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nomic-embed-text-v1.5-Chatbot-matryoshka

Sentence Similarity
sentence-transformers
Safetensors
English
nomic_bert
feature-extraction
Generated from Trainer
dataset_size:530
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
custom_code
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
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---
base_model: nomic-ai/nomic-embed-text-v1.5
datasets: []
language:
- en
library_name: sentence-transformers
license: apache-2.0
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:530
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: If you receive a BharatPe speaker that you didn't order, please
contact BharatPe support immediately. They will assist in resolving the issue
and advise on the next steps.
sentences:
- Can I control multiple BharatPe speakers from one app?
- What to do if the BharatPe speaker's transaction announcements are intermittently
silent?
- What should I do if I receive a BharatPe speaker without ordering it?
- source_sentence: Remote control capabilities depend on the model of the BharatPe
speaker. Check if your model supports remote control through the BharatPe app
or a connected device.
sentences:
- How do I update my personal details in my Bharatpe account?
- What are the benefits of the BharatPe speaker?
- Can I control the BharatPe speaker remotely?
- source_sentence: If the announcements are not clear, check the speaker's volume
settings and ensure it's not placed near noisy equipment. If clarity doesn't improve,
the speaker may need servicing.
sentences:
- What to do if my BharatPe speaker is not syncing with the transaction history
in the app?
- What should I do if the speaker is not announcing payments clearly?
- The speaker doesn't produce any sound, what can be done?
- source_sentence: If the speaker is causing interference, try relocating it or other
devices to reduce the interference. Ensure there's a reasonable distance between
the speaker and other wireless equipment.
sentences:
- Can I use my Bharatpe device for international transactions?
- How do I know if my BharatPe speaker is under warranty?
- What should I do if the BharatPe speaker is causing interference with other wireless
devices?
- source_sentence: I can understand and respond in multiple Indian regional languages.
Feel free to communicate with me in the language you're most comfortable with.
sentences:
- How can I check if the BharatPe speaker is receiving a network signal?
- Bharti, can you provide tips for effective online communication?
- Bharti, what languages can you understand and respond to?
model-index:
- name: Nomic v1.5 Chatbot Matryoshka
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.9069767441860465
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9767441860465116
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9767441860465116
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9767441860465116
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9069767441860465
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.32558139534883723
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1953488372093023
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09767441860465115
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.9069767441860465
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9767441860465116
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9767441860465116
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9767441860465116
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9509950990863808
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9418604651162791
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.942829457364341
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.9069767441860465
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9767441860465116
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9767441860465116
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9767441860465116
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9069767441860465
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.32558139534883723
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1953488372093023
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09767441860465115
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.9069767441860465
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9767441860465116
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9767441860465116
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9767441860465116
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9509950990863808
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9418604651162791
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9426356589147287
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.8837209302325582
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9534883720930233
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9767441860465116
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9767441860465116
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8837209302325582
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3178294573643411
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1953488372093023
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09767441860465115
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8837209302325582
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9534883720930233
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9767441860465116
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9767441860465116
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.937755019041576
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9244186046511628
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9246686671667917
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.8837209302325582
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9767441860465116
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9767441860465116
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9767441860465116
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8837209302325582
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.32558139534883723
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1953488372093023
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09767441860465115
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8837209302325582
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9767441860465116
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9767441860465116
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9767441860465116
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9393671921096366
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9263565891472867
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9263565891472867
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.9302325581395349
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9767441860465116
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9767441860465116
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9767441860465116
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9302325581395349
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.32558139534883723
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1953488372093023
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09767441860465115
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.9302325581395349
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9767441860465116
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9767441860465116
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9767441860465116
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9595781280730911
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9534883720930233
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9537827494848395
name: Cosine Map@100
---
# Nomic v1.5 Chatbot Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-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:** [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) <!-- at revision c4f06e01594879a8ccc5c40b0b0a0e2ad46e3a62 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0
### 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': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("MANMEET75/nomic-embed-text-v1.5-Chatbot-matryoshka")
# Run inference
sentences = [
"I can understand and respond in multiple Indian regional languages. Feel free to communicate with me in the language you're most comfortable with.",
'Bharti, what languages can you understand and respond to?',
'Bharti, can you provide tips for effective online communication?',
]
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 | 0.907 |
| cosine_accuracy@3 | 0.9767 |
| cosine_accuracy@5 | 0.9767 |
| cosine_accuracy@10 | 0.9767 |
| cosine_precision@1 | 0.907 |
| cosine_precision@3 | 0.3256 |
| cosine_precision@5 | 0.1953 |
| cosine_precision@10 | 0.0977 |
| cosine_recall@1 | 0.907 |
| cosine_recall@3 | 0.9767 |
| cosine_recall@5 | 0.9767 |
| cosine_recall@10 | 0.9767 |
| cosine_ndcg@10 | 0.951 |
| cosine_mrr@10 | 0.9419 |
| **cosine_map@100** | **0.9428** |
#### 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 | 0.907 |
| cosine_accuracy@3 | 0.9767 |
| cosine_accuracy@5 | 0.9767 |
| cosine_accuracy@10 | 0.9767 |
| cosine_precision@1 | 0.907 |
| cosine_precision@3 | 0.3256 |
| cosine_precision@5 | 0.1953 |
| cosine_precision@10 | 0.0977 |
| cosine_recall@1 | 0.907 |
| cosine_recall@3 | 0.9767 |
| cosine_recall@5 | 0.9767 |
| cosine_recall@10 | 0.9767 |
| cosine_ndcg@10 | 0.951 |
| cosine_mrr@10 | 0.9419 |
| **cosine_map@100** | **0.9426** |
#### 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 | 0.8837 |
| cosine_accuracy@3 | 0.9535 |
| cosine_accuracy@5 | 0.9767 |
| cosine_accuracy@10 | 0.9767 |
| cosine_precision@1 | 0.8837 |
| cosine_precision@3 | 0.3178 |
| cosine_precision@5 | 0.1953 |
| cosine_precision@10 | 0.0977 |
| cosine_recall@1 | 0.8837 |
| cosine_recall@3 | 0.9535 |
| cosine_recall@5 | 0.9767 |
| cosine_recall@10 | 0.9767 |
| cosine_ndcg@10 | 0.9378 |
| cosine_mrr@10 | 0.9244 |
| **cosine_map@100** | **0.9247** |
#### 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 | 0.8837 |
| cosine_accuracy@3 | 0.9767 |
| cosine_accuracy@5 | 0.9767 |
| cosine_accuracy@10 | 0.9767 |
| cosine_precision@1 | 0.8837 |
| cosine_precision@3 | 0.3256 |
| cosine_precision@5 | 0.1953 |
| cosine_precision@10 | 0.0977 |
| cosine_recall@1 | 0.8837 |
| cosine_recall@3 | 0.9767 |
| cosine_recall@5 | 0.9767 |
| cosine_recall@10 | 0.9767 |
| cosine_ndcg@10 | 0.9394 |
| cosine_mrr@10 | 0.9264 |
| **cosine_map@100** | **0.9264** |
#### 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 | 0.9302 |
| cosine_accuracy@3 | 0.9767 |
| cosine_accuracy@5 | 0.9767 |
| cosine_accuracy@10 | 0.9767 |
| cosine_precision@1 | 0.9302 |
| cosine_precision@3 | 0.3256 |
| cosine_precision@5 | 0.1953 |
| cosine_precision@10 | 0.0977 |
| cosine_recall@1 | 0.9302 |
| cosine_recall@3 | 0.9767 |
| cosine_recall@5 | 0.9767 |
| cosine_recall@10 | 0.9767 |
| cosine_ndcg@10 | 0.9596 |
| cosine_mrr@10 | 0.9535 |
| **cosine_map@100** | **0.9538** |
<!--
## 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: 530 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 11 tokens</li><li>mean: 35.33 tokens</li><li>max: 99 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 17.3 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
| positive | anchor |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------|
| <code>BharatPe Speaker comes with the following benefits: - Helps you avoid payment fraud - Lightweight & Easy installation process - Compatible with SIM & GPRS connectivity - Comes with a battery, no hassle of constant charging - Available in 10 Languages - Cashback Offers - Free replacement To Know more and place an order, tap below http://bharatpe.in/speaker.</code> | <code>What are the benefits of the BharatPe speaker?</code> |
| <code>BharatPe Speaker comes with the following benefits: - Helps you avoid payment fraud - Lightweight & Easy installation process - Compatible with SIM & GPRS connectivity - Comes with a battery, no hassle of constant charging - Available in 10 Languages - Cashback Offers - Free replacement To Know more and place an order, tap below http://bharatpe.in/speaker.</code> | <code>What advantages does the BharatPe speaker offer?</code> |
| <code>BharatPe Speaker comes with the following benefits: - Helps you avoid payment fraud - Lightweight & Easy installation process - Compatible with SIM & GPRS connectivity - Comes with a battery, no hassle of constant charging - Available in 10 Languages - Cashback Offers - Free replacement To Know more and place an order, tap below http://bharatpe.in/speaker.</code> | <code>Can you outline the benefits of using the BharatPe speaker?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 10
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `tf32`: False
- `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`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `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`: False
- `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 | 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 |
|:----------:|:-----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.9412 | 1 | - | 0.7883 | 0.8148 | 0.8134 | 0.7657 | 0.8234 |
| 1.8824 | 2 | - | 0.8953 | 0.8956 | 0.8859 | 0.8273 | 0.8855 |
| 2.8235 | 3 | - | 0.9167 | 0.9150 | 0.9310 | 0.8926 | 0.9292 |
| 3.7647 | 4 | - | 0.9205 | 0.9208 | 0.9348 | 0.9073 | 0.9349 |
| 4.7059 | 5 | - | 0.9244 | 0.9247 | 0.9348 | 0.9151 | 0.9388 |
| 5.6471 | 6 | - | 0.9244 | 0.9247 | 0.9387 | 0.9189 | 0.9389 |
| 6.5882 | 7 | - | 0.9244 | 0.9247 | 0.9387 | 0.9189 | 0.9389 |
| 7.5294 | 8 | - | 0.9244 | 0.9247 | 0.9388 | 0.9538 | 0.9428 |
| **8.4706** | **9** | **-** | **0.9264** | **0.9247** | **0.9426** | **0.9538** | **0.9428** |
| 9.4118 | 10 | 1.9538 | 0.9264 | 0.9247 | 0.9426 | 0.9538 | 0.9428 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.32.1
- 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",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### 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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