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DeBERTa2-0.9B-ST-stsb

Sentence Similarity
sentence-transformers
PyTorch
English
deberta-v2
feature-extraction
Generated from Trainer
dataset_size:5749
loss:AnglELoss
Eval Results
Inference Endpoints
Model card Files Community
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---
base_model: BXresearch/DeBERTa2-0.9B-ST-v2
datasets:
- sentence-transformers/stsb
language:
- en
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:5749
- loss:AnglELoss
widget:
- source_sentence: Left side of a silver train engine.
sentences:
- A close-up of a black train engine.
- Two boys are in midair jumping into an inground pool.
- An older Asian couple poses with a newborn baby at the dinner table.
- source_sentence: Four girls in swimsuits are playing volleyball at the beach.
sentences:
- A little girl is walking down a hallway.
- The man is erasing the chalk board.
- Four women in bikinis are playing volleyball on the beach.
- source_sentence: A woman is cooking meat.
sentences:
- The dogs are alone in the forest.
- A man is speaking.
- A dog jumps through a hoop.
- source_sentence: A person is folding a square paper piece.
sentences:
- A woman is carrying her baby.
- A person folds a piece of paper.
- A dog is trying to get through his dog door.
- source_sentence: The boy is playing the piano.
sentences:
- The woman is pouring oil into the pan.
- A small black and white dog is swimming in water.
- Two brown dogs are playing with each other in the snow.
model-index:
- name: SentenceTransformer based on BXresearch/DeBERTa2-0.9B-ST-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.9174070307741418
name: Pearson Cosine
- type: spearman_cosine
value: 0.9292509717696739
name: Spearman Cosine
- type: pearson_manhattan
value: 0.9282688885676256
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.9298350652202988
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9286763713344532
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.9301882421673056
name: Spearman Euclidean
- type: pearson_dot
value: 0.9015673628485675
name: Pearson Dot
- type: spearman_dot
value: 0.9062672614479156
name: Spearman Dot
- type: pearson_max
value: 0.9286763713344532
name: Pearson Max
- type: spearman_max
value: 0.9301882421673056
name: Spearman Max
- task:
type: binary-classification
name: Binary Classification
dataset:
name: allNLI dev
type: allNLI-dev
metrics:
- type: cosine_accuracy
value: 0.75390625
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.7934484481811523
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.6263736263736264
name: Cosine F1
- type: cosine_f1_threshold
value: 0.7287859916687012
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.5643564356435643
name: Cosine Precision
- type: cosine_recall
value: 0.7037037037037037
name: Cosine Recall
- type: cosine_ap
value: 0.5952488621962656
name: Cosine Ap
- type: dot_accuracy
value: 0.74609375
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 853.7699584960938
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.6106194690265486
name: Dot F1
- type: dot_f1_threshold
value: 685.536865234375
name: Dot F1 Threshold
- type: dot_precision
value: 0.47586206896551725
name: Dot Precision
- type: dot_recall
value: 0.8518518518518519
name: Dot Recall
- type: dot_ap
value: 0.5773093883122924
name: Dot Ap
- type: manhattan_accuracy
value: 0.75390625
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 654.8433227539062
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.6244343891402715
name: Manhattan F1
- type: manhattan_f1_threshold
value: 811.658203125
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.4928571428571429
name: Manhattan Precision
- type: manhattan_recall
value: 0.8518518518518519
name: Manhattan Recall
- type: manhattan_ap
value: 0.596555546112473
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.75390625
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 21.04879379272461
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.6244343891402715
name: Euclidean F1
- type: euclidean_f1_threshold
value: 26.11341094970703
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.4928571428571429
name: Euclidean Precision
- type: euclidean_recall
value: 0.8518518518518519
name: Euclidean Recall
- type: euclidean_ap
value: 0.595001077180561
name: Euclidean Ap
- type: max_accuracy
value: 0.75390625
name: Max Accuracy
- type: max_accuracy_threshold
value: 853.7699584960938
name: Max Accuracy Threshold
- type: max_f1
value: 0.6263736263736264
name: Max F1
- type: max_f1_threshold
value: 811.658203125
name: Max F1 Threshold
- type: max_precision
value: 0.5643564356435643
name: Max Precision
- type: max_recall
value: 0.8518518518518519
name: Max Recall
- type: max_ap
value: 0.596555546112473
name: Max Ap
- task:
type: binary-classification
name: Binary Classification
dataset:
name: Qnli dev
type: Qnli-dev
metrics:
- type: cosine_accuracy
value: 0.71484375
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.7152643799781799
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.7224334600760456
name: Cosine F1
- type: cosine_f1_threshold
value: 0.6804982423782349
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.6785714285714286
name: Cosine Precision
- type: cosine_recall
value: 0.7723577235772358
name: Cosine Recall
- type: cosine_ap
value: 0.7550328500735501
name: Cosine Ap
- type: dot_accuracy
value: 0.69140625
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 720.3964233398438
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.7058823529411764
name: Dot F1
- type: dot_f1_threshold
value: 706.561279296875
name: Dot F1 Threshold
- type: dot_precision
value: 0.6442953020134228
name: Dot Precision
- type: dot_recall
value: 0.7804878048780488
name: Dot Recall
- type: dot_ap
value: 0.7012253433472802
name: Dot Ap
- type: manhattan_accuracy
value: 0.72265625
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 760.7179565429688
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.7279693486590038
name: Manhattan F1
- type: manhattan_f1_threshold
value: 807.8878173828125
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.6884057971014492
name: Manhattan Precision
- type: manhattan_recall
value: 0.7723577235772358
name: Manhattan Recall
- type: manhattan_ap
value: 0.7705323139232185
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.7265625
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 25.634429931640625
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.7244094488188976
name: Euclidean F1
- type: euclidean_f1_threshold
value: 25.634429931640625
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.7022900763358778
name: Euclidean Precision
- type: euclidean_recall
value: 0.7479674796747967
name: Euclidean Recall
- type: euclidean_ap
value: 0.7674294690555423
name: Euclidean Ap
- type: max_accuracy
value: 0.7265625
name: Max Accuracy
- type: max_accuracy_threshold
value: 760.7179565429688
name: Max Accuracy Threshold
- type: max_f1
value: 0.7279693486590038
name: Max F1
- type: max_f1_threshold
value: 807.8878173828125
name: Max F1 Threshold
- type: max_precision
value: 0.7022900763358778
name: Max Precision
- type: max_recall
value: 0.7804878048780488
name: Max Recall
- type: max_ap
value: 0.7705323139232185
name: Max Ap
---
# SentenceTransformer based on BXresearch/DeBERTa2-0.9B-ST-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BXresearch/DeBERTa2-0.9B-ST-v2](https://huggingface.co/BXresearch/DeBERTa2-0.9B-ST-v2) on the [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) dataset. It maps sentences & paragraphs to a 1536-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:** [BXresearch/DeBERTa2-0.9B-ST-v2](https://huggingface.co/BXresearch/DeBERTa2-0.9B-ST-v2) <!-- at revision cafb66e82995a2b6620420a30de218463a1cdafd -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1536 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb)
- **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': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model
(1): Pooling({'word_embedding_dimension': 1536, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("bobox/DeBERTa2-0.9B-ST-stsb")
# Run inference
sentences = [
'The boy is playing the piano.',
'The woman is pouring oil into the pan.',
'A small black and white dog is swimming in water.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1536]
# 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.*
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.9174 |
| **spearman_cosine** | **0.9293** |
| pearson_manhattan | 0.9283 |
| spearman_manhattan | 0.9298 |
| pearson_euclidean | 0.9287 |
| spearman_euclidean | 0.9302 |
| pearson_dot | 0.9016 |
| spearman_dot | 0.9063 |
| pearson_max | 0.9287 |
| spearman_max | 0.9302 |
#### Binary Classification
* Dataset: `allNLI-dev`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.7539 |
| cosine_accuracy_threshold | 0.7934 |
| cosine_f1 | 0.6264 |
| cosine_f1_threshold | 0.7288 |
| cosine_precision | 0.5644 |
| cosine_recall | 0.7037 |
| cosine_ap | 0.5952 |
| dot_accuracy | 0.7461 |
| dot_accuracy_threshold | 853.77 |
| dot_f1 | 0.6106 |
| dot_f1_threshold | 685.5369 |
| dot_precision | 0.4759 |
| dot_recall | 0.8519 |
| dot_ap | 0.5773 |
| manhattan_accuracy | 0.7539 |
| manhattan_accuracy_threshold | 654.8433 |
| manhattan_f1 | 0.6244 |
| manhattan_f1_threshold | 811.6582 |
| manhattan_precision | 0.4929 |
| manhattan_recall | 0.8519 |
| manhattan_ap | 0.5966 |
| euclidean_accuracy | 0.7539 |
| euclidean_accuracy_threshold | 21.0488 |
| euclidean_f1 | 0.6244 |
| euclidean_f1_threshold | 26.1134 |
| euclidean_precision | 0.4929 |
| euclidean_recall | 0.8519 |
| euclidean_ap | 0.595 |
| max_accuracy | 0.7539 |
| max_accuracy_threshold | 853.77 |
| max_f1 | 0.6264 |
| max_f1_threshold | 811.6582 |
| max_precision | 0.5644 |
| max_recall | 0.8519 |
| **max_ap** | **0.5966** |
#### Binary Classification
* Dataset: `Qnli-dev`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.7148 |
| cosine_accuracy_threshold | 0.7153 |
| cosine_f1 | 0.7224 |
| cosine_f1_threshold | 0.6805 |
| cosine_precision | 0.6786 |
| cosine_recall | 0.7724 |
| cosine_ap | 0.755 |
| dot_accuracy | 0.6914 |
| dot_accuracy_threshold | 720.3964 |
| dot_f1 | 0.7059 |
| dot_f1_threshold | 706.5613 |
| dot_precision | 0.6443 |
| dot_recall | 0.7805 |
| dot_ap | 0.7012 |
| manhattan_accuracy | 0.7227 |
| manhattan_accuracy_threshold | 760.718 |
| manhattan_f1 | 0.728 |
| manhattan_f1_threshold | 807.8878 |
| manhattan_precision | 0.6884 |
| manhattan_recall | 0.7724 |
| manhattan_ap | 0.7705 |
| euclidean_accuracy | 0.7266 |
| euclidean_accuracy_threshold | 25.6344 |
| euclidean_f1 | 0.7244 |
| euclidean_f1_threshold | 25.6344 |
| euclidean_precision | 0.7023 |
| euclidean_recall | 0.748 |
| euclidean_ap | 0.7674 |
| max_accuracy | 0.7266 |
| max_accuracy_threshold | 760.718 |
| max_f1 | 0.728 |
| max_f1_threshold | 807.8878 |
| max_precision | 0.7023 |
| max_recall | 0.7805 |
| **max_ap** | **0.7705** |
<!--
## 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
#### sentence-transformers/stsb
* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 9.81 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.74 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 | score |
|:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------|
| <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>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_angle_sim"
}
```
### Evaluation Dataset
#### sentence-transformers/stsb
* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
* Size: 512 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 11.16 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.17 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------|:------------------------------------------------------|:------------------|
| <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>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_angle_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_eval_batch_size`: 256
- `gradient_accumulation_steps`: 2
- `learning_rate`: 1.5e-05
- `weight_decay`: 5e-05
- `num_train_epochs`: 2
- `lr_scheduler_type`: cosine_with_min_lr
- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 2e-06}
- `warmup_ratio`: 0.2
- `save_safetensors`: False
- `fp16`: True
- `push_to_hub`: True
- `hub_model_id`: bobox/DeBERTa2-0.9B-ST-stsb-checkpoints-tmp
- `hub_strategy`: all_checkpoints
- `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`: 8
- `per_device_eval_batch_size`: 256
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `learning_rate`: 1.5e-05
- `weight_decay`: 5e-05
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: cosine_with_min_lr
- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 2e-06}
- `warmup_ratio`: 0.2
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: False
- `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`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: bobox/DeBERTa2-0.9B-ST-stsb-checkpoints-tmp
- `hub_strategy`: all_checkpoints
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | loss | Qnli-dev_max_ap | allNLI-dev_max_ap | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:------:|:---------------:|:-----------------:|:------------------------:|
| 0.0056 | 2 | 2.6549 | - | - | - | - |
| 0.0111 | 4 | 2.7355 | - | - | - | - |
| 0.0167 | 6 | 3.6211 | - | - | - | - |
| 0.0223 | 8 | 3.0686 | - | - | - | - |
| 0.0278 | 10 | 3.4113 | - | - | - | - |
| 0.0334 | 12 | 2.4857 | - | - | - | - |
| 0.0389 | 14 | 2.4288 | - | - | - | - |
| 0.0445 | 16 | 2.6203 | - | - | - | - |
| 0.0501 | 18 | 2.7441 | - | - | - | - |
| 0.0556 | 20 | 3.4263 | - | - | - | - |
| 0.0612 | 22 | 2.3565 | - | - | - | - |
| 0.0668 | 24 | 2.5596 | - | - | - | - |
| 0.0723 | 26 | 3.0866 | - | - | - | - |
| 0.0779 | 28 | 3.223 | - | - | - | - |
| 0.0834 | 30 | 2.012 | - | - | - | - |
| 0.0890 | 32 | 3.2829 | - | - | - | - |
| 0.0946 | 34 | 3.9277 | - | - | - | - |
| 0.1001 | 36 | 2.785 | 2.6652 | 0.7960 | 0.6275 | 0.9294 |
| 0.1057 | 38 | 3.4966 | - | - | - | - |
| 0.1113 | 40 | 2.5923 | - | - | - | - |
| 0.1168 | 42 | 3.4418 | - | - | - | - |
| 0.1224 | 44 | 2.6519 | - | - | - | - |
| 0.1280 | 46 | 3.7746 | - | - | - | - |
| 0.1335 | 48 | 2.6736 | - | - | - | - |
| 0.1391 | 50 | 3.6764 | - | - | - | - |
| 0.1446 | 52 | 3.5311 | - | - | - | - |
| 0.1502 | 54 | 2.5869 | - | - | - | - |
| 0.1558 | 56 | 3.183 | - | - | - | - |
| 0.1613 | 58 | 2.747 | - | - | - | - |
| 0.1669 | 60 | 1.965 | - | - | - | - |
| 0.1725 | 62 | 2.1785 | - | - | - | - |
| 0.1780 | 64 | 2.5788 | - | - | - | - |
| 0.1836 | 66 | 3.1776 | - | - | - | - |
| 0.1892 | 68 | 2.6464 | - | - | - | - |
| 0.1947 | 70 | 2.7619 | - | - | - | - |
| 0.2003 | 72 | 3.0911 | 2.6171 | 0.7923 | 0.6295 | 0.9276 |
| 0.2058 | 74 | 2.4308 | - | - | - | - |
| 0.2114 | 76 | 3.2068 | - | - | - | - |
| 0.2170 | 78 | 2.4081 | - | - | - | - |
| 0.2225 | 80 | 2.3257 | - | - | - | - |
| 0.2281 | 82 | 3.0499 | - | - | - | - |
| 0.2337 | 84 | 3.2518 | - | - | - | - |
| 0.2392 | 86 | 2.7876 | - | - | - | - |
| 0.2448 | 88 | 2.7898 | - | - | - | - |
| 0.2503 | 90 | 2.7116 | - | - | - | - |
| 0.2559 | 92 | 3.0505 | - | - | - | - |
| 0.2615 | 94 | 2.5901 | - | - | - | - |
| 0.2670 | 96 | 1.9563 | - | - | - | - |
| 0.2726 | 98 | 2.1006 | - | - | - | - |
| 0.2782 | 100 | 2.1853 | - | - | - | - |
| 0.2837 | 102 | 2.327 | - | - | - | - |
| 0.2893 | 104 | 1.9937 | - | - | - | - |
| 0.2949 | 106 | 2.543 | - | - | - | - |
| 0.3004 | 108 | 1.9826 | 2.4596 | 0.7919 | 0.6329 | 0.9341 |
| 0.3060 | 110 | 3.0746 | - | - | - | - |
| 0.3115 | 112 | 2.4145 | - | - | - | - |
| 0.3171 | 114 | 2.244 | - | - | - | - |
| 0.3227 | 116 | 2.78 | - | - | - | - |
| 0.3282 | 118 | 2.8323 | - | - | - | - |
| 0.3338 | 120 | 2.4639 | - | - | - | - |
| 0.3394 | 122 | 2.9216 | - | - | - | - |
| 0.3449 | 124 | 2.0747 | - | - | - | - |
| 0.3505 | 126 | 2.7573 | - | - | - | - |
| 0.3561 | 128 | 3.7019 | - | - | - | - |
| 0.3616 | 130 | 3.3155 | - | - | - | - |
| 0.3672 | 132 | 3.625 | - | - | - | - |
| 0.3727 | 134 | 3.2889 | - | - | - | - |
| 0.3783 | 136 | 3.5936 | - | - | - | - |
| 0.3839 | 138 | 3.5932 | - | - | - | - |
| 0.3894 | 140 | 3.0457 | - | - | - | - |
| 0.3950 | 142 | 3.093 | - | - | - | - |
| 0.4006 | 144 | 2.7189 | 2.4698 | 0.7752 | 0.5896 | 0.9346 |
| 0.4061 | 146 | 3.2578 | - | - | - | - |
| 0.4117 | 148 | 3.3581 | - | - | - | - |
| 0.4172 | 150 | 2.9734 | - | - | - | - |
| 0.4228 | 152 | 3.0514 | - | - | - | - |
| 0.4284 | 154 | 3.1983 | - | - | - | - |
| 0.4339 | 156 | 2.9033 | - | - | - | - |
| 0.4395 | 158 | 2.9279 | - | - | - | - |
| 0.4451 | 160 | 3.1336 | - | - | - | - |
| 0.4506 | 162 | 3.1467 | - | - | - | - |
| 0.4562 | 164 | 3.0381 | - | - | - | - |
| 0.4618 | 166 | 3.068 | - | - | - | - |
| 0.4673 | 168 | 3.0261 | - | - | - | - |
| 0.4729 | 170 | 3.2867 | - | - | - | - |
| 0.4784 | 172 | 2.8474 | - | - | - | - |
| 0.4840 | 174 | 2.7982 | - | - | - | - |
| 0.4896 | 176 | 2.7945 | - | - | - | - |
| 0.4951 | 178 | 3.1312 | - | - | - | - |
| 0.5007 | 180 | 2.9704 | 2.4640 | 0.7524 | 0.6033 | 0.9242 |
| 0.5063 | 182 | 2.9856 | - | - | - | - |
| 0.5118 | 184 | 3.014 | - | - | - | - |
| 0.5174 | 186 | 3.0125 | - | - | - | - |
| 0.5229 | 188 | 2.8149 | - | - | - | - |
| 0.5285 | 190 | 2.7954 | - | - | - | - |
| 0.5341 | 192 | 3.078 | - | - | - | - |
| 0.5396 | 194 | 2.955 | - | - | - | - |
| 0.5452 | 196 | 2.9468 | - | - | - | - |
| 0.5508 | 198 | 3.0791 | - | - | - | - |
| 0.5563 | 200 | 2.998 | - | - | - | - |
| 0.5619 | 202 | 2.9068 | - | - | - | - |
| 0.5675 | 204 | 2.8283 | - | - | - | - |
| 0.5730 | 206 | 2.9216 | - | - | - | - |
| 0.5786 | 208 | 3.3441 | - | - | - | - |
| 0.5841 | 210 | 3.0 | - | - | - | - |
| 0.5897 | 212 | 2.9023 | - | - | - | - |
| 0.5953 | 214 | 2.8177 | - | - | - | - |
| 0.6008 | 216 | 2.8262 | 2.4979 | 0.7899 | 0.6037 | 0.9260 |
| 0.6064 | 218 | 2.7832 | - | - | - | - |
| 0.6120 | 220 | 3.0085 | - | - | - | - |
| 0.6175 | 222 | 2.8762 | - | - | - | - |
| 0.6231 | 224 | 3.147 | - | - | - | - |
| 0.6287 | 226 | 3.4262 | - | - | - | - |
| 0.6342 | 228 | 2.8271 | - | - | - | - |
| 0.6398 | 230 | 2.4024 | - | - | - | - |
| 0.6453 | 232 | 2.7556 | - | - | - | - |
| 0.6509 | 234 | 3.4652 | - | - | - | - |
| 0.6565 | 236 | 2.7235 | - | - | - | - |
| 0.6620 | 238 | 2.6498 | - | - | - | - |
| 0.6676 | 240 | 3.0933 | - | - | - | - |
| 0.6732 | 242 | 3.1193 | - | - | - | - |
| 0.6787 | 244 | 2.7249 | - | - | - | - |
| 0.6843 | 246 | 2.8931 | - | - | - | - |
| 0.6898 | 248 | 2.7913 | - | - | - | - |
| 0.6954 | 250 | 2.6933 | - | - | - | - |
| 0.7010 | 252 | 2.5632 | 2.4585 | 0.7700 | 0.6065 | 0.9298 |
| 0.7065 | 254 | 2.8347 | - | - | - | - |
| 0.7121 | 256 | 2.3827 | - | - | - | - |
| 0.7177 | 258 | 2.9065 | - | - | - | - |
| 0.7232 | 260 | 2.8162 | - | - | - | - |
| 0.7288 | 262 | 2.5485 | - | - | - | - |
| 0.7344 | 264 | 2.5751 | - | - | - | - |
| 0.7399 | 266 | 2.9056 | - | - | - | - |
| 0.7455 | 268 | 3.1397 | - | - | - | - |
| 0.7510 | 270 | 3.3107 | - | - | - | - |
| 0.7566 | 272 | 2.9024 | - | - | - | - |
| 0.7622 | 274 | 2.2307 | - | - | - | - |
| 0.7677 | 276 | 3.0097 | - | - | - | - |
| 0.7733 | 278 | 3.1406 | - | - | - | - |
| 0.7789 | 280 | 2.6786 | - | - | - | - |
| 0.7844 | 282 | 2.8882 | - | - | - | - |
| 0.7900 | 284 | 2.7215 | - | - | - | - |
| 0.7955 | 286 | 3.4188 | - | - | - | - |
| 0.8011 | 288 | 2.9901 | 2.4414 | 0.7665 | 0.6023 | 0.9288 |
| 0.8067 | 290 | 2.5144 | - | - | - | - |
| 0.8122 | 292 | 3.1932 | - | - | - | - |
| 0.8178 | 294 | 2.9733 | - | - | - | - |
| 0.8234 | 296 | 2.6895 | - | - | - | - |
| 0.8289 | 298 | 2.678 | - | - | - | - |
| 0.8345 | 300 | 2.5462 | - | - | - | - |
| 0.8401 | 302 | 2.6911 | - | - | - | - |
| 0.8456 | 304 | 2.8404 | - | - | - | - |
| 0.8512 | 306 | 2.5358 | - | - | - | - |
| 0.8567 | 308 | 3.1245 | - | - | - | - |
| 0.8623 | 310 | 2.3404 | - | - | - | - |
| 0.8679 | 312 | 3.0751 | - | - | - | - |
| 0.8734 | 314 | 2.7005 | - | - | - | - |
| 0.8790 | 316 | 2.7387 | - | - | - | - |
| 0.8846 | 318 | 2.7227 | - | - | - | - |
| 0.8901 | 320 | 2.9085 | - | - | - | - |
| 0.8957 | 322 | 3.3239 | - | - | - | - |
| 0.9013 | 324 | 2.4256 | 2.4106 | 0.7644 | 0.6087 | 0.9304 |
| 0.9068 | 326 | 2.5059 | - | - | - | - |
| 0.9124 | 328 | 2.5387 | - | - | - | - |
| 0.9179 | 330 | 2.899 | - | - | - | - |
| 0.9235 | 332 | 2.7256 | - | - | - | - |
| 0.9291 | 334 | 2.4862 | - | - | - | - |
| 0.9346 | 336 | 3.0014 | - | - | - | - |
| 0.9402 | 338 | 2.4164 | - | - | - | - |
| 0.9458 | 340 | 2.3148 | - | - | - | - |
| 0.9513 | 342 | 2.9414 | - | - | - | - |
| 0.9569 | 344 | 2.4435 | - | - | - | - |
| 0.9624 | 346 | 2.6286 | - | - | - | - |
| 0.9680 | 348 | 2.1744 | - | - | - | - |
| 0.9736 | 350 | 2.5866 | - | - | - | - |
| 0.9791 | 352 | 2.8333 | - | - | - | - |
| 0.9847 | 354 | 2.3544 | - | - | - | - |
| 0.9903 | 356 | 2.5397 | - | - | - | - |
| 0.9958 | 358 | 3.4058 | - | - | - | - |
| 1.0014 | 360 | 2.2904 | 2.4089 | 0.7888 | 0.6104 | 0.9338 |
| 1.0070 | 362 | 2.7925 | - | - | - | - |
| 1.0125 | 364 | 2.6415 | - | - | - | - |
| 1.0181 | 366 | 2.724 | - | - | - | - |
| 1.0236 | 368 | 2.569 | - | - | - | - |
| 1.0292 | 370 | 2.808 | - | - | - | - |
| 1.0348 | 372 | 2.4672 | - | - | - | - |
| 1.0403 | 374 | 2.3964 | - | - | - | - |
| 1.0459 | 376 | 2.3518 | - | - | - | - |
| 1.0515 | 378 | 2.7617 | - | - | - | - |
| 1.0570 | 380 | 2.5651 | - | - | - | - |
| 1.0626 | 382 | 2.2623 | - | - | - | - |
| 1.0682 | 384 | 2.2048 | - | - | - | - |
| 1.0737 | 386 | 2.1426 | - | - | - | - |
| 1.0793 | 388 | 1.8182 | - | - | - | - |
| 1.0848 | 390 | 2.3166 | - | - | - | - |
| 1.0904 | 392 | 2.4101 | - | - | - | - |
| 1.0960 | 394 | 2.8932 | - | - | - | - |
| 1.1015 | 396 | 3.0201 | 2.4217 | 0.7851 | 0.6205 | 0.9301 |
| 1.1071 | 398 | 2.6101 | - | - | - | - |
| 1.1127 | 400 | 2.3627 | - | - | - | - |
| 1.1182 | 402 | 2.5402 | - | - | - | - |
| 1.1238 | 404 | 2.695 | - | - | - | - |
| 1.1293 | 406 | 3.0563 | - | - | - | - |
| 1.1349 | 408 | 2.2296 | - | - | - | - |
| 1.1405 | 410 | 3.057 | - | - | - | - |
| 1.1460 | 412 | 2.8023 | - | - | - | - |
| 1.1516 | 414 | 2.6492 | - | - | - | - |
| 1.1572 | 416 | 2.2406 | - | - | - | - |
| 1.1627 | 418 | 1.7195 | - | - | - | - |
| 1.1683 | 420 | 2.2773 | - | - | - | - |
| 1.1739 | 422 | 2.3639 | - | - | - | - |
| 1.1794 | 424 | 2.3348 | - | - | - | - |
| 1.1850 | 426 | 2.6791 | - | - | - | - |
| 1.1905 | 428 | 2.3621 | - | - | - | - |
| 1.1961 | 430 | 2.5224 | - | - | - | - |
| 1.2017 | 432 | 2.4063 | 2.4724 | 0.7628 | 0.6043 | 0.9270 |
| 1.2072 | 434 | 1.9713 | - | - | - | - |
| 1.2128 | 436 | 2.4265 | - | - | - | - |
| 1.2184 | 438 | 2.0827 | - | - | - | - |
| 1.2239 | 440 | 2.0696 | - | - | - | - |
| 1.2295 | 442 | 2.7507 | - | - | - | - |
| 1.2350 | 444 | 2.5436 | - | - | - | - |
| 1.2406 | 446 | 2.4039 | - | - | - | - |
| 1.2462 | 448 | 2.4229 | - | - | - | - |
| 1.2517 | 450 | 2.323 | - | - | - | - |
| 1.2573 | 452 | 2.6099 | - | - | - | - |
| 1.2629 | 454 | 2.0329 | - | - | - | - |
| 1.2684 | 456 | 1.8797 | - | - | - | - |
| 1.2740 | 458 | 1.4485 | - | - | - | - |
| 1.2796 | 460 | 1.6794 | - | - | - | - |
| 1.2851 | 462 | 2.0934 | - | - | - | - |
| 1.2907 | 464 | 1.9579 | - | - | - | - |
| 1.2962 | 466 | 1.9288 | - | - | - | - |
| 1.3018 | 468 | 1.5874 | 2.5056 | 0.7833 | 0.5948 | 0.9345 |
| 1.3074 | 470 | 1.8715 | - | - | - | - |
| 1.3129 | 472 | 1.3778 | - | - | - | - |
| 1.3185 | 474 | 2.2242 | - | - | - | - |
| 1.3241 | 476 | 2.4031 | - | - | - | - |
| 1.3296 | 478 | 1.924 | - | - | - | - |
| 1.3352 | 480 | 1.7895 | - | - | - | - |
| 1.3408 | 482 | 2.0349 | - | - | - | - |
| 1.3463 | 484 | 1.8116 | - | - | - | - |
| 1.3519 | 486 | 2.353 | - | - | - | - |
| 1.3574 | 488 | 3.4263 | - | - | - | - |
| 1.3630 | 490 | 4.0606 | - | - | - | - |
| 1.3686 | 492 | 2.7423 | - | - | - | - |
| 1.3741 | 494 | 2.8461 | - | - | - | - |
| 1.3797 | 496 | 3.0742 | - | - | - | - |
| 1.3853 | 498 | 2.2054 | - | - | - | - |
| 1.3908 | 500 | 2.6009 | - | - | - | - |
| 1.3964 | 502 | 2.242 | - | - | - | - |
| 1.4019 | 504 | 2.9416 | 2.5288 | 0.7969 | 0.6010 | 0.9323 |
| 1.4075 | 506 | 3.8179 | - | - | - | - |
| 1.4131 | 508 | 3.0147 | - | - | - | - |
| 1.4186 | 510 | 2.2185 | - | - | - | - |
| 1.4242 | 512 | 3.0323 | - | - | - | - |
| 1.4298 | 514 | 2.6922 | - | - | - | - |
| 1.4353 | 516 | 2.6219 | - | - | - | - |
| 1.4409 | 518 | 2.4365 | - | - | - | - |
| 1.4465 | 520 | 3.1643 | - | - | - | - |
| 1.4520 | 522 | 2.5548 | - | - | - | - |
| 1.4576 | 524 | 2.3798 | - | - | - | - |
| 1.4631 | 526 | 2.6361 | - | - | - | - |
| 1.4687 | 528 | 2.6859 | - | - | - | - |
| 1.4743 | 530 | 2.6071 | - | - | - | - |
| 1.4798 | 532 | 2.2565 | - | - | - | - |
| 1.4854 | 534 | 2.2415 | - | - | - | - |
| 1.4910 | 536 | 2.4591 | - | - | - | - |
| 1.4965 | 538 | 2.6729 | - | - | - | - |
| 1.5021 | 540 | 2.3898 | 2.5025 | 0.7881 | 0.5978 | 0.9300 |
| 1.5076 | 542 | 2.4614 | - | - | - | - |
| 1.5132 | 544 | 2.5447 | - | - | - | - |
| 1.5188 | 546 | 2.502 | - | - | - | - |
| 1.5243 | 548 | 2.1892 | - | - | - | - |
| 1.5299 | 550 | 2.7081 | - | - | - | - |
| 1.5355 | 552 | 2.5523 | - | - | - | - |
| 1.5410 | 554 | 2.3571 | - | - | - | - |
| 1.5466 | 556 | 2.7694 | - | - | - | - |
| 1.5522 | 558 | 2.2 | - | - | - | - |
| 1.5577 | 560 | 2.4179 | - | - | - | - |
| 1.5633 | 562 | 2.3914 | - | - | - | - |
| 1.5688 | 564 | 2.1722 | - | - | - | - |
| 1.5744 | 566 | 2.345 | - | - | - | - |
| 1.5800 | 568 | 3.0069 | - | - | - | - |
| 1.5855 | 570 | 2.4231 | - | - | - | - |
| 1.5911 | 572 | 2.3597 | - | - | - | - |
| 1.5967 | 574 | 2.143 | - | - | - | - |
| 1.6022 | 576 | 2.6288 | 2.5368 | 0.7943 | 0.6048 | 0.9265 |
| 1.6078 | 578 | 2.3905 | - | - | - | - |
| 1.6134 | 580 | 2.1823 | - | - | - | - |
| 1.6189 | 582 | 2.367 | - | - | - | - |
| 1.6245 | 584 | 2.8189 | - | - | - | - |
| 1.6300 | 586 | 2.6536 | - | - | - | - |
| 1.6356 | 588 | 2.2134 | - | - | - | - |
| 1.6412 | 590 | 1.6949 | - | - | - | - |
| 1.6467 | 592 | 2.2029 | - | - | - | - |
| 1.6523 | 594 | 3.0223 | - | - | - | - |
| 1.6579 | 596 | 2.239 | - | - | - | - |
| 1.6634 | 598 | 2.3388 | - | - | - | - |
| 1.6690 | 600 | 2.3066 | - | - | - | - |
| 1.6745 | 602 | 2.4762 | - | - | - | - |
| 1.6801 | 604 | 1.9503 | - | - | - | - |
| 1.6857 | 606 | 2.1252 | - | - | - | - |
| 1.6912 | 608 | 1.8253 | - | - | - | - |
| 1.6968 | 610 | 2.2938 | - | - | - | - |
| 1.7024 | 612 | 1.9489 | 2.5747 | 0.7675 | 0.5964 | 0.9267 |
| 1.7079 | 614 | 1.9238 | - | - | - | - |
| 1.7135 | 616 | 1.8171 | - | - | - | - |
| 1.7191 | 618 | 2.2371 | - | - | - | - |
| 1.7246 | 620 | 2.4901 | - | - | - | - |
| 1.7302 | 622 | 1.8503 | - | - | - | - |
| 1.7357 | 624 | 2.017 | - | - | - | - |
| 1.7413 | 626 | 2.3069 | - | - | - | - |
| 1.7469 | 628 | 2.444 | - | - | - | - |
| 1.7524 | 630 | 1.9606 | - | - | - | - |
| 1.7580 | 632 | 2.2364 | - | - | - | - |
| 1.7636 | 634 | 1.8711 | - | - | - | - |
| 1.7691 | 636 | 2.4233 | - | - | - | - |
| 1.7747 | 638 | 2.4065 | - | - | - | - |
| 1.7803 | 640 | 2.0725 | - | - | - | - |
| 1.7858 | 642 | 2.0578 | - | - | - | - |
| 1.7914 | 644 | 2.2066 | - | - | - | - |
| 1.7969 | 646 | 1.7767 | - | - | - | - |
| 1.8025 | 648 | 2.7388 | 2.5685 | 0.7663 | 0.5959 | 0.9292 |
| 1.8081 | 650 | 1.854 | - | - | - | - |
| 1.8136 | 652 | 2.7337 | - | - | - | - |
| 1.8192 | 654 | 2.4477 | - | - | - | - |
| 1.8248 | 656 | 2.4818 | - | - | - | - |
| 1.8303 | 658 | 1.8592 | - | - | - | - |
| 1.8359 | 660 | 1.8396 | - | - | - | - |
| 1.8414 | 662 | 2.3893 | - | - | - | - |
| 1.8470 | 664 | 2.0139 | - | - | - | - |
| 1.8526 | 666 | 2.8837 | - | - | - | - |
| 1.8581 | 668 | 2.0342 | - | - | - | - |
| 1.8637 | 670 | 1.8857 | - | - | - | - |
| 1.8693 | 672 | 2.1147 | - | - | - | - |
| 1.8748 | 674 | 1.6263 | - | - | - | - |
| 1.8804 | 676 | 2.2987 | - | - | - | - |
| 1.8860 | 678 | 1.9678 | - | - | - | - |
| 1.8915 | 680 | 1.9999 | - | - | - | - |
| 1.8971 | 682 | 2.2802 | - | - | - | - |
| 1.9026 | 684 | 1.9666 | 2.5536 | 0.7717 | 0.5967 | 0.9289 |
| 1.9082 | 686 | 1.8156 | - | - | - | - |
| 1.9138 | 688 | 1.9542 | - | - | - | - |
| 1.9193 | 690 | 1.859 | - | - | - | - |
| 1.9249 | 692 | 1.6237 | - | - | - | - |
| 1.9305 | 694 | 2.3085 | - | - | - | - |
| 1.9360 | 696 | 2.1461 | - | - | - | - |
| 1.9416 | 698 | 1.7024 | - | - | - | - |
| 1.9471 | 700 | 2.2181 | - | - | - | - |
| 1.9527 | 702 | 2.4782 | - | - | - | - |
| 1.9583 | 704 | 1.7378 | - | - | - | - |
| 1.9638 | 706 | 2.0422 | - | - | - | - |
| 1.9694 | 708 | 1.7577 | - | - | - | - |
| 1.9750 | 710 | 2.0209 | - | - | - | - |
| 1.9805 | 712 | 2.0372 | - | - | - | - |
| 1.9861 | 714 | 2.0915 | - | - | - | - |
| 1.9917 | 716 | 1.603 | - | - | - | - |
| 1.9972 | 718 | 1.7111 | 2.5566 | 0.7705 | 0.5966 | 0.9293 |
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.4.0+cu121
- Accelerate: 0.32.1
- Datasets: 2.21.0
- 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",
}
```
#### AnglELoss
```bibtex
@misc{li2023angleoptimized,
title={AnglE-optimized Text Embeddings},
author={Xianming Li and Jing Li},
year={2023},
eprint={2309.12871},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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