sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:560 loss:OnlineContrastiveLoss arxiv:1908.10084 base_model:srikarvar/fine_tuned_model_5 base_model:finetune:srikarvar/fine_tuned_model_5 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: srikarvar/fine_tuned_model_5
library_name: sentence-transformers
metrics:
- 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:560
- loss:OnlineContrastiveLoss
widget:
- source_sentence: The `Garage` class has a `to_services` method which is used to
    transform tasks into a list of `ServiceRecord` objects that are scheduled.
  sentences:
  - The `to_services` method in the Garage class is used to convert Garage tasks to
    a list of scheduled `ServiceRecord` objects.
  - It returns a `Recipe` for the specified serving size.
  - The AI community is a group of individuals who collaborate on models, datasets,
    and tools to advance artificial intelligence research.
- source_sentence: The main version of the guide contains the INSTALLATION page. Click
    the link to be directed there.
  sentences:
  - You can bake bread by following the Bake bread tutorial.
  - The base class for documents generated from a data stream is StreamBasedBuilder.
  - You can find the INSTALLATION page in the main version of the guide. Click on
    the provided link to redirect to the main version.
- source_sentence: A major distinction between a ProductList and an InventoryList
    is that a ProductList allows for random access to the items, while an InventoryList
    updates gradually as it is navigated.
  sentences:
  - The how-to guides for the platform include Setup, Processing, Streaming, TensorFlow
    integration, PyTorch integration, Cache management, Cloud storage, Search index,
    Analytics, and Data Pipelines.
  - 'Yes, there is a tutorial for analyzing stock market data. You can find it at
    the link provided: /docs/stocks/v2.10.0/data_analysis.'
  - The main difference between a ProductList and an InventoryList is that a ProductList
    provides random access to the items, while an InventoryList updates progressively
    as you browse the list.
- source_sentence: ImageFolder is a dataset builder that eliminates the need for coding
    to quickly load a dataset with thousands of image files. It will automatically
    incorporate any extra data such as resolution, format, or tags, provided that
    it is included in a metadata file (metadata.csv/metadata.jsonl).
  sentences:
  - The function `calc_and_sum` returns the calculated value and sum.
  - Some examples of supported network drives are Network File System (NFS), Server
    Message Block (SMB), and WebDAV.
  - ImageFolder is a dataset builder designed to quickly load an image dataset with
    several thousand image files without requiring you to write any code. It automatically
    loads any additional information about your dataset, such as image resolution,
    format, or image tags, as long as you include this information in a metadata file
    (metadata.csv/metadata.jsonl).
- source_sentence: The `num_services` method gives the quantity of services in the
    garage.
  sentences:
  - A signature in the sales database is a unique identifier for a transaction that
    is updated every time a change is made. It is computed by combining the previous
    signature and a hash of the latest update applied.
  - The `num_services` method returns the number of services in the garage.
  - It returns the number of entries in the dataset.
model-index:
- name: SentenceTransformer based on srikarvar/fine_tuned_model_5
  results:
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: pair class dev
      type: pair-class-dev
    metrics:
    - type: cosine_accuracy
      value: 0.9821428571428571
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.9922685623168945
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.9909909909909909
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.9922685623168945
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 1.0
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9821428571428571
      name: Cosine Recall
    - type: cosine_ap
      value: 1.0
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.9821428571428571
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 0.9922685623168945
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.9909909909909909
      name: Dot F1
    - type: dot_f1_threshold
      value: 0.9922685623168945
      name: Dot F1 Threshold
    - type: dot_precision
      value: 1.0
      name: Dot Precision
    - type: dot_recall
      value: 0.9821428571428571
      name: Dot Recall
    - type: dot_ap
      value: 1.0
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.9821428571428571
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 1.8805665969848633
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.9909909909909909
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 1.8805665969848633
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 1.0
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.9821428571428571
      name: Manhattan Recall
    - type: manhattan_ap
      value: 1.0
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.9821428571428571
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 0.12164457887411118
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.9909909909909909
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 0.12164457887411118
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 1.0
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.9821428571428571
      name: Euclidean Recall
    - type: euclidean_ap
      value: 1.0
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.9821428571428571
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 1.8805665969848633
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.9909909909909909
      name: Max F1
    - type: max_f1_threshold
      value: 1.8805665969848633
      name: Max F1 Threshold
    - type: max_precision
      value: 1.0
      name: Max Precision
    - type: max_recall
      value: 0.9821428571428571
      name: Max Recall
    - type: max_ap
      value: 1.0
      name: Max Ap
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: pair class test
      type: pair-class-test
    metrics:
    - type: cosine_accuracy
      value: 0.9821428571428571
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.9922685623168945
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.9909909909909909
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.9922685623168945
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 1.0
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9821428571428571
      name: Cosine Recall
    - type: cosine_ap
      value: 1.0
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.9821428571428571
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 0.9922685623168945
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.9909909909909909
      name: Dot F1
    - type: dot_f1_threshold
      value: 0.9922685623168945
      name: Dot F1 Threshold
    - type: dot_precision
      value: 1.0
      name: Dot Precision
    - type: dot_recall
      value: 0.9821428571428571
      name: Dot Recall
    - type: dot_ap
      value: 1.0
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.9821428571428571
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 1.8805665969848633
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.9909909909909909
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 1.8805665969848633
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 1.0
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.9821428571428571
      name: Manhattan Recall
    - type: manhattan_ap
      value: 1.0
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.9821428571428571
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 0.12164457887411118
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.9909909909909909
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 0.12164457887411118
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 1.0
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.9821428571428571
      name: Euclidean Recall
    - type: euclidean_ap
      value: 1.0
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.9821428571428571
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 1.8805665969848633
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.9909909909909909
      name: Max F1
    - type: max_f1_threshold
      value: 1.8805665969848633
      name: Max F1 Threshold
    - type: max_precision
      value: 1.0
      name: Max Precision
    - type: max_recall
      value: 0.9821428571428571
      name: Max Recall
    - type: max_ap
      value: 1.0
      name: Max Ap
---

# SentenceTransformer based on srikarvar/fine_tuned_model_5

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [srikarvar/fine_tuned_model_5](https://huggingface.co/srikarvar/fine_tuned_model_5) on the json dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [srikarvar/fine_tuned_model_5](https://huggingface.co/srikarvar/fine_tuned_model_5) <!-- at revision 4e4dc22ad09f760a0a35c55d14d2f89ebe2d2ff2 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - json
<!-- - **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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("srikarvar/fine_tuned_model_12")
# Run inference
sentences = [
    'The `num_services` method gives the quantity of services in the garage.',
    'The `num_services` method returns the number of services in the garage.',
    'It returns the number of entries in the dataset.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
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### 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

#### Binary Classification
* Dataset: `pair-class-dev`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                       | Value   |
|:-----------------------------|:--------|
| cosine_accuracy              | 0.9821  |
| cosine_accuracy_threshold    | 0.9923  |
| cosine_f1                    | 0.991   |
| cosine_f1_threshold          | 0.9923  |
| cosine_precision             | 1.0     |
| cosine_recall                | 0.9821  |
| cosine_ap                    | 1.0     |
| dot_accuracy                 | 0.9821  |
| dot_accuracy_threshold       | 0.9923  |
| dot_f1                       | 0.991   |
| dot_f1_threshold             | 0.9923  |
| dot_precision                | 1.0     |
| dot_recall                   | 0.9821  |
| dot_ap                       | 1.0     |
| manhattan_accuracy           | 0.9821  |
| manhattan_accuracy_threshold | 1.8806  |
| manhattan_f1                 | 0.991   |
| manhattan_f1_threshold       | 1.8806  |
| manhattan_precision          | 1.0     |
| manhattan_recall             | 0.9821  |
| manhattan_ap                 | 1.0     |
| euclidean_accuracy           | 0.9821  |
| euclidean_accuracy_threshold | 0.1216  |
| euclidean_f1                 | 0.991   |
| euclidean_f1_threshold       | 0.1216  |
| euclidean_precision          | 1.0     |
| euclidean_recall             | 0.9821  |
| euclidean_ap                 | 1.0     |
| max_accuracy                 | 0.9821  |
| max_accuracy_threshold       | 1.8806  |
| max_f1                       | 0.991   |
| max_f1_threshold             | 1.8806  |
| max_precision                | 1.0     |
| max_recall                   | 0.9821  |
| **max_ap**                   | **1.0** |

#### Binary Classification
* Dataset: `pair-class-test`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                       | Value   |
|:-----------------------------|:--------|
| cosine_accuracy              | 0.9821  |
| cosine_accuracy_threshold    | 0.9923  |
| cosine_f1                    | 0.991   |
| cosine_f1_threshold          | 0.9923  |
| cosine_precision             | 1.0     |
| cosine_recall                | 0.9821  |
| cosine_ap                    | 1.0     |
| dot_accuracy                 | 0.9821  |
| dot_accuracy_threshold       | 0.9923  |
| dot_f1                       | 0.991   |
| dot_f1_threshold             | 0.9923  |
| dot_precision                | 1.0     |
| dot_recall                   | 0.9821  |
| dot_ap                       | 1.0     |
| manhattan_accuracy           | 0.9821  |
| manhattan_accuracy_threshold | 1.8806  |
| manhattan_f1                 | 0.991   |
| manhattan_f1_threshold       | 1.8806  |
| manhattan_precision          | 1.0     |
| manhattan_recall             | 0.9821  |
| manhattan_ap                 | 1.0     |
| euclidean_accuracy           | 0.9821  |
| euclidean_accuracy_threshold | 0.1216  |
| euclidean_f1                 | 0.991   |
| euclidean_f1_threshold       | 0.1216  |
| euclidean_precision          | 1.0     |
| euclidean_recall             | 0.9821  |
| euclidean_ap                 | 1.0     |
| max_accuracy                 | 0.9821  |
| max_accuracy_threshold       | 1.8806  |
| max_f1                       | 0.991   |
| max_f1_threshold             | 1.8806  |
| max_precision                | 1.0     |
| max_recall                   | 0.9821  |
| **max_ap**                   | **1.0** |

<!--
## 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.*
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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

### Training Dataset

#### json

* Dataset: json
* Size: 560 training samples
* Columns: <code>label</code>, <code>sentence2</code>, and <code>sentence1</code>
* Approximate statistics based on the first 560 samples:
  |         | label                        | sentence2                                                                         | sentence1                                                                        |
  |:--------|:-----------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | int                          | string                                                                            | string                                                                           |
  | details | <ul><li>1: 100.00%</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 30.18 tokens</li><li>max: 98 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 30.0 tokens</li><li>max: 98 tokens</li></ul> |
* Samples:
  | label          | sentence2                                                                                          | sentence1                                                                                       |
  |:---------------|:---------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|
  | <code>1</code> | <code>It is not available in v2.10.0.</code>                                                       | <code>No, it doesn't exist in v2.10.0.</code>                                                   |
  | <code>1</code> | <code>You can become a member of the research forum and pose questions to the AI community.</code> | <code>You can join and ask questions in the AI research forum.</code>                           |
  | <code>1</code> | <code>No information regarding initializing a project for PyTorch is included in the guide.</code> | <code>The guide does not provide information on how to initialize a project for PyTorch.</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

### Evaluation Dataset

#### json

* Dataset: json
* Size: 560 evaluation samples
* Columns: <code>label</code>, <code>sentence2</code>, and <code>sentence1</code>
* Approximate statistics based on the first 560 samples:
  |         | label                        | sentence2                                                                          | sentence1                                                                          |
  |:--------|:-----------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | int                          | string                                                                             | string                                                                             |
  | details | <ul><li>1: 100.00%</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 32.29 tokens</li><li>max: 82 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 31.96 tokens</li><li>max: 82 tokens</li></ul> |
* Samples:
  | label          | sentence2                                                                                                                                                                                                               | sentence1                                                                                                                                                                                                        |
  |:---------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>1</code> | <code>The how-to guides for the platform include instructions for Setup, Processing, Streaming, TensorFlow integration, PyTorch integration, Caching, Cloud storage, Indexing, Analytics, and Data Pipelines.</code>    | <code>The how-to guides for the platform include Setup, Processing, Streaming, TensorFlow integration, PyTorch integration, Cache management, Cloud storage, Search index, Analytics, and Data Pipelines.</code> |
  | <code>1</code> | <code>In the absence of a model script, all files in the supported formats will be loaded. However, if a model script is present, it will be downloaded and executed in order to download and prepare the model.</code> | <code>If there’s no model script, all the files in the supported formats are loaded. If there’s a model script, it is downloaded and executed to download and prepare the model.</code>                          |
  | <code>1</code> | <code>React, Angular, and Vue are compatible with the Plugin library.</code>                                                                                                                                            | <code>The Plugin library can be used with React, Angular, and Vue.</code>                                                                                                                                        |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `gradient_accumulation_steps`: 2
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_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`: 4
- `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`: True
- `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_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch   | Step   | Training Loss | loss       | pair-class-dev_max_ap | pair-class-test_max_ap |
|:-------:|:------:|:-------------:|:----------:|:---------------------:|:----------------------:|
| 0       | 0      | -             | -          | 1.0                   | -                      |
| 1.0     | 8      | -             | 0.0028     | 1.0                   | -                      |
| 1.25    | 10     | 0.1425        | -          | -                     | -                      |
| 2.0     | 16     | -             | 0.0003     | 1.0                   | -                      |
| 2.5     | 20     | 0.002         | -          | -                     | -                      |
| 3.0     | 24     | -             | 0.0001     | 1.0                   | -                      |
| 3.75    | 30     | 0.0008        | -          | -                     | -                      |
| **4.0** | **32** | **-**         | **0.0001** | **1.0**               | **1.0**                |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.0
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.34.2
- Datasets: 2.19.1
- 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",
}
```

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