---
base_model: google-bert/bert-base-uncased
datasets:
- stanfordnlp/snli
language:
- en
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:494430
- loss:SoftmaxLoss
widget:
- source_sentence: A person out front of a business with a woman statue holding a
bottle.
sentences:
- A couple holds hands.
- The young boy is upside down.
- the man is baking some bread
- source_sentence: A person is dressed up in a weird costume with a red tongue sticking
out.
sentences:
- thhe man plays a tuba
- Four siblings are climbing on a fake black bear.
- the tongue is blue
- source_sentence: A man on a train is talking on a cellphone.
sentences:
- A man is playing a flute on a bus.
- The woman is sexy.
- two cyclists racing
- source_sentence: An elderly woman giving her daughter a hug.
sentences:
- There are two women hugging.
- A man holds a flag on the street.
- people are sitting on a red roofed bus going to a museum
- source_sentence: A pilot dressed in a dark-colored sweater is sitting in the cock-pit
of a plane with his hands crossed.
sentences:
- A pilot is sitting in his plain with his hands crossed
- The boys are playing outside on a log.
- Two men discuss their love lives.
---
# SentenceTransformer based on google-bert/bert-base-uncased
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co./google-bert/bert-base-uncased) on the [stanfordnlp/snli](https://huggingface.co./datasets/stanfordnlp/snli) 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:** [google-bert/bert-base-uncased](https://huggingface.co./google-bert/bert-base-uncased)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [stanfordnlp/snli](https://huggingface.co./datasets/stanfordnlp/snli)
- **Language:** en
### 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: BertModel
(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("hcy5561/distilroberta-base-sentence-transformer-snli")
# Run inference
sentences = [
'A pilot dressed in a dark-colored sweater is sitting in the cock-pit of a plane with his hands crossed.',
'A pilot is sitting in his plain with his hands crossed',
'The boys are playing outside on a log.',
]
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]
```
## Training Details
### Training Dataset
#### stanfordnlp/snli
* Dataset: [stanfordnlp/snli](https://huggingface.co./datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co./datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
* Size: 494,430 training samples
* Columns: premise, hypothesis, and label
* Approximate statistics based on the first 1000 samples:
| | premise | hypothesis | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
| type | string | string | int |
| details | - min: 6 tokens
- mean: 16.24 tokens
- max: 50 tokens
| - min: 4 tokens
- mean: 10.55 tokens
- max: 26 tokens
| - 0: ~31.10%
- 1: ~33.40%
- 2: ~35.50%
|
* Samples:
| premise | hypothesis | label |
|:------------------------------------------------------------------------------|:---------------------------------------|:---------------|
| Two men, one in yellow, are on a wooden boat. | Two men swimming in water | 2 |
| Two people sleep on a couch. | Two people are asleep. | 0 |
| a little boy is learning to swim with the help of a float board. | The boy is crawling. | 2 |
* Loss: [SoftmaxLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
### Evaluation Dataset
#### stanfordnlp/snli
* Dataset: [stanfordnlp/snli](https://huggingface.co./datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co./datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
* Size: 27,468 evaluation samples
* Columns: premise, hypothesis, and label
* Approximate statistics based on the first 1000 samples:
| | premise | hypothesis | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
| type | string | string | int |
| details | - min: 6 tokens
- mean: 16.66 tokens
- max: 44 tokens
| - min: 4 tokens
- mean: 10.48 tokens
- max: 31 tokens
| - 0: ~36.10%
- 1: ~31.80%
- 2: ~32.10%
|
* Samples:
| premise | hypothesis | label |
|:---------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|:---------------|
| A taxi cab driver looks stressed out in his car. | a taxi driver is stressed | 0 |
| Two men do trick in a park. | The men only sat on the bench in the park, doing nothing. | 2 |
| Two woman walking, the blond is looking at the camera wearing sunglasses making an oh face. | One lady makes a shocked face at the camera as the photographer tells the women they are lost. | 1 |
* Loss: [SoftmaxLoss](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`: 4
- `warmup_ratio`: 0.1
- `batch_sampler`: no_duplicates
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `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`: 1.0
- `num_train_epochs`: 4
- `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
- `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}
- `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
- `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_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional