168 |
| 0.1755 | 1530 | - | 0.8147 |
| 0.1767 | 1540 | - | 0.8127 |
| 0.1778 | 1550 | - | 0.8107 |
| 0.1790 | 1560 | - | 0.8082 |
| 0.1801 | 1570 | - | 0.8059 |
| 0.1813 | 1580 | - | 0.8036 |
| 0.1824 | 1590 | - | 0.8015 |
| 0.1835 | 1600 | 1.3734 | 0.7993 |
| 0.1847 | 1610 | - | 0.7970 |
| 0.1858 | 1620 | - | 0.7948 |
| 0.1870 | 1630 | - | 0.7922 |
| 0.1881 | 1640 | - | 0.7900 |
| 0.1893 | 1650 | - | 0.7877 |
| 0.1904 | 1660 | - | 0.7852 |
| 0.1916 | 1670 | - | 0.7829 |
| 0.1927 | 1680 | - | 0.7804 |
| 0.1939 | 1690 | - | 0.7779 |
| 0.1950 | 1700 | 1.3327 | 0.7757 |
| 0.1962 | 1710 | - | 0.7738 |
| 0.1973 | 1720 | - | 0.7719 |
| 0.1985 | 1730 | - | 0.7700 |
| 0.1996 | 1740 | - | 0.7679 |
| 0.2008 | 1750 | - | 0.7658 |
| 0.2019 | 1760 | - | 0.7641 |
| 0.2031 | 1770 | - | 0.7621 |
| 0.2042 | 1780 | - | 0.7601 |
| 0.2053 | 1790 | - | 0.7580 |
| 0.2065 | 1800 | 1.2804 | 0.7558 |
| 0.2076 | 1810 | - | 0.7536 |
| 0.2088 | 1820 | - | 0.7514 |
| 0.2099 | 1830 | - | 0.7493 |
| 0.2111 | 1840 | - | 0.7473 |
| 0.2122 | 1850 | - | 0.7451 |
| 0.2134 | 1860 | - | 0.7429 |
| 0.2145 | 1870 | - | 0.7408 |
| 0.2157 | 1880 | - | 0.7389 |
| 0.2168 | 1890 | - | 0.7368 |
| 0.2180 | 1900 | 1.2255 | 0.7349 |
| 0.2191 | 1910 | - | 0.7328 |
| 0.2203 | 1920 | - | 0.7310 |
| 0.2214 | 1930 | - | 0.7293 |
| 0.2226 | 1940 | - | 0.7277 |
| 0.2237 | 1950 | - | 0.7259 |
| 0.2248 | 1960 | - | 0.7240 |
| 0.2260 | 1970 | - | 0.7221 |
| 0.2271 | 1980 | - | 0.7203 |
| 0.2283 | 1990 | - | 0.7184 |
| 0.2294 | 2000 | 1.2635 | 0.7165 |
</details>
### Framework Versions
- Python: 3.12.8
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.2.0+cu121
- Accelerate: 1.4.0
- 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",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->