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---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated
base_model: microsoft/mpnet-base
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: 'Really? No kidding! '
sentences:
- yeah really no kidding
- At the end of the fourth century was when baked goods flourished.
- The campaigns seem to reach a new pool of contributors.
- source_sentence: A sleeping man.
sentences:
- Two men are sleeping.
- Someone is selling oranges
- the family is young
- source_sentence: a guy on a bike
sentences:
- A tall person on a bike
- A man is on a frozen lake.
- The women throw food at the kids
- source_sentence: yeah really no kidding
sentences:
- oh uh-huh well no they wouldn't would they no
- yeah i mean just when uh the they military paid for her education
- The campaigns seem to reach a new pool of contributors.
- source_sentence: He ran like an athlete.
sentences:
- ' Then he ran.'
- yeah i mean just when uh the they military paid for her education
- Similarly, OIM revised the electronic Grant Renewal Application to accommodate
new information sought by LSC and to ensure greater ease for users.
pipeline_tag: sentence-similarity
co2_eq_emissions:
emissions: 17.515467907816664
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.13
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: SentenceTransformer based on microsoft/mpnet-base
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.7331234146933103
name: Pearson Cosine
- type: spearman_cosine
value: 0.7435439430716654
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7389474504545281
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7473580293303098
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7356264396007131
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7436137284782617
name: Spearman Euclidean
- type: pearson_dot
value: 0.7093073700072118
name: Pearson Dot
- type: spearman_dot
value: 0.7150453113301433
name: Spearman Dot
- type: pearson_max
value: 0.7389474504545281
name: Pearson Max
- type: spearman_max
value: 0.7473580293303098
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.6750510843835755
name: Pearson Cosine
- type: spearman_cosine
value: 0.6615639695746663
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6718085205234632
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6589482932175834
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6693170762111229
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6578210069410166
name: Spearman Euclidean
- type: pearson_dot
value: 0.6490291380804283
name: Pearson Dot
- type: spearman_dot
value: 0.6335192601696299
name: Spearman Dot
- type: pearson_max
value: 0.6750510843835755
name: Pearson Max
- type: spearman_max
value: 0.6615639695746663
name: Spearman Max
---
# SentenceTransformer based on microsoft/mpnet-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co./microsoft/mpnet-base) on the [multi_nli](https://huggingface.co./datasets/nyu-mll/multi_nli), [snli](https://huggingface.co./datasets/stanfordnlp/snli) and [stsb](https://huggingface.co./datasets/mteb/stsbenchmark-sts) datasets. 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:** [microsoft/mpnet-base](https://huggingface.co./microsoft/mpnet-base)
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 tokens
- **Training Datasets:**
- [multi_nli](https://huggingface.co./datasets/nyu-mll/multi_nli)
- [snli](https://huggingface.co./datasets/stanfordnlp/snli)
- [stsb](https://huggingface.co./datasets/mteb/stsbenchmark-sts)
- **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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(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("sentence_transformers_model_id")
# Run inference
sentences = [
"He ran like an athlete.",
" Then he ran.",
"yeah i mean just when uh the they military paid for her education",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
```
<!--
### 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-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7331 |
| **spearman_cosine** | **0.7435** |
| pearson_manhattan | 0.7389 |
| spearman_manhattan | 0.7474 |
| pearson_euclidean | 0.7356 |
| spearman_euclidean | 0.7436 |
| pearson_dot | 0.7093 |
| spearman_dot | 0.715 |
| pearson_max | 0.7389 |
| spearman_max | 0.7474 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6751 |
| **spearman_cosine** | **0.6616** |
| pearson_manhattan | 0.6718 |
| spearman_manhattan | 0.6589 |
| pearson_euclidean | 0.6693 |
| spearman_euclidean | 0.6578 |
| pearson_dot | 0.649 |
| spearman_dot | 0.6335 |
| pearson_max | 0.6751 |
| spearman_max | 0.6616 |
<!--
## 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 Datasets
#### multi_nli
* Dataset: [multi_nli](https://huggingface.co./datasets/nyu-mll/multi_nli) at [da70db2](https://huggingface.co./datasets/nyu-mll/multi_nli/tree/da70db2af9d09693783c3320c4249840212ee221)
* Size: 10,000 training samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | premise | hypothesis | label |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 4 tokens</li><li>mean: 26.95 tokens</li><li>max: 189 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.11 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>0: ~34.30%</li><li>1: ~28.20%</li><li>2: ~37.50%</li></ul> |
* Samples:
| premise | hypothesis | label |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------|
| <code>Conceptually cream skimming has two basic dimensions - product and geography.</code> | <code>Product and geography are what make cream skimming work. </code> | <code>1</code> |
| <code>you know during the season and i guess at at your level uh you lose them to the next level if if they decide to recall the the parent team the Braves decide to call to recall a guy from triple A then a double A guy goes up to replace him and a single A guy goes up to replace him</code> | <code>You lose the things to the following level if the people recall.</code> | <code>0</code> |
| <code>One of our number will carry out your instructions minutely.</code> | <code>A member of my team will execute your orders with immense precision.</code> | <code>0</code> |
* Loss: [<code>sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss</code>](https://sbert.net/docs/package_reference/losses.html#softmaxloss)
#### snli
* Dataset: [snli](https://huggingface.co./datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co./datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
* Size: 10,000 training samples
* Columns: <code>snli_premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | snli_premise | hypothesis | label |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 17.38 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.7 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>0: ~33.40%</li><li>1: ~33.30%</li><li>2: ~33.30%</li></ul> |
* Samples:
| snli_premise | hypothesis | label |
|:--------------------------------------------------------------------|:---------------------------------------------------------------|:---------------|
| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is training his horse for a competition.</code> | <code>1</code> |
| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is at a diner, ordering an omelette.</code> | <code>2</code> |
| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>0</code> |
* Loss: [<code>sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss</code>](https://sbert.net/docs/package_reference/losses.html#softmaxloss)
#### stsb
* Dataset: [stsb](https://huggingface.co./datasets/mteb/stsbenchmark-sts) at [8913289](https://huggingface.co./datasets/mteb/stsbenchmark-sts/tree/8913289635987208e6e7c72789e4be2fe94b6abd)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 10.0 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.95 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------|
| <code>A plane is taking off.</code> | <code>An air plane is taking off.</code> | <code>1.0</code> |
| <code>A man is playing a large flute.</code> | <code>A man is playing a flute.</code> | <code>0.76</code> |
| <code>A man is spreading shreded cheese on a pizza.</code> | <code>A man is spreading shredded cheese on an uncooked pizza.</code> | <code>0.76</code> |
* Loss: [<code>sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Evaluation Datasets
#### multi_nli
* Dataset: [multi_nli](https://huggingface.co./datasets/nyu-mll/multi_nli) at [da70db2](https://huggingface.co./datasets/nyu-mll/multi_nli/tree/da70db2af9d09693783c3320c4249840212ee221)
* Size: 100 evaluation samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | premise | hypothesis | label |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 5 tokens</li><li>mean: 27.67 tokens</li><li>max: 138 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.48 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>0: ~35.00%</li><li>1: ~31.00%</li><li>2: ~34.00%</li></ul> |
* Samples:
| premise | hypothesis | label |
|:---------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:---------------|
| <code>The new rights are nice enough</code> | <code>Everyone really likes the newest benefits </code> | <code>1</code> |
| <code>This site includes a list of all award winners and a searchable database of Government Executive articles.</code> | <code>The Government Executive articles housed on the website are not able to be searched.</code> | <code>2</code> |
| <code>uh i don't know i i have mixed emotions about him uh sometimes i like him but at the same times i love to see somebody beat him</code> | <code>I like him for the most part, but would still enjoy seeing someone beat him.</code> | <code>0</code> |
* Loss: [<code>sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss</code>](https://sbert.net/docs/package_reference/losses.html#softmaxloss)
#### snli
* Dataset: [snli](https://huggingface.co./datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co./datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
* Size: 9,842 evaluation samples
* Columns: <code>snli_premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | snli_premise | hypothesis | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 18.44 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.57 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>0: ~33.10%</li><li>1: ~33.30%</li><li>2: ~33.60%</li></ul> |
* Samples:
| snli_premise | hypothesis | label |
|:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------|
| <code>Two women are embracing while holding to go packages.</code> | <code>The sisters are hugging goodbye while holding to go packages after just eating lunch.</code> | <code>1</code> |
| <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>0</code> |
| <code>Two women are embracing while holding to go packages.</code> | <code>The men are fighting outside a deli.</code> | <code>2</code> |
* Loss: [<code>sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss</code>](https://sbert.net/docs/package_reference/losses.html#softmaxloss)
#### stsb
* Dataset: [stsb](https://huggingface.co./datasets/mteb/stsbenchmark-sts) at [8913289](https://huggingface.co./datasets/mteb/stsbenchmark-sts/tree/8913289635987208e6e7c72789e4be2fe94b6abd)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 15.1 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.11 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:--------------------------------------------------|:------------------------------------------------------|:------------------|
| <code>A man with a hard hat is dancing.</code> | <code>A man wearing a hard hat is dancing.</code> | <code>1.0</code> |
| <code>A young child is riding a horse.</code> | <code>A child is riding a horse.</code> | <code>0.95</code> |
| <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code> |
* Loss: [<code>sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- per_device_train_batch_size: 128
- per_device_eval_batch_size: 128
- learning_rate: 2e-05
- num_train_epochs: 1
- warmup_ratio: 0.1
- seed: 33
- bf16: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- overwrite_output_dir: False
- do_predict: False
- prediction_loss_only: False
- per_device_train_batch_size: 128
- per_device_eval_batch_size: 128
- per_gpu_train_batch_size: None
- per_gpu_eval_batch_size: None
- gradient_accumulation_steps: 1
- eval_accumulation_steps: None
- learning_rate: 2e-05
- weight_decay: 0.0
- adam_beta1: 0.9
- adam_beta2: 0.999
- adam_epsilon: 1e-08
- max_grad_norm: 1.0
- num_train_epochs: 1
- max_steps: -1
- lr_scheduler_type: linear
- lr_scheduler_kwargs: {}
- warmup_ratio: 0.1
- warmup_steps: 0
- log_level: passive
- log_level_replica: warning
- log_on_each_node: True
- logging_nan_inf_filter: True
- save_safetensors: True
- save_on_each_node: False
- save_only_model: False
- no_cuda: False
- use_cpu: False
- use_mps_device: False
- seed: 33
- data_seed: None
- jit_mode_eval: False
- use_ipex: False
- bf16: True
- fp16: False
- fp16_opt_level: O1
- half_precision_backend: auto
- bf16_full_eval: False
- fp16_full_eval: False
- tf32: None
- local_rank: 0
- ddp_backend: None
- tpu_num_cores: None
- tpu_metrics_debug: False
- debug: []
- dataloader_drop_last: False
- dataloader_num_workers: 0
- dataloader_prefetch_factor: None
- past_index: -1
- disable_tqdm: False
- remove_unused_columns: True
- label_names: None
- load_best_model_at_end: 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}
- 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: None
- 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
- 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
- round_robin_sampler: False
</details>
### Training Logs
| Epoch | Step | Training Loss | multi nli loss | snli loss | stsb loss | sts-dev spearman cosine |
|:------:|:----:|:-------------:|:--------------:|:---------:|:---------:|:-----------------------:|
| 0.0493 | 10 | 0.9199 | 1.1019 | 1.1017 | 0.3016 | 0.6324 |
| 0.0985 | 20 | 1.0063 | 1.1000 | 1.0966 | 0.2635 | 0.6093 |
| 0.1478 | 30 | 1.002 | 1.0995 | 1.0908 | 0.1766 | 0.5328 |
| 0.1970 | 40 | 0.7946 | 1.0980 | 1.0913 | 0.0923 | 0.5991 |
| 0.2463 | 50 | 0.9891 | 1.0967 | 1.0781 | 0.0912 | 0.6457 |
| 0.2956 | 60 | 0.784 | 1.0938 | 1.0699 | 0.0934 | 0.6629 |
| 0.3448 | 70 | 0.6735 | 1.0940 | 1.0728 | 0.0640 | 0.7538 |
| 0.3941 | 80 | 0.7713 | 1.0893 | 1.0676 | 0.0612 | 0.7653 |
| 0.4433 | 90 | 0.9772 | 1.0870 | 1.0573 | 0.0636 | 0.7621 |
| 0.4926 | 100 | 0.8613 | 1.0862 | 1.0515 | 0.0632 | 0.7583 |
| 0.5419 | 110 | 0.7528 | 1.0814 | 1.0397 | 0.0617 | 0.7536 |
| 0.5911 | 120 | 0.6541 | 1.0854 | 1.0329 | 0.0657 | 0.7512 |
| 0.6404 | 130 | 1.051 | 1.0658 | 1.0211 | 0.0607 | 0.7340 |
| 0.6897 | 140 | 0.8516 | 1.0631 | 1.0171 | 0.0587 | 0.7467 |
| 0.7389 | 150 | 0.7484 | 1.0563 | 1.0122 | 0.0556 | 0.7537 |
| 0.7882 | 160 | 0.7368 | 1.0534 | 1.0100 | 0.0588 | 0.7526 |
| 0.8374 | 170 | 0.8373 | 1.0498 | 1.0030 | 0.0565 | 0.7491 |
| 0.8867 | 180 | 0.9311 | 1.0387 | 0.9981 | 0.0588 | 0.7302 |
| 0.9360 | 190 | 0.5445 | 1.0357 | 0.9967 | 0.0565 | 0.7382 |
| 0.9852 | 200 | 0.9154 | 1.0359 | 0.9964 | 0.0556 | 0.7435 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.018 kg of CO2
- **Hours Used**: 0.13 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 2.7.0.dev0
- Transformers: 4.39.3
- PyTorch: 2.1.0+cu121
- Accelerate: 0.26.1
- Datasets: 2.18.0
- Tokenizers: 0.15.2
## Citation
### BibTeX
#### Sentence Transformers and SoftmaxLoss
```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",
}
```
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