---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:3000
- loss:BatchAllTripletLoss
base_model: microsoft/mpnet-base
widget:
- source_sentence: what am i supposed to do if i lost my luggage
sentences:
- do i need a visa if i go there
- why did you freeze my bank account
- tell my bank that i'm travelling to france in 2 days
- source_sentence: can you suggest some of the most popular travel destination
sentences:
- what is the total of my repair bill
- could you tell me my bill's minimum payment
- can you get me a car rental for march 1st to 3rd in seattle, and i'd like a sedan
if possible
- source_sentence: is there a minimum amount accepted
sentences:
- am i going to need a visa for traveling to canada
- submit payment to duke energy for my electric bill
- let me know chase's routing number
- source_sentence: my account appears to be blocked and i don't know why
sentences:
- how do you say hello in japanese
- how much is due on the gas bill
- how much was my last transaction for
- source_sentence: are there any travel alerts for juarez
sentences:
- i am now out of checks, how do i order new ones
- lowest amount for cable bill
- how much interest do i get on my citizen's savings account
datasets:
- contemmcm/clinc150
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# 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) on the [clinc150](https://huggingface.co/datasets/contemmcm/clinc150) dataset. 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)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [clinc150](https://huggingface.co/datasets/contemmcm/clinc150)
- **Language:** en
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/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, 'architecture': '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("bnoland/mpnet-base-clinc-subset")
# Run inference
sentences = [
'are there any travel alerts for juarez',
"how much interest do i get on my citizen's savings account",
'lowest amount for cable bill',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7056, 0.6717],
# [0.7056, 1.0000, 0.7377],
# [0.6717, 0.7377, 1.0000]])
```
## Training Details
### Training Dataset
#### clinc150
* Dataset: [clinc150](https://huggingface.co/datasets/contemmcm/clinc150) at [2bbb9af](https://huggingface.co/datasets/contemmcm/clinc150/tree/2bbb9afebdafb9b9f6719250310bfcf3b1e8f666)
* Size: 3,000 training samples
* Columns: text and label
* Approximate statistics based on the first 1000 samples:
| | text | label |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| type | string | int |
| details | - min: 6 tokens
- mean: 12.61 tokens
- max: 28 tokens
| - 1: ~3.60%
- 2: ~3.80%
- 3: ~3.50%
- 4: ~4.30%
- 5: ~3.90%
- 6: ~3.50%
- 7: ~2.20%
- 8: ~3.00%
- 9: ~2.80%
- 10: ~2.90%
- 11: ~3.70%
- 12: ~2.80%
- 13: ~3.70%
- 14: ~2.80%
- 15: ~3.90%
- 76: ~3.60%
- 77: ~3.40%
- 78: ~3.60%
- 79: ~3.40%
- 80: ~3.20%
- 81: ~3.70%
- 82: ~3.00%
- 83: ~2.90%
- 84: ~3.30%
- 85: ~3.50%
- 86: ~3.70%
- 87: ~2.40%
- 88: ~3.70%
- 89: ~2.70%
- 90: ~3.50%
|
* Samples:
| text | label |
|:---------------------------------------------------------------------|:----------------|
| is there enough money in my bank of hawaii for vacation | 12 |
| i need to let my bank know i am visiting asia soon | 77 |
| what's bank of america's routing number | 2 |
* Loss: [BatchAllTripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss)
### Evaluation Dataset
#### clinc150
* Dataset: [clinc150](https://huggingface.co/datasets/contemmcm/clinc150) at [2bbb9af](https://huggingface.co/datasets/contemmcm/clinc150/tree/2bbb9afebdafb9b9f6719250310bfcf3b1e8f666)
* Size: 600 evaluation samples
* Columns: text and label
* Approximate statistics based on the first 600 samples:
| | text | label |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| type | string | int |
| details | - min: 6 tokens
- mean: 12.83 tokens
- max: 26 tokens
| - 1: ~3.33%
- 2: ~3.33%
- 3: ~3.33%
- 4: ~3.33%
- 5: ~3.33%
- 6: ~3.33%
- 7: ~3.33%
- 8: ~3.33%
- 9: ~3.33%
- 10: ~3.33%
- 11: ~3.33%
- 12: ~3.33%
- 13: ~3.33%
- 14: ~3.33%
- 15: ~3.33%
- 76: ~3.33%
- 77: ~3.33%
- 78: ~3.33%
- 79: ~3.33%
- 80: ~3.33%
- 81: ~3.33%
- 82: ~3.33%
- 83: ~3.33%
- 84: ~3.33%
- 85: ~3.33%
- 86: ~3.33%
- 87: ~3.33%
- 88: ~3.33%
- 89: ~3.33%
- 90: ~3.33%
|
* Samples:
| text | label |
|:------------------------------------------------------------------|:----------------|
| was my last transaction at walmart | 14 |
| what interest rate is us bank giving me on my acount | 7 |
| look up carry-on rules for american airlines | 89 |
* Loss: [BatchAllTripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `warmup_steps`: 10
- `fp16`: True
- `batch_sampler`: group_by_label
#### All Hyperparameters
Click to expand
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_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`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_ratio`: None
- `warmup_steps`: 10
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `enable_jit_checkpoint`: False
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `use_cpu`: False
- `seed`: 42
- `data_seed`: None
- `bf16`: False
- `fp16`: True
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: -1
- `ddp_backend`: None
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `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
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `auto_find_batch_size`: False
- `full_determinism`: False
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `use_cache`: False
- `prompts`: None
- `batch_sampler`: group_by_label
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}