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my_new_model

Sentence Similarity
sentence-transformers
Safetensors
new
feature-extraction
Generated from Trainer
dataset_size:583058
loss:MultipleNegativesRankingLoss
custom_code
text-embeddings-inference
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Add new SentenceTransformer model
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 ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:583058
- loss:MultipleNegativesRankingLoss
base_model: Alibaba-NLP/gte-multilingual-base
widget:
- source_sentence: 'Pre-Emphasis (PE)
A pre-emphasis filter is applied to the framed offset-free input signal:
)1
('
sentences:
- 'Windowing (W)
A Hamming window of length N is applied to the output of the pre-emphasis block:
(
)
N
n
n
s
N
n
n
s
pe
w
≤
≤
×
−
−
×
−
='
- 'Group or broadcast call, called mobile stations (GSM only)
Within each set of voice group call or voice broadcast call attributes stored
in the GCR as defined in 3GPP TS 43.068
and 3GPP TS 43.069, respectively, a priority level is included if eMLPP is applied.
The priority level will be provided
by the GCR to the MSC together with the call attributes.
The priority level shall be indicated together with the related notification messages
and treated in the mobile station as
defined in 3GPP TS 43.0'
- 'Description of the access technology indicator mechanism
This clause describes the mechanisms that can be employed to indicate access technology
specific dependencies in a
multi-access technology environment.
There are cases where toolkit applications need to know which access technology
the terminal is currently in so that it
can issue access technology dependent commands as well as determine that the response
to a particular command is
technology dependent. Setting up the event, ACCESS TECHNOL'
- source_sentence: 'Distribution of DL delay between NG-RAN and UE
a) This measurement provides the distribution of DL packet delay between NG-RAN
and UE, which is the delay
incurred in NG-RAN (including the delay at gNB-CU-UP, on F1-U and on gNB-DU) and
the delay over Uu
interface. This measurement is split into subcounters per 5QI and subcounters
per S-NSSAI.
b) DER (n=1).
ETSI
ETSI TS 128 552 V16.18.0 (2024-08)'
sentences:
- 'Distribution of UL delay between NG-RAN and UE
a) This measurement provides the distribution of UL packet delay between NG-RAN
and UE, which is the delay
incurred in NG-RAN (including the delay at gNB-CU-UP, on F1-U and on gNB-DU) and
the delay over Uu
interface. This measurement is split into subcounters per 5QI and subcounters
per S-NSSAI.
b) DER (n=1).
c) The measurement is obtained by the following method:
The gNB performs the GTP PDU packet delay measurement for QoS monitoring per the
GTP '
- 'Subscriber data
Subscription to MExE services shall be logically separate to subscription of network
services. A subscriber may have a
MExE subscription to multiple MExE service providers. It may also be possible
for the subscriber to interrogate such
subscription registration (with a suitable means of authorisation), depending
on PLMN support.'
- 'MSC for LMU Control
When a control message has to be routed to an LMU from an SMLC, the SMLC addresses
the serving MSC for the
LMU using an E.164 address.
ETSI
ETSI TS 129 002 V10.6.0 (2012-04)'
- source_sentence: 'Enter SMS Block Mode Protocol +CESP
Table 3.2.4-1: +CESP Action Command Syntax
Command
Possible response(s)
+CESP
+CESP=?
Description
Execution command sets the TA in SMS block protocol mode. The TA shall return
OK (or 0) to confirm acceptance of
the command prior to entering the block mode (see clause 2.1.1). The final result
code OK (or 0) shall be returned when
the block mode is exited.
NOTE:
Commands following +CESP in the AT command line must not be processed by the TA.
Implementation
Ma'
sentences:
- 'SGSN
To support NBIFOM, the SGSN needs to be capable to:
ETSI
ETSI TS 123 161 V14.0.0 (2017-05)'
- 'Message Service Failure Result Code +CMS ERROR
Final result code +CMS ERROR: <err> indicates an error related to mobile equipment
or network. The operation is
similar to ERROR final result code. None of the following commands in the same
command line is executed. Neither
ERROR nor OK final result code shall be returned. ERROR is returned normally when
error is related to syntax or invalid
parameters.
Defined Values
<err> values used by common messaging commands:'
- 'C
C
-
-
P
Service Priority Level'
- source_sentence: 'Definition
Cell synchronization accuracy is defined as the maximum deviation in frame start
times between any pair of cells on the
same frequency that have overlapping coverage areas.'
sentences:
- 'Minimum requirements
The cell synchronization accuracy shall be better than or equal to 3μs.'
- "Subsequent Inter-MSC Handover to third MSC\nWhen a Mobile Station is being handed\
\ over to a third MSC, the procedure (described in GSM 03.09)\ndoes require one\
\ specific interworking case in MSC-A (figure 20) between E-Interface from MSC-B\
\ and E-\nInterface from MSC-B' other than the combination of the ones described\
\ in the chapter 4.5.1 and 4.5.2.\n%66\x10$\x03\x03\x03\x03\x03\x0306&\x10%\x03\
\x03\x03\x03\x03\x03\x03\x03\x03\x03\x03\x03\x03\x03\x03\x03\x03\x0306&\x10$\x03\
\x03\x03\x03\x03\x03\x03\x03\x03\x03\x03\x0306&\x10%\n_\x03\x03\x03\x03\x03\x03\
\x03\x03\x03\x03\x03\x03_\x03\x03\x03\x03\x03\x03\x03\x03\x03\x03\x03\x03\x03\x03\
\x03\x03\x03\x03\x03\x03\x03\x03_\x03\x03\x03\x03\x03\x03\x03\x03\x03\x03\x03\x03\
\x03\x03\x03\x03_\n_+$1'29(5\x03\x03\x03\x03_\x03\x03\x03\x03\x03\x03\x03\x03\x03\
\x03\x03\x03\x03\x03\x03\x03\x03\x03\x03\x03\x03\x03_\x03\x03\x03\x03"
- 'DL Total PRB Usage
a) This measurement provides the total usage (in percentage) of physical resource
blocks (PRBs) on the downlink
for any purpose.
b) SI
c) This measurement is obtained as:


∗
='
- source_sentence: Carrier aggregation measurement accuracy
sentences:
- 'PUCCH / PUSCH / SRS time mask
The PUCCH/PUSCH/SRS time mask defines the observation period between sounding
reference symbol (SRS) and an
adjacent PUSCH/PUCCH symbol and subsequent sub-frame.
There are no additional requirements on UE transmit power beyond that which is
required in subclause 6.2.2 and
subclause 6.6.2.3
ETSI
ETSI TS 136 101 V9.16.0 (2013-07)'
- 'Reference Signal Time Difference (RSTD) Measurement Accuracy
Requirements for Carrier Aggregation
A.8
UE Measurements Procedures
A.9
Measurement Performance Requirements
NOTE:
Only requirements and test cases in this table defined for inter-band carrier
aggregation shall apply.
ETSI
ETSI TS 136 307 V10.17.0 (2016-01)'
- 'Operator control
Three general architectures are candidates to offer energy savings functionalities:
Distributed, NM-Centralized, EM-Centralized as defined in TS 32.500 [6].
Energy savings in cells can be initiated in several different ways. Some of the
mechanisms are:
For NM-centralized architecture
-
IRPManager instructs the cells to move to energySaving state (e.g. according to
a schedule determined by
network statistics) , configures trigger points (e.g. load threshold crossing)
when it want'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base). 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:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision 9fdd4ee8bba0e2808a34e0e739576f6740d2b225 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NewModel
(1): Pooling({'word_embedding_dimension': 768, '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("lucian-li/my_new_model")
# Run inference
sentences = [
'Carrier aggregation measurement accuracy',
'Reference Signal Time Difference (RSTD) Measurement Accuracy\nRequirements for Carrier Aggregation\nA.8\nUE Measurements Procedures\nA.9\nMeasurement Performance Requirements\nNOTE:\nOnly requirements and test cases in this table defined for inter-band carrier aggregation shall apply.\n\n\nETSI\nETSI TS 136 307 V10.17.0 (2016-01)',
'Operator control\nThree general architectures are candidates to offer energy savings functionalities:\nDistributed, NM-Centralized, EM-Centralized as defined in TS 32.500 [6].\nEnergy savings in cells can be initiated in several different ways. Some of the mechanisms are:\nFor NM-centralized architecture\n-\nIRPManager instructs the cells to move to energySaving state (e.g. according to a schedule determined by\nnetwork statistics) , configures trigger points (e.g. load threshold crossing) when it want',
]
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
#### Unnamed Dataset
* Size: 583,058 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: 7 tokens</li><li>mean: 85.73 tokens</li><li>max: 229 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 85.86 tokens</li><li>max: 229 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Triggering Optimization Function (TG_F)<br>This functional bloc supports the following functions: [SO2], [SO3].</code> | <code>Optimization Fallback Function (O_FB_F)<br>This functional bloc supports the following functions: [SO7], [SO9], [SO10].</code> |
| <code>Optimization Fallback Function (O_FB_F)<br>This functional bloc supports the following functions: [SO7], [SO9], [SO10].</code> | <code>Self-Optimization Progress Update Function (SO_PGS_UF)<br>This function updates the self-optimization progress and important events to the operator: [SO11]</code> |
| <code>Self-Optimization Progress Update Function (SO_PGS_UF)<br>This function updates the self-optimization progress and important events to the operator: [SO11]</code> | <code>NRM IRP Update Function (NRM_UF)<br>This function updates the E-UTRAN and EPC NRM IRP with the optimization modification if needed.</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`: 11
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
#### 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`: 11
- `per_device_eval_batch_size`: 8
- `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`: 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`: 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}
- `tp_size`: 0
- `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
- `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
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0019 | 100 | 0.8198 |
| 0.0038 | 200 | 0.7651 |
| 0.0057 | 300 | 0.6659 |
| 0.0075 | 400 | 0.6404 |
| 0.0094 | 500 | 0.5638 |
| 0.0113 | 600 | 0.5184 |
| 0.0132 | 700 | 0.448 |
| 0.0151 | 800 | 0.4464 |
| 0.0170 | 900 | 0.3461 |
| 0.0189 | 1000 | 0.3731 |
| 0.0208 | 1100 | 0.343 |
| 0.0226 | 1200 | 0.3557 |
| 0.0245 | 1300 | 0.3623 |
| 0.0264 | 1400 | 0.2941 |
| 0.0283 | 1500 | 0.3153 |
| 0.0302 | 1600 | 0.2724 |
| 0.0321 | 1700 | 0.2702 |
| 0.0340 | 1800 | 0.2934 |
| 0.0358 | 1900 | 0.2255 |
| 0.0377 | 2000 | 0.2519 |
| 0.0396 | 2100 | 0.2424 |
| 0.0415 | 2200 | 0.1883 |
| 0.0434 | 2300 | 0.2428 |
| 0.0453 | 2400 | 0.2212 |
| 0.0472 | 2500 | 0.1862 |
| 0.0491 | 2600 | 0.2451 |
| 0.0509 | 2700 | 0.2336 |
| 0.0528 | 2800 | 0.225 |
| 0.0547 | 2900 | 0.2154 |
| 0.0566 | 3000 | 0.1907 |
| 0.0585 | 3100 | 0.2514 |
| 0.0604 | 3200 | 0.2082 |
| 0.0623 | 3300 | 0.2076 |
| 0.0641 | 3400 | 0.1818 |
| 0.0660 | 3500 | 0.1688 |
| 0.0679 | 3600 | 0.2261 |
| 0.0698 | 3700 | 0.2108 |
| 0.0717 | 3800 | 0.1732 |
| 0.0736 | 3900 | 0.1764 |
| 0.0755 | 4000 | 0.1481 |
| 0.0773 | 4100 | 0.1687 |
| 0.0792 | 4200 | 0.1897 |
| 0.0811 | 4300 | 0.1685 |
| 0.0830 | 4400 | 0.1915 |
| 0.0849 | 4500 | 0.2013 |
| 0.0868 | 4600 | 0.1701 |
| 0.0887 | 4700 | 0.2006 |
| 0.0906 | 4800 | 0.2006 |
| 0.0924 | 4900 | 0.1617 |
| 0.0943 | 5000 | 0.1406 |
| 0.0962 | 5100 | 0.1456 |
| 0.0981 | 5200 | 0.1703 |
| 0.1000 | 5300 | 0.1464 |
| 0.1019 | 5400 | 0.1803 |
| 0.1038 | 5500 | 0.1346 |
| 0.1056 | 5600 | 0.134 |
| 0.1075 | 5700 | 0.1567 |
| 0.1094 | 5800 | 0.163 |
| 0.1113 | 5900 | 0.1544 |
| 0.1132 | 6000 | 0.1648 |
| 0.1151 | 6100 | 0.1505 |
| 0.1170 | 6200 | 0.1231 |
| 0.1189 | 6300 | 0.1591 |
| 0.1207 | 6400 | 0.1533 |
| 0.1226 | 6500 | 0.1376 |
| 0.1245 | 6600 | 0.1473 |
| 0.1264 | 6700 | 0.1405 |
| 0.1283 | 6800 | 0.141 |
| 0.1302 | 6900 | 0.1105 |
| 0.1321 | 7000 | 0.1712 |
| 0.1339 | 7100 | 0.1534 |
| 0.1358 | 7200 | 0.1578 |
| 0.1377 | 7300 | 0.1101 |
| 0.1396 | 7400 | 0.128 |
| 0.1415 | 7500 | 0.1679 |
| 0.1434 | 7600 | 0.1592 |
| 0.1453 | 7700 | 0.1383 |
| 0.1472 | 7800 | 0.1274 |
| 0.1490 | 7900 | 0.1616 |
| 0.1509 | 8000 | 0.1617 |
| 0.1528 | 8100 | 0.1361 |
| 0.1547 | 8200 | 0.1268 |
| 0.1566 | 8300 | 0.1286 |
| 0.1585 | 8400 | 0.1253 |
| 0.1604 | 8500 | 0.1157 |
| 0.1622 | 8600 | 0.1499 |
| 0.1641 | 8700 | 0.1398 |
| 0.1660 | 8800 | 0.1188 |
| 0.1679 | 8900 | 0.1103 |
| 0.1698 | 9000 | 0.1217 |
| 0.1717 | 9100 | 0.1144 |
| 0.1736 | 9200 | 0.1203 |
| 0.1755 | 9300 | 0.1074 |
| 0.1773 | 9400 | 0.1145 |
| 0.1792 | 9500 | 0.1035 |
| 0.1811 | 9600 | 0.1406 |
| 0.1830 | 9700 | 0.1465 |
| 0.1849 | 9800 | 0.1169 |
| 0.1868 | 9900 | 0.1115 |
| 0.1887 | 10000 | 0.1207 |
| 0.1905 | 10100 | 0.1191 |
| 0.1924 | 10200 | 0.1099 |
| 0.1943 | 10300 | 0.1309 |
| 0.1962 | 10400 | 0.1092 |
| 0.1981 | 10500 | 0.1075 |
| 0.2000 | 10600 | 0.1174 |
| 0.2019 | 10700 | 0.1103 |
| 0.2038 | 10800 | 0.1077 |
| 0.2056 | 10900 | 0.0844 |
| 0.2075 | 11000 | 0.1093 |
| 0.2094 | 11100 | 0.1428 |
| 0.2113 | 11200 | 0.0928 |
| 0.2132 | 11300 | 0.1039 |
| 0.2151 | 11400 | 0.1436 |
| 0.2170 | 11500 | 0.1197 |
| 0.2188 | 11600 | 0.1249 |
| 0.2207 | 11700 | 0.0856 |
| 0.2226 | 11800 | 0.1126 |
| 0.2245 | 11900 | 0.1028 |
| 0.2264 | 12000 | 0.0988 |
| 0.2283 | 12100 | 0.1031 |
| 0.2302 | 12200 | 0.101 |
| 0.2320 | 12300 | 0.1188 |
| 0.2339 | 12400 | 0.0908 |
| 0.2358 | 12500 | 0.069 |
| 0.2377 | 12600 | 0.1099 |
| 0.2396 | 12700 | 0.1227 |
| 0.2415 | 12800 | 0.0794 |
| 0.2434 | 12900 | 0.0969 |
| 0.2453 | 13000 | 0.0864 |
| 0.2471 | 13100 | 0.1193 |
| 0.2490 | 13200 | 0.0824 |
| 0.2509 | 13300 | 0.12 |
| 0.2528 | 13400 | 0.0928 |
| 0.2547 | 13500 | 0.1126 |
| 0.2566 | 13600 | 0.0912 |
| 0.2585 | 13700 | 0.1126 |
| 0.2603 | 13800 | 0.078 |
| 0.2622 | 13900 | 0.0715 |
| 0.2641 | 14000 | 0.1095 |
| 0.2660 | 14100 | 0.089 |
| 0.2679 | 14200 | 0.0926 |
| 0.2698 | 14300 | 0.086 |
| 0.2717 | 14400 | 0.1115 |
| 0.2736 | 14500 | 0.0996 |
| 0.2754 | 14600 | 0.1014 |
| 0.2773 | 14700 | 0.1033 |
| 0.2792 | 14800 | 0.0732 |
| 0.2811 | 14900 | 0.0994 |
| 0.2830 | 15000 | 0.0872 |
| 0.2849 | 15100 | 0.0923 |
| 0.2868 | 15200 | 0.111 |
| 0.2886 | 15300 | 0.0891 |
| 0.2905 | 15400 | 0.0868 |
| 0.2924 | 15500 | 0.0773 |
| 0.2943 | 15600 | 0.0918 |
| 0.2962 | 15700 | 0.0726 |
| 0.2981 | 15800 | 0.0951 |
| 0.3000 | 15900 | 0.0835 |
| 0.3019 | 16000 | 0.083 |
| 0.3037 | 16100 | 0.095 |
| 0.3056 | 16200 | 0.0722 |
| 0.3075 | 16300 | 0.1061 |
| 0.3094 | 16400 | 0.0902 |
| 0.3113 | 16500 | 0.0978 |
| 0.3132 | 16600 | 0.0983 |
| 0.3151 | 16700 | 0.0808 |
| 0.3169 | 16800 | 0.0758 |
| 0.3188 | 16900 | 0.071 |
| 0.3207 | 17000 | 0.0918 |
| 0.3226 | 17100 | 0.1011 |
| 0.3245 | 17200 | 0.079 |
| 0.3264 | 17300 | 0.0992 |
| 0.3283 | 17400 | 0.1089 |
| 0.3302 | 17500 | 0.0904 |
| 0.3320 | 17600 | 0.0956 |
| 0.3339 | 17700 | 0.0747 |
| 0.3358 | 17800 | 0.0961 |
| 0.3377 | 17900 | 0.0923 |
| 0.3396 | 18000 | 0.1114 |
| 0.3415 | 18100 | 0.0689 |
| 0.3434 | 18200 | 0.1308 |
| 0.3452 | 18300 | 0.0923 |
| 0.3471 | 18400 | 0.0756 |
| 0.3490 | 18500 | 0.0842 |
| 0.3509 | 18600 | 0.0859 |
| 0.3528 | 18700 | 0.0903 |
| 0.3547 | 18800 | 0.084 |
| 0.3566 | 18900 | 0.0923 |
| 0.3584 | 19000 | 0.0848 |
| 0.3603 | 19100 | 0.0812 |
| 0.3622 | 19200 | 0.0872 |
| 0.3641 | 19300 | 0.083 |
| 0.3660 | 19400 | 0.0826 |
| 0.3679 | 19500 | 0.101 |
| 0.3698 | 19600 | 0.0804 |
| 0.3717 | 19700 | 0.0676 |
| 0.3735 | 19800 | 0.0836 |
| 0.3754 | 19900 | 0.0711 |
| 0.3773 | 20000 | 0.0825 |
| 0.3792 | 20100 | 0.0835 |
| 0.3811 | 20200 | 0.0816 |
| 0.3830 | 20300 | 0.0812 |
| 0.3849 | 20400 | 0.0689 |
| 0.3867 | 20500 | 0.0627 |
| 0.3886 | 20600 | 0.0965 |
| 0.3905 | 20700 | 0.0632 |
| 0.3924 | 20800 | 0.0945 |
| 0.3943 | 20900 | 0.0923 |
| 0.3962 | 21000 | 0.0833 |
| 0.3981 | 21100 | 0.0537 |
| 0.4000 | 21200 | 0.0822 |
| 0.4018 | 21300 | 0.0684 |
| 0.4037 | 21400 | 0.0807 |
| 0.4056 | 21500 | 0.0945 |
| 0.4075 | 21600 | 0.0981 |
| 0.4094 | 21700 | 0.0748 |
| 0.4113 | 21800 | 0.0943 |
| 0.4132 | 21900 | 0.0709 |
| 0.4150 | 22000 | 0.0551 |
| 0.4169 | 22100 | 0.0679 |
| 0.4188 | 22200 | 0.0666 |
| 0.4207 | 22300 | 0.0976 |
| 0.4226 | 22400 | 0.0666 |
| 0.4245 | 22500 | 0.0651 |
| 0.4264 | 22600 | 0.0803 |
| 0.4283 | 22700 | 0.068 |
| 0.4301 | 22800 | 0.0541 |
| 0.4320 | 22900 | 0.0487 |
| 0.4339 | 23000 | 0.091 |
| 0.4358 | 23100 | 0.074 |
| 0.4377 | 23200 | 0.0733 |
| 0.4396 | 23300 | 0.0845 |
| 0.4415 | 23400 | 0.0823 |
| 0.4433 | 23500 | 0.0561 |
| 0.4452 | 23600 | 0.0508 |
| 0.4471 | 23700 | 0.074 |
| 0.4490 | 23800 | 0.0683 |
| 0.4509 | 23900 | 0.0797 |
| 0.4528 | 24000 | 0.0561 |
| 0.4547 | 24100 | 0.0744 |
| 0.4566 | 24200 | 0.0638 |
| 0.4584 | 24300 | 0.0633 |
| 0.4603 | 24400 | 0.062 |
| 0.4622 | 24500 | 0.0887 |
| 0.4641 | 24600 | 0.0908 |
| 0.4660 | 24700 | 0.0654 |
| 0.4679 | 24800 | 0.0522 |
| 0.4698 | 24900 | 0.0851 |
| 0.4716 | 25000 | 0.0763 |
| 0.4735 | 25100 | 0.0623 |
| 0.4754 | 25200 | 0.0712 |
| 0.4773 | 25300 | 0.0866 |
| 0.4792 | 25400 | 0.0812 |
| 0.4811 | 25500 | 0.0706 |
| 0.4830 | 25600 | 0.0734 |
| 0.4849 | 25700 | 0.068 |
| 0.4867 | 25800 | 0.111 |
| 0.4886 | 25900 | 0.0627 |
| 0.4905 | 26000 | 0.0459 |
| 0.4924 | 26100 | 0.0794 |
| 0.4943 | 26200 | 0.0547 |
| 0.4962 | 26300 | 0.0779 |
| 0.4981 | 26400 | 0.0609 |
| 0.4999 | 26500 | 0.0785 |
| 0.5018 | 26600 | 0.0722 |
| 0.5037 | 26700 | 0.0585 |
| 0.5056 | 26800 | 0.0572 |
| 0.5075 | 26900 | 0.0636 |
| 0.5094 | 27000 | 0.0642 |
| 0.5113 | 27100 | 0.0606 |
| 0.5131 | 27200 | 0.0725 |
| 0.5150 | 27300 | 0.0664 |
| 0.5169 | 27400 | 0.0933 |
| 0.5188 | 27500 | 0.0486 |
| 0.5207 | 27600 | 0.0514 |
| 0.5226 | 27700 | 0.0779 |
| 0.5245 | 27800 | 0.0614 |
| 0.5264 | 27900 | 0.0646 |
| 0.5282 | 28000 | 0.0606 |
| 0.5301 | 28100 | 0.0453 |
| 0.5320 | 28200 | 0.0749 |
| 0.5339 | 28300 | 0.0695 |
| 0.5358 | 28400 | 0.0897 |
| 0.5377 | 28500 | 0.0612 |
| 0.5396 | 28600 | 0.0542 |
| 0.5414 | 28700 | 0.0504 |
| 0.5433 | 28800 | 0.0539 |
| 0.5452 | 28900 | 0.0584 |
| 0.5471 | 29000 | 0.0552 |
| 0.5490 | 29100 | 0.076 |
| 0.5509 | 29200 | 0.0861 |
| 0.5528 | 29300 | 0.067 |
| 0.5547 | 29400 | 0.0887 |
| 0.5565 | 29500 | 0.059 |
| 0.5584 | 29600 | 0.0484 |
| 0.5603 | 29700 | 0.0703 |
| 0.5622 | 29800 | 0.0802 |
| 0.5641 | 29900 | 0.0805 |
| 0.5660 | 30000 | 0.0737 |
| 0.5679 | 30100 | 0.0518 |
| 0.5697 | 30200 | 0.0517 |
| 0.5716 | 30300 | 0.0806 |
| 0.5735 | 30400 | 0.0586 |
| 0.5754 | 30500 | 0.0491 |
| 0.5773 | 30600 | 0.0591 |
| 0.5792 | 30700 | 0.066 |
| 0.5811 | 30800 | 0.0419 |
| 0.5830 | 30900 | 0.0517 |
| 0.5848 | 31000 | 0.0539 |
| 0.5867 | 31100 | 0.0845 |
| 0.5886 | 31200 | 0.044 |
| 0.5905 | 31300 | 0.0597 |
| 0.5924 | 31400 | 0.0556 |
| 0.5943 | 31500 | 0.0724 |
| 0.5962 | 31600 | 0.0465 |
| 0.5980 | 31700 | 0.0585 |
| 0.5999 | 31800 | 0.0978 |
| 0.6018 | 31900 | 0.0657 |
| 0.6037 | 32000 | 0.0438 |
| 0.6056 | 32100 | 0.0429 |
| 0.6075 | 32200 | 0.0629 |
| 0.6094 | 32300 | 0.0591 |
| 0.6113 | 32400 | 0.0543 |
| 0.6131 | 32500 | 0.0502 |
| 0.6150 | 32600 | 0.0733 |
| 0.6169 | 32700 | 0.0426 |
| 0.6188 | 32800 | 0.0626 |
| 0.6207 | 32900 | 0.0406 |
| 0.6226 | 33000 | 0.0524 |
| 0.6245 | 33100 | 0.0619 |
| 0.6263 | 33200 | 0.0633 |
| 0.6282 | 33300 | 0.0582 |
| 0.6301 | 33400 | 0.0852 |
| 0.6320 | 33500 | 0.0482 |
| 0.6339 | 33600 | 0.0509 |
| 0.6358 | 33700 | 0.0626 |
| 0.6377 | 33800 | 0.0609 |
| 0.6396 | 33900 | 0.0508 |
| 0.6414 | 34000 | 0.0486 |
| 0.6433 | 34100 | 0.0508 |
| 0.6452 | 34200 | 0.0581 |
| 0.6471 | 34300 | 0.0409 |
| 0.6490 | 34400 | 0.0703 |
| 0.6509 | 34500 | 0.0606 |
| 0.6528 | 34600 | 0.0517 |
| 0.6546 | 34700 | 0.0493 |
| 0.6565 | 34800 | 0.0271 |
| 0.6584 | 34900 | 0.0337 |
| 0.6603 | 35000 | 0.0369 |
| 0.6622 | 35100 | 0.0474 |
| 0.6641 | 35200 | 0.0562 |
| 0.6660 | 35300 | 0.0663 |
| 0.6678 | 35400 | 0.0419 |
| 0.6697 | 35500 | 0.0766 |
| 0.6716 | 35600 | 0.0439 |
| 0.6735 | 35700 | 0.0538 |
| 0.6754 | 35800 | 0.0512 |
| 0.6773 | 35900 | 0.0388 |
| 0.6792 | 36000 | 0.0528 |
| 0.6811 | 36100 | 0.0489 |
| 0.6829 | 36200 | 0.0454 |
| 0.6848 | 36300 | 0.0449 |
| 0.6867 | 36400 | 0.055 |
| 0.6886 | 36500 | 0.0344 |
| 0.6905 | 36600 | 0.0485 |
| 0.6924 | 36700 | 0.0496 |
| 0.6943 | 36800 | 0.0705 |
| 0.6961 | 36900 | 0.0617 |
| 0.6980 | 37000 | 0.054 |
| 0.6999 | 37100 | 0.0613 |
| 0.7018 | 37200 | 0.0549 |
| 0.7037 | 37300 | 0.0378 |
| 0.7056 | 37400 | 0.0508 |
| 0.7075 | 37500 | 0.0613 |
| 0.7094 | 37600 | 0.0602 |
| 0.7112 | 37700 | 0.0592 |
| 0.7131 | 37800 | 0.0441 |
| 0.7150 | 37900 | 0.0445 |
| 0.7169 | 38000 | 0.0464 |
| 0.7188 | 38100 | 0.0537 |
| 0.7207 | 38200 | 0.0521 |
| 0.7226 | 38300 | 0.0447 |
| 0.7244 | 38400 | 0.044 |
| 0.7263 | 38500 | 0.0506 |
| 0.7282 | 38600 | 0.043 |
| 0.7301 | 38700 | 0.0441 |
| 0.7320 | 38800 | 0.0444 |
| 0.7339 | 38900 | 0.0416 |
| 0.7358 | 39000 | 0.0556 |
| 0.7377 | 39100 | 0.0829 |
| 0.7395 | 39200 | 0.043 |
| 0.7414 | 39300 | 0.0366 |
| 0.7433 | 39400 | 0.0457 |
| 0.7452 | 39500 | 0.0622 |
| 0.7471 | 39600 | 0.0353 |
| 0.7490 | 39700 | 0.0597 |
| 0.7509 | 39800 | 0.0468 |
| 0.7527 | 39900 | 0.0418 |
| 0.7546 | 40000 | 0.0606 |
| 0.7565 | 40100 | 0.0613 |
| 0.7584 | 40200 | 0.0654 |
| 0.7603 | 40300 | 0.046 |
| 0.7622 | 40400 | 0.034 |
| 0.7641 | 40500 | 0.0378 |
| 0.7660 | 40600 | 0.0461 |
| 0.7678 | 40700 | 0.0404 |
| 0.7697 | 40800 | 0.0583 |
| 0.7716 | 40900 | 0.0636 |
| 0.7735 | 41000 | 0.0537 |
| 0.7754 | 41100 | 0.0336 |
| 0.7773 | 41200 | 0.0315 |
| 0.7792 | 41300 | 0.0536 |
| 0.7810 | 41400 | 0.0532 |
| 0.7829 | 41500 | 0.0553 |
| 0.7848 | 41600 | 0.0458 |
| 0.7867 | 41700 | 0.0372 |
| 0.7886 | 41800 | 0.0346 |
| 0.7905 | 41900 | 0.0419 |
| 0.7924 | 42000 | 0.0461 |
| 0.7942 | 42100 | 0.0517 |
| 0.7961 | 42200 | 0.0574 |
| 0.7980 | 42300 | 0.0411 |
| 0.7999 | 42400 | 0.0389 |
| 0.8018 | 42500 | 0.0578 |
| 0.8037 | 42600 | 0.0637 |
| 0.8056 | 42700 | 0.0434 |
| 0.8075 | 42800 | 0.0776 |
| 0.8093 | 42900 | 0.0644 |
| 0.8112 | 43000 | 0.0537 |
| 0.8131 | 43100 | 0.0519 |
| 0.8150 | 43200 | 0.0241 |
| 0.8169 | 43300 | 0.0295 |
| 0.8188 | 43400 | 0.0618 |
| 0.8207 | 43500 | 0.0275 |
| 0.8225 | 43600 | 0.0605 |
| 0.8244 | 43700 | 0.0414 |
| 0.8263 | 43800 | 0.0446 |
| 0.8282 | 43900 | 0.0449 |
| 0.8301 | 44000 | 0.0558 |
| 0.8320 | 44100 | 0.0336 |
| 0.8339 | 44200 | 0.0555 |
| 0.8358 | 44300 | 0.0399 |
| 0.8376 | 44400 | 0.0319 |
| 0.8395 | 44500 | 0.0331 |
| 0.8414 | 44600 | 0.0415 |
| 0.8433 | 44700 | 0.0424 |
| 0.8452 | 44800 | 0.0287 |
| 0.8471 | 44900 | 0.044 |
| 0.8490 | 45000 | 0.0375 |
| 0.8508 | 45100 | 0.032 |
| 0.8527 | 45200 | 0.0406 |
| 0.8546 | 45300 | 0.0429 |
| 0.8565 | 45400 | 0.0727 |
| 0.8584 | 45500 | 0.05 |
| 0.8603 | 45600 | 0.0436 |
| 0.8622 | 45700 | 0.0401 |
| 0.8641 | 45800 | 0.0312 |
| 0.8659 | 45900 | 0.036 |
| 0.8678 | 46000 | 0.0558 |
| 0.8697 | 46100 | 0.0436 |
| 0.8716 | 46200 | 0.0517 |
| 0.8735 | 46300 | 0.0361 |
| 0.8754 | 46400 | 0.038 |
| 0.8773 | 46500 | 0.0418 |
| 0.8791 | 46600 | 0.0407 |
| 0.8810 | 46700 | 0.0336 |
| 0.8829 | 46800 | 0.0559 |
| 0.8848 | 46900 | 0.0488 |
| 0.8867 | 47000 | 0.0463 |
| 0.8886 | 47100 | 0.0504 |
| 0.8905 | 47200 | 0.0414 |
| 0.8924 | 47300 | 0.0428 |
| 0.8942 | 47400 | 0.0389 |
| 0.8961 | 47500 | 0.0422 |
| 0.8980 | 47600 | 0.0533 |
| 0.8999 | 47700 | 0.0386 |
| 0.9018 | 47800 | 0.0672 |
| 0.9037 | 47900 | 0.0505 |
| 0.9056 | 48000 | 0.0632 |
| 0.9074 | 48100 | 0.0263 |
| 0.9093 | 48200 | 0.0448 |
| 0.9112 | 48300 | 0.0413 |
| 0.9131 | 48400 | 0.0532 |
| 0.9150 | 48500 | 0.0503 |
| 0.9169 | 48600 | 0.0472 |
| 0.9188 | 48700 | 0.0255 |
| 0.9207 | 48800 | 0.035 |
| 0.9225 | 48900 | 0.0353 |
| 0.9244 | 49000 | 0.0407 |
| 0.9263 | 49100 | 0.0154 |
| 0.9282 | 49200 | 0.0535 |
| 0.9301 | 49300 | 0.0435 |
| 0.9320 | 49400 | 0.0461 |
| 0.9339 | 49500 | 0.0288 |
| 0.9357 | 49600 | 0.0366 |
| 0.9376 | 49700 | 0.0411 |
| 0.9395 | 49800 | 0.0605 |
| 0.9414 | 49900 | 0.0551 |
| 0.9433 | 50000 | 0.0297 |
| 0.9452 | 50100 | 0.0388 |
| 0.9471 | 50200 | 0.0402 |
| 0.9489 | 50300 | 0.0321 |
| 0.9508 | 50400 | 0.0538 |
| 0.9527 | 50500 | 0.036 |
| 0.9546 | 50600 | 0.0318 |
| 0.9565 | 50700 | 0.0398 |
| 0.9584 | 50800 | 0.0405 |
| 0.9603 | 50900 | 0.0408 |
| 0.9622 | 51000 | 0.0485 |
| 0.9640 | 51100 | 0.047 |
| 0.9659 | 51200 | 0.0452 |
| 0.9678 | 51300 | 0.0469 |
| 0.9697 | 51400 | 0.0473 |
| 0.9716 | 51500 | 0.039 |
| 0.9735 | 51600 | 0.0579 |
| 0.9754 | 51700 | 0.0332 |
| 0.9772 | 51800 | 0.0322 |
| 0.9791 | 51900 | 0.0324 |
| 0.9810 | 52000 | 0.035 |
| 0.9829 | 52100 | 0.0517 |
| 0.9848 | 52200 | 0.0275 |
| 0.9867 | 52300 | 0.0466 |
| 0.9886 | 52400 | 0.0452 |
| 0.9905 | 52500 | 0.0446 |
| 0.9923 | 52600 | 0.0357 |
| 0.9942 | 52700 | 0.0368 |
| 0.9961 | 52800 | 0.0365 |
| 0.9980 | 52900 | 0.0303 |
| 0.9999 | 53000 | 0.0288 |
</details>
### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 3.4.1
- Transformers: 4.51.1
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.2
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## 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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