---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:80
- loss:CoSENTLoss
base_model: abdeljalilELmajjodi/model
widget:
- source_sentence: A couple play in the tide with their young son.
sentences:
- The family is outside.
- A couple are playing frisbee with a young child at the beach.
- Two adults swimming in water
- source_sentence: A man and a woman cross the street in front of a pizza and gyro
restaurant.
sentences:
- A person that is hungry.
- A man is advertising for a restaurant.
- Near a couple of restaurants, two people walk across the street.
- source_sentence: People waiting to get on a train or just getting off.
sentences:
- There are people just getting on a train
- The friends have just met for the first time in 20 years, and have had a great
time catching up.
- A couple watch a little girl play by herself on the beach.
- source_sentence: A few people in a restaurant setting, one of them is drinking orange
juice.
sentences:
- The diners are at a restaurant.
- The two sisters saw each other across the crowded diner and shared a hug, both
clutching their doggie bags.
- The people are related.
- source_sentence: A woman is walking across the street eating a banana, while a man
is following with his briefcase.
sentences:
- A blond man wearing a brown shirt is reading a book on a bench in the park
- A woman eats ice cream walking down the sidewalk, and there is another woman in
front of her with a purse.
- The woman and man are playing baseball together.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on abdeljalilELmajjodi/model
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: pair score evaluator dev
type: pair-score-evaluator-dev
metrics:
- type: pearson_cosine
value: -0.5092696642724214
name: Pearson Cosine
- type: spearman_cosine
value: -0.4962172958016046
name: Spearman Cosine
---
# SentenceTransformer based on abdeljalilELmajjodi/model
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [abdeljalilELmajjodi/model](https://huggingface.co/abdeljalilELmajjodi/model) on the all-nli dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [abdeljalilELmajjodi/model](https://huggingface.co/abdeljalilELmajjodi/model)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Supported Modality:** Text
- **Training Dataset:**
- all-nli
### 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({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'XLMRobertaModel'})
(1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'mean', '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("sentence_transformers_model_id")
# Run inference
sentences = [
'A woman is walking across the street eating a banana, while a man is following with his briefcase.',
'The woman and man are playing baseball together.',
'A blond man wearing a brown shirt is reading a book on a bench in the park',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9832, 0.9819],
# [0.9832, 1.0000, 0.9758],
# [0.9819, 0.9758, 1.0000]])
```
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `pair-score-evaluator-dev`
* Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:------------|
| pearson_cosine | -0.5093 |
| **spearman_cosine** | **-0.4962** |
## Training Details
### Training Dataset
#### all-nli
* Dataset: all-nli
* Size: 80 training samples
* Columns: sentence1, sentence2, and score
* Approximate statistics based on the first 80 samples:
| | sentence1 | sentence2 | score |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | - min: 10 tokens
- mean: 26.16 tokens
- max: 52 tokens
| - min: 5 tokens
- mean: 11.53 tokens
- max: 25 tokens
| - min: 0.0
- mean: 0.51
- max: 1.0
|
* Samples:
| sentence1 | sentence2 | score |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------|:-----------------|
| A woman wearing all white and eating, walks next to a man holding a briefcase. | A married couple is sleeping. | 0.0 |
| Two adults, one female in white, with shades and one male, gray clothes, walking across a street, away from a eatery with a blurred image of a dark colored red shirted person in the foreground. | Two friends cross a street. | 0.5 |
| A Little League team tries to catch a runner sliding into a base in an afternoon game. | A team is playing baseball on Saturn. | 0.0 |
* Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Evaluation Dataset
#### all-nli
* Dataset: all-nli
* Size: 20 evaluation samples
* Columns: sentence1, sentence2, and score
* Approximate statistics based on the first 20 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | - min: 13 tokens
- mean: 24.0 tokens
- max: 52 tokens
| - min: 7 tokens
- mean: 13.8 tokens
- max: 29 tokens
| - min: 0.0
- mean: 0.47
- max: 1.0
|
* Samples:
| sentence1 | sentence2 | score |
|:-------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------|:-----------------|
| A woman is walking across the street eating a banana, while a man is following with his briefcase. | A woman eats ice cream walking down the sidewalk, and there is another woman in front of her with a purse. | 0.0 |
| A couple playing with a little boy on the beach. | A couple watch a little girl play by herself on the beach. | 0.0 |
| Woman in white in foreground and a man slightly behind walking with a sign for John's Pizza and Gyro in the background. | They are walking with a sign. | 1.0 |
* Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `num_train_epochs`: 1
- `warmup_steps`: 0.05
- `use_cpu`: True
- `load_best_model_at_end`: True
- `dataloader_pin_memory`: False
- `push_to_hub`: True
- `gradient_checkpointing`: True
#### All Hyperparameters
Click to expand
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `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`: 0.05
- `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`: True
- `seed`: 42
- `data_seed`: None
- `bf16`: False
- `fp16`: False
- `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`: True
- `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`: False
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `push_to_hub`: True
- `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`: True
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}