sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:48 loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:1705.00652 base_model:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-base-en-v1.5
datasets: []
language: []
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:48
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: The Supplier shall deliver the Batteries to the Manufacturer within
    5 days of receipt of each monthly purchase order.
  sentences:
  - What rights does the Manufacturer have regarding the inspection and rejection  non-conforming
    Batteries?
  - What is the Delivery Schedule for the Batteries?
  - What constitutes a force majeure event under the Agreement?
- source_sentence: The Client will pay a flat fee of Rs. 52,000/-, with 50% (Rs. 26,000/-)
    due upon signing the agreement and the remaining 50% due one week after completion
    of pre-production. Payment delays will result in proportional delays in data delivery
    and editing.
  sentences:
  - What are the specified payment terms for the photography services under this contract?
  - What actions can a user take if the platform is unable to fulfill a successfully
    placed order?
  - What is the delivery schedule for the Batteries once the purchase order is received?
- source_sentence: Users can contact Customer Care before confirmation to request
    a refund for offline services or reschedule for online services, subject to the
    platform's discretion.
  sentences:
  - How does Paratalks handle refund requests made before a service professional confirms
    a booking?
  - What is the total quantity of electric vehicle batteries that the Supplier agrees
    to supply to the Manufacturer?
  - What are the conditions under which a user is not entitled to a refund according
    to Paratalks' refund policy?
- source_sentence: In the event of a material breach of this Agreement by either Party,
    the non-breaching Party shall be entitled to pursue all available remedies at
    law or in equity, including injunctive relief and damages.
  sentences:
  - Under what conditions may this agreement be terminated?
  - What events constitute Force Majeure under this Agreement?
  - What remedies are available to a non-breaching Party in the event of a material
    breach of the Agreement?
- source_sentence: No refund shall be issued in case wrong contact details are provided
    by the User or the User's device being unreachable, or any other technical glitch
    attributable to the User. Additionally, no refund shall be issued for any live-session
    or call, whether audio or video, once the call or live-session is connected.
  sentences:
  - What deductions may be applied when processing refunds according to Paratalks'
    refund policy?
  - What are the initial job title and duties of the Employee as stated in the employment
    agreement?
  - What circumstances lead to no refund being issued to a User according to the Refund
    Policy?
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.3333333333333333
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 1.0
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 1.0
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.3333333333333333
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3333333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19999999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09999999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.3333333333333333
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 1.0
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 1.0
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7321315434523954
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.638888888888889
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.638888888888889
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.3333333333333333
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 1.0
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 1.0
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.3333333333333333
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3333333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19999999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09999999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.3333333333333333
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 1.0
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 1.0
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7321315434523954
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.638888888888889
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.638888888888889
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.5
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8333333333333334
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 1.0
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27777777777777773
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19999999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09999999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8333333333333334
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 1.0
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7747853857295762
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7000000000000001
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7000000000000001
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.5
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8333333333333334
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8333333333333334
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.27777777777777773
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16666666666666666
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09999999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8333333333333334
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8333333333333334
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7604815838011495
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6851851851851851
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6851851851851851
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.5
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.6666666666666666
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6666666666666666
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8333333333333334
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2222222222222222
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.13333333333333333
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08333333333333333
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.6666666666666666
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6666666666666666
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8333333333333334
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.66452282344658
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.611111111111111
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6262626262626262
      name: Cosine Map@100
---

# SentenceTransformer based on BAAI/bge-base-en-v1.5

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

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

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

# Download from the 🤗 Hub
model = SentenceTransformer("vineet10/fm2")
# Run inference
sentences = [
    "No refund shall be issued in case wrong contact details are provided by the User or the User's device being unreachable, or any other technical glitch attributable to the User. Additionally, no refund shall be issued for any live-session or call, whether audio or video, once the call or live-session is connected.",
    'What circumstances lead to no refund being issued to a User according to the Refund Policy?',
    'What are the initial job title and duties of the Employee as stated in the employment agreement?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

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

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

</details>
-->

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

You can finetune this model on your own dataset.

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

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.3333     |
| cosine_accuracy@3   | 1.0        |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.3333     |
| cosine_precision@3  | 0.3333     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.3333     |
| cosine_recall@3     | 1.0        |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.7321     |
| cosine_mrr@10       | 0.6389     |
| **cosine_map@100**  | **0.6389** |

#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.3333     |
| cosine_accuracy@3   | 1.0        |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.3333     |
| cosine_precision@3  | 0.3333     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.3333     |
| cosine_recall@3     | 1.0        |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.7321     |
| cosine_mrr@10       | 0.6389     |
| **cosine_map@100**  | **0.6389** |

#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value   |
|:--------------------|:--------|
| cosine_accuracy@1   | 0.5     |
| cosine_accuracy@3   | 0.8333  |
| cosine_accuracy@5   | 1.0     |
| cosine_accuracy@10  | 1.0     |
| cosine_precision@1  | 0.5     |
| cosine_precision@3  | 0.2778  |
| cosine_precision@5  | 0.2     |
| cosine_precision@10 | 0.1     |
| cosine_recall@1     | 0.5     |
| cosine_recall@3     | 0.8333  |
| cosine_recall@5     | 1.0     |
| cosine_recall@10    | 1.0     |
| cosine_ndcg@10      | 0.7748  |
| cosine_mrr@10       | 0.7     |
| **cosine_map@100**  | **0.7** |

#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.5        |
| cosine_accuracy@3   | 0.8333     |
| cosine_accuracy@5   | 0.8333     |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.5        |
| cosine_precision@3  | 0.2778     |
| cosine_precision@5  | 0.1667     |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.5        |
| cosine_recall@3     | 0.8333     |
| cosine_recall@5     | 0.8333     |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.7605     |
| cosine_mrr@10       | 0.6852     |
| **cosine_map@100**  | **0.6852** |

#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.5        |
| cosine_accuracy@3   | 0.6667     |
| cosine_accuracy@5   | 0.6667     |
| cosine_accuracy@10  | 0.8333     |
| cosine_precision@1  | 0.5        |
| cosine_precision@3  | 0.2222     |
| cosine_precision@5  | 0.1333     |
| cosine_precision@10 | 0.0833     |
| cosine_recall@1     | 0.5        |
| cosine_recall@3     | 0.6667     |
| cosine_recall@5     | 0.6667     |
| cosine_recall@10    | 0.8333     |
| cosine_ndcg@10      | 0.6645     |
| cosine_mrr@10       | 0.6111     |
| **cosine_map@100**  | **0.6263** |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 48 training samples
* Columns: <code>context</code> and <code>question</code>
* Approximate statistics based on the first 1000 samples:
  |         | context                                                                           | question                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            |
  | details | <ul><li>min: 18 tokens</li><li>mean: 41.0 tokens</li><li>max: 85 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 17.88 tokens</li><li>max: 32 tokens</li></ul> |
* Samples:
  | context                                                                                                                                                                                  | question                                                                                                                  |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------|
  | <code>The contract is governed by the laws of India, and any disputes shall be resolved exclusively by the courts in Kota.</code>                                                        | <code>What is the jurisdiction and governing law applicable to this contract?</code>                                      |
  | <code>The Parties shall maintain the confidentiality of all proprietary and confidential information disclosed by one Party to the other Party in connection with this Agreement.</code> | <code>How should proprietary and confidential information disclosed under the Agreement be treated by the Parties?</code> |
  | <code>No refund shall be provided for any products or merchandise that is purchased by the User from or through the Platform.</code>                                                     | <code>What is the refund policy for products or merchandise purchased by the User through the Platform?</code>            |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `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`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch | Step | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:-----:|:----:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0     | 0    | 0.6852                 | 0.7000                 | 0.6389                 | 0.6263                | 0.6389                 |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Accelerate: 0.32.1
- Datasets: 2.20.0
- Tokenizers: 0.19.1

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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