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SimoneAstarita
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interstellar-ice-crystal-xs

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:416298
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Inference Endpoints
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---
base_model: Snowflake/snowflake-arctic-embed-xs
language:
- en
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:416298
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: The radial profiles using frank for the seven targets can be seen
in Figure 6.
sentences:
- At longer radio wavelengths, we selected the newest observations of the appropriate
resolution from the VLA archive.
- The radial profiles using frank for the seven targets can be seen in Figure 6.
- For further information on observation and data calibration, refer to Hunt et al.
(2021).
- source_sentence: They are extragalactic scaled up versions of galactic Ultra Compact
(UC) H ii regions, which are typically excited by a single massive star and are
≲less-than-or-similar-to\lesssim 0.1 pc in size (Wood & Churchwell, 1989).
sentences:
- They are extragalactic scaled up versions of galactic Ultra Compact (UC) H ii
regions, which are typically excited by a single massive star and are ≲less-than-or-similar-to\lesssim
0.1 pc in size (Wood & Churchwell, 1989).
- The LMT is a project operated by the Instituto Nacional de Astrófisica, Óptica,
y Electrónica (Mexico) and the University of Massachusetts at Amherst (USA).
- We measure the detection confidence in the resolved image as the ratio between
the local mean posterior and the local posterior standard deviation of the estimated
circular polarization, evaluated based on 1000 images drawn from the posterior
distribution.
- source_sentence: The flux density calibrator was 3C286, and the complex gain calibrator
was J0836-2016.
sentences:
- The flux density calibrator was 3C286, and the complex gain calibrator was J0836-2016.
- While rcsubscript𝑟cr_{\rm c} has a clear dependence on Dmaxsubscript𝐷maxD_{\rm
max}, xMMSNsubscript𝑥MMSNx_{\rm MMSN} and tagesubscript𝑡aget_{\rm age}, ΣcsubscriptΣc\Sigma_{\rm
c} only has weak dependence on Dmaxsubscript𝐷maxD_{\rm max}, and so is mostly
sensitive to the scaling of the total initial planetesimal mass, xMMSNsubscript𝑥MMSNx_{\rm
MMSN} and tagesubscript𝑡aget_{\rm age}.
- 20 is valid only at r=rc𝑟subscript𝑟cr=r_{\rm c}, it has been shown that the surface
density of dust at r>rc𝑟subscript𝑟cr>r_{\rm c} is expected to be flat for a primordial
surface density exponent (−α𝛼-\alpha) of -3/2, or more generally proportional
to r−0.6​α+0.9superscript𝑟0.6𝛼0.9r^{-0.6\alpha+0.9} (Schüppler et al., 2016; Marino
et al., 2017b; Geiler & Krivov, 2017).
- source_sentence: We would like to thank A. Deller and W. Brisken for EHT-specific
support with the use of DiFX.
sentences:
- Ice has one of the weakest strengths, and thus if we had assumed stronger solids
the derived values of Dmaxsubscript𝐷D_{\max} and xMMSNsubscript𝑥MMSNx_{\rm MMSN}
would be lower.
- We would like to thank A. Deller and W. Brisken for EHT-specific support with
the use of DiFX.
- The wsmoothsubscript𝑤smoothw_{\rm smooth} chosen parameter ranged from 10−2superscript10210^{-2}
to 10−4superscript10410^{-4} depending on the disc.
- source_sentence: New higher resolution images and our parametric modelling confirmed
this finding.
sentences:
- New higher resolution images and our parametric modelling confirmed this finding.
- With the 3-bit correlator configuration, we obtained a total bandwidth of ∼similar-to\sim8 GHz
across Ka-band.
- Pan & Schlichting, 2012) and thus could slightly affect the surface density slope.
---
# internstall-ice-crystal-xs
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs). It maps sentences & paragraphs to a 384-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:** [Snowflake/snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs) <!-- at revision 742da4f66e1823b5b4dbe6c320a1375a1fd85f9e -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0
### 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': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("SimoneAstarita/interstellar-ice-crystal-xs")
# Run inference
sentences = [
'New higher resolution images and our parametric modelling confirmed this finding.',
'New higher resolution images and our parametric modelling confirmed this finding.',
'Pan & Schlichting, 2012) and thus could slightly affect the surface density slope.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# 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
#### Unnamed Dataset
* Size: 416,298 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 42.81 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 42.81 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Resolving the inner parsec of the blazar J1924–2914 with the Event Horizon Telescope</code> | <code>Resolving the inner parsec of the blazar J1924–2914 with the Event Horizon Telescope</code> |
| <code>The radio source J1924–2914 (PKS 1921–293, OV–236) is a radio-loud quasar at a redshift z=0.353𝑧0.353z=0.353 (Wills & Wills, 1981; Jones et al., 2009).</code> | <code>The radio source J1924–2914 (PKS 1921–293, OV–236) is a radio-loud quasar at a redshift z=0.353𝑧0.353z=0.353 (Wills & Wills, 1981; Jones et al., 2009).</code> |
| <code>The source exhibits strong optical variability and is highly polarized (Wills & Wills, 1981; Pica et al., 1988; Worrall & Wilkes, 1990).</code> | <code>The source exhibits strong optical variability and is highly polarized (Wills & Wills, 1981; Pica et al., 1988; Worrall & Wilkes, 1990).</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
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `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`: 0.1
- `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`: 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`: 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
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0077 | 100 | 0.4784 |
| 0.0154 | 200 | 0.2415 |
| 0.0231 | 300 | 0.0424 |
| 0.0307 | 400 | 0.021 |
| 0.0384 | 500 | 0.0149 |
| 0.0461 | 600 | 0.0081 |
| 0.0538 | 700 | 0.0084 |
| 0.0615 | 800 | 0.0067 |
| 0.0692 | 900 | 0.0034 |
| 0.0769 | 1000 | 0.0025 |
| 0.0846 | 1100 | 0.0016 |
| 0.0077 | 100 | 0.0025 |
| 0.0154 | 200 | 0.0032 |
| 0.0231 | 300 | 0.0026 |
| 0.0307 | 400 | 0.0026 |
| 0.0384 | 500 | 0.0041 |
| 0.0461 | 600 | 0.0014 |
| 0.0538 | 700 | 0.0019 |
| 0.0615 | 800 | 0.0015 |
| 0.0692 | 900 | 0.001 |
| 0.0769 | 1000 | 0.0005 |
| 0.0846 | 1100 | 0.0004 |
| 0.0922 | 1200 | 0.0013 |
| 0.0999 | 1300 | 0.0013 |
| 0.1076 | 1400 | 0.0027 |
| 0.1153 | 1500 | 0.0018 |
| 0.1230 | 1600 | 0.001 |
| 0.1307 | 1700 | 0.0014 |
| 0.1384 | 1800 | 0.0012 |
| 0.1460 | 1900 | 0.0041 |
| 0.1537 | 2000 | 0.0009 |
| 0.1614 | 2100 | 0.0005 |
| 0.1691 | 2200 | 0.0011 |
| 0.1768 | 2300 | 0.001 |
| 0.1845 | 2400 | 0.0004 |
| 0.1922 | 2500 | 0.0011 |
| 0.1998 | 2600 | 0.0044 |
| 0.2075 | 2700 | 0.0004 |
| 0.2152 | 2800 | 0.0022 |
| 0.2229 | 2900 | 0.0007 |
| 0.2306 | 3000 | 0.0006 |
| 0.2383 | 3100 | 0.0002 |
| 0.2460 | 3200 | 0.0006 |
| 0.2537 | 3300 | 0.0004 |
| 0.2613 | 3400 | 0.0013 |
| 0.2690 | 3500 | 0.0006 |
| 0.2767 | 3600 | 0.0005 |
| 0.2844 | 3700 | 0.0018 |
| 0.2921 | 3800 | 0.0023 |
| 0.2998 | 3900 | 0.0011 |
| 0.3075 | 4000 | 0.0007 |
| 0.3151 | 4100 | 0.0008 |
| 0.3228 | 4200 | 0.0013 |
| 0.3305 | 4300 | 0.0012 |
| 0.3382 | 4400 | 0.001 |
| 0.3459 | 4500 | 0.0016 |
| 0.3536 | 4600 | 0.0025 |
| 0.3613 | 4700 | 0.0015 |
| 0.3689 | 4800 | 0.0018 |
| 0.3766 | 4900 | 0.0019 |
| 0.3843 | 5000 | 0.0021 |
| 0.3920 | 5100 | 0.0018 |
| 0.3997 | 5200 | 0.0004 |
| 0.4074 | 5300 | 0.0006 |
| 0.4151 | 5400 | 0.0007 |
| 0.4228 | 5500 | 0.0009 |
| 0.4304 | 5600 | 0.0004 |
| 0.4381 | 5700 | 0.0003 |
| 0.4458 | 5800 | 0.0007 |
| 0.4535 | 5900 | 0.0013 |
| 0.4612 | 6000 | 0.0007 |
| 0.4689 | 6100 | 0.0005 |
| 0.4766 | 6200 | 0.001 |
| 0.4842 | 6300 | 0.0027 |
| 0.4919 | 6400 | 0.0018 |
| 0.4996 | 6500 | 0.0006 |
| 0.5073 | 6600 | 0.0008 |
| 0.5150 | 6700 | 0.0006 |
| 0.5227 | 6800 | 0.0007 |
| 0.5304 | 6900 | 0.001 |
| 0.5380 | 7000 | 0.0007 |
| 0.5457 | 7100 | 0.0005 |
| 0.5534 | 7200 | 0.0012 |
| 0.5611 | 7300 | 0.0012 |
| 0.5688 | 7400 | 0.0011 |
| 0.5765 | 7500 | 0.0005 |
| 0.5842 | 7600 | 0.0013 |
| 0.5919 | 7700 | 0.0012 |
| 0.5995 | 7800 | 0.0007 |
| 0.6072 | 7900 | 0.0012 |
| 0.6149 | 8000 | 0.0012 |
| 0.6226 | 8100 | 0.0003 |
| 0.6303 | 8200 | 0.0003 |
| 0.6380 | 8300 | 0.0003 |
| 0.6457 | 8400 | 0.002 |
| 0.6533 | 8500 | 0.0003 |
| 0.6610 | 8600 | 0.0016 |
| 0.6687 | 8700 | 0.0003 |
| 0.6764 | 8800 | 0.0002 |
| 0.6841 | 8900 | 0.0006 |
| 0.6918 | 9000 | 0.0005 |
| 0.6995 | 9100 | 0.0017 |
| 0.7071 | 9200 | 0.0037 |
| 0.7148 | 9300 | 0.0005 |
| 0.7225 | 9400 | 0.0006 |
| 0.7302 | 9500 | 0.0004 |
| 0.7379 | 9600 | 0.0002 |
| 0.7456 | 9700 | 0.0008 |
| 0.7533 | 9800 | 0.0005 |
| 0.7610 | 9900 | 0.0006 |
| 0.7686 | 10000 | 0.0004 |
| 0.7763 | 10100 | 0.0004 |
| 0.7840 | 10200 | 0.0006 |
| 0.7917 | 10300 | 0.0019 |
| 0.7994 | 10400 | 0.0007 |
| 0.8071 | 10500 | 0.0003 |
| 0.8148 | 10600 | 0.0003 |
| 0.8224 | 10700 | 0.0005 |
| 0.8301 | 10800 | 0.0009 |
| 0.8378 | 10900 | 0.0006 |
| 0.8455 | 11000 | 0.002 |
| 0.8532 | 11100 | 0.0018 |
| 0.8609 | 11200 | 0.0009 |
| 0.8686 | 11300 | 0.0004 |
| 0.8762 | 11400 | 0.0005 |
| 0.8839 | 11500 | 0.0008 |
| 0.8916 | 11600 | 0.0003 |
| 0.8993 | 11700 | 0.0002 |
| 0.9070 | 11800 | 0.0004 |
| 0.9147 | 11900 | 0.0007 |
| 0.9224 | 12000 | 0.0009 |
| 0.9301 | 12100 | 0.0007 |
| 0.9377 | 12200 | 0.0007 |
| 0.9454 | 12300 | 0.0009 |
| 0.9531 | 12400 | 0.0007 |
| 0.9608 | 12500 | 0.0009 |
| 0.9685 | 12600 | 0.0004 |
| 0.9762 | 12700 | 0.0002 |
| 0.9839 | 12800 | 0.0003 |
| 0.9915 | 12900 | 0.0002 |
| 0.9992 | 13000 | 0.0002 |
| 1.0069 | 13100 | 0.0006 |
| 1.0146 | 13200 | 0.0007 |
| 1.0223 | 13300 | 0.0007 |
| 1.0300 | 13400 | 0.0005 |
| 1.0377 | 13500 | 0.0008 |
| 1.0453 | 13600 | 0.0016 |
| 1.0530 | 13700 | 0.0007 |
| 1.0607 | 13800 | 0.0013 |
| 1.0684 | 13900 | 0.0005 |
| 1.0761 | 14000 | 0.0002 |
| 1.0838 | 14100 | 0.0001 |
| 1.0915 | 14200 | 0.0003 |
| 1.0992 | 14300 | 0.0003 |
| 1.1068 | 14400 | 0.0006 |
| 1.1145 | 14500 | 0.0002 |
| 1.1222 | 14600 | 0.0003 |
| 1.1299 | 14700 | 0.0002 |
| 1.1376 | 14800 | 0.0006 |
| 1.1453 | 14900 | 0.0011 |
| 1.1530 | 15000 | 0.0004 |
| 1.1606 | 15100 | 0.0001 |
| 1.1683 | 15200 | 0.0003 |
| 1.1760 | 15300 | 0.0001 |
| 1.1837 | 15400 | 0.0002 |
| 1.1914 | 15500 | 0.0001 |
| 1.1991 | 15600 | 0.003 |
| 1.2068 | 15700 | 0.0001 |
| 1.2145 | 15800 | 0.0002 |
| 1.2221 | 15900 | 0.0005 |
| 1.2298 | 16000 | 0.0004 |
| 1.2375 | 16100 | 0.0001 |
| 1.2452 | 16200 | 0.0003 |
| 1.2529 | 16300 | 0.0003 |
| 1.2606 | 16400 | 0.0008 |
| 1.2683 | 16500 | 0.0004 |
| 1.2759 | 16600 | 0.0001 |
| 1.2836 | 16700 | 0.0002 |
| 1.2913 | 16800 | 0.0011 |
| 1.2990 | 16900 | 0.0001 |
| 1.3067 | 17000 | 0.0001 |
| 1.3144 | 17100 | 0.0002 |
| 1.3221 | 17200 | 0.0005 |
| 1.3297 | 17300 | 0.0012 |
| 1.3374 | 17400 | 0.0003 |
| 1.3451 | 17500 | 0.0002 |
| 1.3528 | 17600 | 0.0009 |
| 1.3605 | 17700 | 0.0003 |
| 1.3682 | 17800 | 0.0005 |
| 1.3759 | 17900 | 0.0008 |
| 1.3836 | 18000 | 0.0005 |
| 1.3912 | 18100 | 0.0007 |
| 1.3989 | 18200 | 0.0002 |
| 1.4066 | 18300 | 0.0003 |
| 1.4143 | 18400 | 0.0002 |
| 1.4220 | 18500 | 0.0001 |
| 1.4297 | 18600 | 0.0001 |
| 1.4374 | 18700 | 0.0001 |
| 1.4450 | 18800 | 0.0005 |
| 1.4527 | 18900 | 0.0002 |
| 1.4604 | 19000 | 0.0001 |
| 1.4681 | 19100 | 0.0002 |
| 1.4758 | 19200 | 0.0006 |
| 1.4835 | 19300 | 0.0015 |
| 1.4912 | 19400 | 0.0012 |
| 1.4988 | 19500 | 0.0003 |
| 1.5065 | 19600 | 0.0005 |
| 1.5142 | 19700 | 0.0001 |
| 1.5219 | 19800 | 0.0002 |
| 1.5296 | 19900 | 0.0009 |
| 1.5373 | 20000 | 0.0002 |
| 1.5450 | 20100 | 0.0001 |
| 1.5527 | 20200 | 0.0003 |
| 1.5603 | 20300 | 0.0006 |
| 1.5680 | 20400 | 0.0002 |
| 1.5757 | 20500 | 0.0004 |
| 1.5834 | 20600 | 0.0006 |
| 1.5911 | 20700 | 0.0004 |
| 1.5988 | 20800 | 0.0002 |
| 1.6065 | 20900 | 0.0006 |
| 1.6141 | 21000 | 0.0006 |
| 1.6218 | 21100 | 0.0001 |
| 1.6295 | 21200 | 0.0001 |
| 1.6372 | 21300 | 0.0001 |
| 1.6449 | 21400 | 0.0008 |
| 1.6526 | 21500 | 0.0001 |
| 1.6603 | 21600 | 0.0005 |
| 1.6679 | 21700 | 0.0001 |
| 1.6756 | 21800 | 0.0001 |
| 1.6833 | 21900 | 0.0001 |
| 1.6910 | 22000 | 0.0001 |
| 1.6987 | 22100 | 0.0008 |
| 1.7064 | 22200 | 0.0014 |
| 1.7141 | 22300 | 0.0002 |
| 1.7218 | 22400 | 0.0007 |
| 1.7294 | 22500 | 0.0001 |
| 1.7371 | 22600 | 0.0001 |
| 1.7448 | 22700 | 0.0001 |
| 1.7525 | 22800 | 0.0002 |
| 1.7602 | 22900 | 0.0002 |
| 1.7679 | 23000 | 0.0001 |
| 1.7756 | 23100 | 0.0001 |
| 1.7832 | 23200 | 0.0005 |
| 1.7909 | 23300 | 0.0004 |
| 1.7986 | 23400 | 0.0002 |
| 1.8063 | 23500 | 0.0001 |
| 1.8140 | 23600 | 0.0001 |
| 1.8217 | 23700 | 0.0001 |
| 1.8294 | 23800 | 0.0004 |
| 1.8370 | 23900 | 0.0002 |
| 1.8447 | 24000 | 0.0002 |
| 1.8524 | 24100 | 0.0013 |
| 1.8601 | 24200 | 0.0004 |
| 1.8678 | 24300 | 0.0002 |
| 1.8755 | 24400 | 0.0002 |
| 1.8832 | 24500 | 0.0001 |
| 1.8909 | 24600 | 0.0001 |
| 1.8985 | 24700 | 0.0001 |
| 1.9062 | 24800 | 0.0002 |
| 1.9139 | 24900 | 0.0005 |
| 1.9216 | 25000 | 0.0001 |
| 1.9293 | 25100 | 0.0001 |
| 1.9370 | 25200 | 0.0002 |
| 1.9447 | 25300 | 0.0002 |
| 1.9523 | 25400 | 0.0006 |
| 1.9600 | 25500 | 0.0004 |
| 1.9677 | 25600 | 0.0002 |
| 1.9754 | 25700 | 0.0001 |
| 1.9831 | 25800 | 0.0001 |
| 1.9908 | 25900 | 0.0001 |
| 1.9985 | 26000 | 0.0001 |
| 2.0061 | 26100 | 0.0002 |
| 2.0138 | 26200 | 0.0007 |
| 2.0215 | 26300 | 0.0003 |
| 2.0292 | 26400 | 0.0001 |
| 2.0369 | 26500 | 0.0011 |
| 2.0446 | 26600 | 0.0002 |
| 2.0523 | 26700 | 0.0001 |
| 2.0600 | 26800 | 0.0002 |
| 2.0676 | 26900 | 0.0004 |
| 2.0753 | 27000 | 0.0001 |
| 2.0830 | 27100 | 0.0001 |
| 2.0907 | 27200 | 0.0001 |
| 2.0984 | 27300 | 0.0002 |
| 2.1061 | 27400 | 0.0001 |
| 2.1138 | 27500 | 0.0001 |
| 2.1214 | 27600 | 0.0001 |
| 2.1291 | 27700 | 0.0001 |
| 2.1368 | 27800 | 0.0003 |
| 2.1445 | 27900 | 0.0012 |
| 2.1522 | 28000 | 0.0001 |
| 2.1599 | 28100 | 0.0001 |
| 2.1676 | 28200 | 0.0001 |
| 2.1752 | 28300 | 0.0001 |
| 2.1829 | 28400 | 0.0001 |
| 2.1906 | 28500 | 0.0001 |
| 2.1983 | 28600 | 0.0014 |
| 2.2060 | 28700 | 0.0001 |
| 2.2137 | 28800 | 0.0001 |
| 2.2214 | 28900 | 0.0002 |
| 2.2291 | 29000 | 0.0 |
| 2.2367 | 29100 | 0.0001 |
| 2.2444 | 29200 | 0.0001 |
| 2.2521 | 29300 | 0.0001 |
| 2.2598 | 29400 | 0.0001 |
| 2.2675 | 29500 | 0.0001 |
| 2.2752 | 29600 | 0.0001 |
| 2.2829 | 29700 | 0.0001 |
| 2.2905 | 29800 | 0.0001 |
| 2.2982 | 29900 | 0.0001 |
| 2.3059 | 30000 | 0.0001 |
| 2.3136 | 30100 | 0.0001 |
| 2.3213 | 30200 | 0.0002 |
| 2.3290 | 30300 | 0.0011 |
| 2.3367 | 30400 | 0.0001 |
| 2.3444 | 30500 | 0.0001 |
| 2.3520 | 30600 | 0.0005 |
| 2.3597 | 30700 | 0.0001 |
| 2.3674 | 30800 | 0.0001 |
| 2.3751 | 30900 | 0.0006 |
| 2.3828 | 31000 | 0.0001 |
| 2.3905 | 31100 | 0.0001 |
| 2.3982 | 31200 | 0.0002 |
| 2.4058 | 31300 | 0.0001 |
| 2.4135 | 31400 | 0.0001 |
| 2.4212 | 31500 | 0.0001 |
| 2.4289 | 31600 | 0.0001 |
| 2.4366 | 31700 | 0.0001 |
| 2.4443 | 31800 | 0.0004 |
| 2.4520 | 31900 | 0.0001 |
| 2.4596 | 32000 | 0.0001 |
| 2.4673 | 32100 | 0.0002 |
| 2.4750 | 32200 | 0.0002 |
| 2.4827 | 32300 | 0.0004 |
| 2.4904 | 32400 | 0.0008 |
| 2.4981 | 32500 | 0.0001 |
| 2.5058 | 32600 | 0.0001 |
| 2.5135 | 32700 | 0.0001 |
| 2.5211 | 32800 | 0.0001 |
| 2.5288 | 32900 | 0.0006 |
| 2.5365 | 33000 | 0.0001 |
| 2.5442 | 33100 | 0.0001 |
| 2.5519 | 33200 | 0.0002 |
| 2.5596 | 33300 | 0.0001 |
| 2.5673 | 33400 | 0.0002 |
| 2.5749 | 33500 | 0.0001 |
| 2.5826 | 33600 | 0.0001 |
| 2.5903 | 33700 | 0.0001 |
| 2.5980 | 33800 | 0.0001 |
| 2.6057 | 33900 | 0.0001 |
| 2.6134 | 34000 | 0.0007 |
| 2.6211 | 34100 | 0.0 |
| 2.6287 | 34200 | 0.0001 |
| 2.6364 | 34300 | 0.0001 |
| 2.6441 | 34400 | 0.0006 |
| 2.6518 | 34500 | 0.0001 |
| 2.6595 | 34600 | 0.0001 |
| 2.6672 | 34700 | 0.0001 |
| 2.6749 | 34800 | 0.0 |
| 2.6826 | 34900 | 0.0001 |
| 2.6902 | 35000 | 0.0001 |
| 2.6979 | 35100 | 0.0005 |
| 2.7056 | 35200 | 0.0006 |
| 2.7133 | 35300 | 0.0001 |
| 2.7210 | 35400 | 0.0005 |
| 2.7287 | 35500 | 0.0001 |
| 2.7364 | 35600 | 0.0001 |
| 2.7440 | 35700 | 0.0001 |
| 2.7517 | 35800 | 0.0001 |
| 2.7594 | 35900 | 0.0001 |
| 2.7671 | 36000 | 0.0001 |
| 2.7748 | 36100 | 0.0001 |
| 2.7825 | 36200 | 0.0005 |
| 2.7902 | 36300 | 0.0001 |
| 2.7978 | 36400 | 0.0001 |
| 2.8055 | 36500 | 0.0001 |
| 2.8132 | 36600 | 0.0001 |
| 2.8209 | 36700 | 0.0001 |
| 2.8286 | 36800 | 0.0001 |
| 2.8363 | 36900 | 0.0001 |
| 2.8440 | 37000 | 0.0001 |
| 2.8517 | 37100 | 0.0013 |
| 2.8593 | 37200 | 0.0001 |
| 2.8670 | 37300 | 0.0001 |
| 2.8747 | 37400 | 0.0001 |
| 2.8824 | 37500 | 0.0001 |
| 2.8901 | 37600 | 0.0001 |
| 2.8978 | 37700 | 0.0001 |
| 2.9055 | 37800 | 0.0001 |
| 2.9131 | 37900 | 0.0002 |
| 2.9208 | 38000 | 0.0001 |
| 2.9285 | 38100 | 0.0001 |
| 2.9362 | 38200 | 0.0001 |
| 2.9439 | 38300 | 0.0001 |
| 2.9516 | 38400 | 0.0004 |
| 2.9593 | 38500 | 0.0001 |
| 2.9669 | 38600 | 0.0001 |
| 2.9746 | 38700 | 0.0001 |
| 2.9823 | 38800 | 0.0001 |
| 2.9900 | 38900 | 0.0001 |
| 2.9977 | 39000 | 0.0001 |
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.0
- Transformers: 4.44.2
- PyTorch: 2.4.0+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.0
- 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",
}
```
#### 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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