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---
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: Users are entitled to a refund for excess payments after necessary
deductions, provided that payments were not processed to a wrong account due to
user error.
sentences:
- What is the timeline for the delivery of the documentary film as outlined in this
contract?
- Under what circumstances can a user receive a refund for multiple payments made
for a single order?
- What are the Payment Terms for the Batteries?
- 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?
- How should proprietary and confidential information disclosed under the Agreement
be treated by the Parties?
- When does this Agreement terminate?
- source_sentence: If there is any unreasonable delay in the refund process, the User
can report it to Customer Care at contact@paratalks.in or +91-9116768791.
sentences:
- What should a User do if there is an unreasonable delay in the refund process?
- What are the confidentiality provisions in this contract?
- What are the specified payment terms for the photography services under this contract?
- source_sentence: The refund (if permitted by the Platform) shall be processed after
deductions, which may include transaction charges levied by the bank and/or the
payment gateway, as well as any other charges incurred by the Platform for facilitating
the payment or refund.
sentences:
- What are the conditions under which a user is not entitled to a refund according
to Paratalks' refund policy?
- What is the jurisdiction and governing law applicable to this contract?
- How are refunds processed if permitted by the Platform?
- source_sentence: This Agreement shall be governed by and construed in accordance
with the laws of Indiana. Any dispute arising out of or in connection with this
Agreement shall be resolved through good faith negotiations between the Parties
and will be subject to the jurisdiction of the courts of Dania.
sentences:
- Under what condition will the User not be entitled to a refund if the payment
is processed to a wrong Account?
- What events constitute Force Majeure under this Agreement?
- Under which laws is the Battery Supply Agreement governed and how are disputes
resolved?
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: 0.8333333333333334
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: 0.8333333333333334
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: 0.8333333333333334
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: 0.892701197851337
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8611111111111112
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8611111111111112
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.8333333333333334
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: 0.8333333333333334
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: 0.8333333333333334
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: 0.892701197851337
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8611111111111112
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8611111111111112
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.8333333333333334
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: 0.8333333333333334
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: 0.8333333333333334
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: 0.892701197851337
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8611111111111112
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8611111111111112
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.8333333333333334
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: 0.8333333333333334
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: 0.8333333333333334
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: 0.8859108127976215
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8541666666666666
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8541666666666666
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.8333333333333334
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: 0.8333333333333334
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: 0.8333333333333334
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: 0.8835049992773302
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8518518518518517
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8518518518518517
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/fm1")
# Run inference
sentences = [
'This Agreement shall be governed by and construed in accordance with the laws of Indiana. Any dispute arising out of or in connection with this Agreement shall be resolved through good faith negotiations between the Parties and will be subject to the jurisdiction of the courts of Dania.',
'Under which laws is the Battery Supply Agreement governed and how are disputes resolved?',
'What events constitute Force Majeure under this 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 | 0.8333 |
| cosine_accuracy@3 | 0.8333 |
| cosine_accuracy@5 | 0.8333 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.8333 |
| cosine_precision@3 | 0.2778 |
| cosine_precision@5 | 0.1667 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.8333 |
| cosine_recall@3 | 0.8333 |
| cosine_recall@5 | 0.8333 |
| cosine_recall@10 | 1.0 |
| cosine_ndcg@10 | 0.8927 |
| cosine_mrr@10 | 0.8611 |
| **cosine_map@100** | **0.8611** |
#### 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 | 0.8333 |
| cosine_accuracy@3 | 0.8333 |
| cosine_accuracy@5 | 0.8333 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.8333 |
| cosine_precision@3 | 0.2778 |
| cosine_precision@5 | 0.1667 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.8333 |
| cosine_recall@3 | 0.8333 |
| cosine_recall@5 | 0.8333 |
| cosine_recall@10 | 1.0 |
| cosine_ndcg@10 | 0.8927 |
| cosine_mrr@10 | 0.8611 |
| **cosine_map@100** | **0.8611** |
#### 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 | 0.8333 |
| cosine_accuracy@3 | 0.8333 |
| cosine_accuracy@5 | 0.8333 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.8333 |
| cosine_precision@3 | 0.2778 |
| cosine_precision@5 | 0.1667 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.8333 |
| cosine_recall@3 | 0.8333 |
| cosine_recall@5 | 0.8333 |
| cosine_recall@10 | 1.0 |
| cosine_ndcg@10 | 0.8927 |
| cosine_mrr@10 | 0.8611 |
| **cosine_map@100** | **0.8611** |
#### 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 | 0.8333 |
| cosine_accuracy@3 | 0.8333 |
| cosine_accuracy@5 | 0.8333 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.8333 |
| cosine_precision@3 | 0.2778 |
| cosine_precision@5 | 0.1667 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.8333 |
| cosine_recall@3 | 0.8333 |
| cosine_recall@5 | 0.8333 |
| cosine_recall@10 | 1.0 |
| cosine_ndcg@10 | 0.8859 |
| cosine_mrr@10 | 0.8542 |
| **cosine_map@100** | **0.8542** |
#### 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 | 0.8333 |
| cosine_accuracy@3 | 0.8333 |
| cosine_accuracy@5 | 0.8333 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.8333 |
| cosine_precision@3 | 0.2778 |
| cosine_precision@5 | 0.1667 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.8333 |
| cosine_recall@3 | 0.8333 |
| cosine_recall@5 | 0.8333 |
| cosine_recall@10 | 1.0 |
| cosine_ndcg@10 | 0.8835 |
| cosine_mrr@10 | 0.8519 |
| **cosine_map@100** | **0.8519** |
<!--
## 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: 39.58 tokens</li><li>max: 85 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 17.9 tokens</li><li>max: 32 tokens</li></ul> |
* Samples:
| context | question |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|
| <code>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.</code> | <code>What are the specified payment terms for the photography services under this contract?</code> |
| <code>Users can report delays to Customer Care and expect an automatic refund within 3-4 business days if services are canceled or rescheduled by the platform.</code> | <code>What actions can a user take if the platform is unable to fulfill a successfully placed order?</code> |
| <code>Signed by James Hira, Managing Director of Electric Vehicle Battery Supplier Pvt. Ltd, and Managing Director of Best Car Manufacturer Pvt. Ltd</code> | <code>Who signed the Battery Supply Agreement on behalf of the Supplier and the Manufacturer?</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 | 0.8542 | 0.8611 | 0.8611 | 0.8519 | 0.8611 |
### 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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