sentence-transformers safetensors mpnet sentence-similarity feature-extraction generated_from_trainer dataset_size:24901 loss:SoftmaxLoss arxiv:1908.10084 base_model:microsoft/mpnet-base base_model:finetune:microsoft/mpnet-base autotrain_compatible endpoints_compatible region:us
---
base_model: microsoft/mpnet-base
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:24901
- loss:SoftmaxLoss
widget:
- source_sentence: Cryptocurrency holders are being exploited, with whales creating
    more coins and profiting from their value.
  sentences:
  - Buyer purchases cryptocurrency from seller in exchange.
  - Price fluctuates due to fear and uncertainty, only time will reveal its direction.
  - New user's post removed due to lack of required **karma** and account age.
- source_sentence: User seeks assistance with retrieving funds from a cryptocurrency
    investment platform.
  sentences:
  - Bot removed post for being too short, resubmit with more characters.
  - People enjoy walking while searching for digital currency in their area.
  - Cryptocurrency project's legitimacy unlikely due to complexity and scrutiny in
    parachain development and ecosystem interactions.
- source_sentence: Large cryptocurrencies' market dominance may change as new projects
    emerge with exceptional utility and marketing.
  sentences:
  - Market experiencing significant decline.
  - Decentralized concept in crypto is main idea, but most coins are centralized.
  - Cryptocurrency users share information.
- source_sentence: Use XLM for low-cost transactions between exchanges, saving on
    fees.
  sentences:
  - Exchanges should automate process for increased activity.
  - Investment taxes vary by country, but generally apply after withdrawal, with losses
    still needing declaration.
  - Use basic version, buy coins with credit card.
- source_sentence: New user seeks advice on storing Bitcoin and USDT on WazirX or
    Binance, considering pros and cons.
  sentences:
  - Buy cryptocurrency directly with credit card, but high fee makes Indian exchange
    a better option.
  - 'Cryptocurrency prices: Bitcoin, Ethereum, and others fluctuate.'
  - Investor has faith in Tezos, making strategic moves.
---

# SentenceTransformer based on microsoft/mpnet-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base). 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:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
- **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': False}) with Transformer model: MPNetModel 
  (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("pawan2411/crypto_nli")
# Run inference
sentences = [
    'New user seeks advice on storing Bitcoin and USDT on WazirX or Binance, considering pros and cons.',
    'Buy cryptocurrency directly with credit card, but high fee makes Indian exchange a better option.',
    'Investor has faith in Tezos, making strategic moves.',
]
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]
```

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 24,901 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                        | sentence_1                                                                        | label                                           |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                            | string                                                                            | int                                             |
  | details | <ul><li>min: 6 tokens</li><li>mean: 21.86 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.67 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>0: ~83.50%</li><li>1: ~16.50%</li></ul> |
* Samples:
  | sentence_0                                                                                                              | sentence_1                                                                                       | label          |
  |:------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------|
  | <code>User asks about tracing crypto swaps and process of exchanging digital currencies.</code>                         | <code>"Private cryptocurrency swap can't be traced."</code>                                      | <code>0</code> |
  | <code>Cryptocurrency project with weak fundamentals deserves to fail, cherish coins before next market downturn.</code> | <code>"Trust information in this community."</code>                                              | <code>0</code> |
  | <code>New user seeks advice on using crypto credit cards in daily life.</code>                                          | <code>User uses digital wallet for cryptocurrency transactions, earning cashback rewards.</code> | <code>1</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)

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

- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 10
- `multi_dataset_batch_sampler`: round_robin

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `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
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: 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`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 1.2821 | 500  | 0.3912        |
| 2.5641 | 1000 | 0.3157        |
| 3.8462 | 1500 | 0.2926        |
| 5.1282 | 2000 | 0.2788        |
| 6.4103 | 2500 | 0.2599        |
| 7.6923 | 3000 | 0.2428        |
| 8.9744 | 3500 | 0.2314        |
| 1.2821 | 500  | 0.2333        |
| 2.5641 | 1000 | 0.2292        |
| 3.8462 | 1500 | 0.1987        |
| 5.1282 | 2000 | 0.1757        |
| 6.4103 | 2500 | 0.1578        |
| 7.6923 | 3000 | 0.1413        |
| 8.9744 | 3500 | 0.1258        |
| 1.2821 | 500  | 0.1086        |
| 2.5641 | 1000 | 0.1048        |
| 3.8462 | 1500 | 0.0917        |
| 5.1282 | 2000 | 0.0805        |
| 6.4103 | 2500 | 0.0712        |
| 7.6923 | 3000 | 0.0673        |
| 8.9744 | 3500 | 0.0646        |
| 1.2821 | 500  | 0.0505        |
| 2.5641 | 1000 | 0.0511        |
| 3.8462 | 1500 | 0.046         |
| 5.1282 | 2000 | 0.0415        |
| 6.4103 | 2500 | 0.0396        |
| 7.6923 | 3000 | 0.0357        |
| 8.9744 | 3500 | 0.0382        |
| 1.2821 | 500  | 0.0252        |
| 2.5641 | 1000 | 0.029         |
| 3.8462 | 1500 | 0.0247        |
| 5.1282 | 2000 | 0.0233        |
| 6.4103 | 2500 | 0.0228        |
| 7.6923 | 3000 | 0.0218        |
| 8.9744 | 3500 | 0.0251        |
| 1.2821 | 500  | 0.0158        |
| 2.5641 | 1000 | 0.0184        |
| 3.8462 | 1500 | 0.0165        |
| 5.1282 | 2000 | 0.0139        |
| 6.4103 | 2500 | 0.0145        |
| 7.6923 | 3000 | 0.0139        |
| 8.9744 | 3500 | 0.0164        |


### 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 and SoftmaxLoss
```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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