---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:80
- loss:CoSENTLoss
base_model: abdeljalilELmajjodi/model
widget:
- source_sentence: A man with blond-hair, and a brown shirt drinking out of a public
water fountain.
sentences:
- A blond man wearing a brown shirt is reading a book on a bench in the park
- The people are standing still on the curb.
- An elderly man sits in a small shop.
- source_sentence: People waiting to get on a train or just getting off.
sentences:
- A team is trying to tag a runner out.
- A team is playing baseball on Saturn.
- There are people just getting on a train
- source_sentence: A person on a horse jumps over a broken down airplane.
sentences:
- A person is outdoors, on a horse.
- The adults are both male and female.
- The woman and man are outdoors.
- source_sentence: Woman in white in foreground and a man slightly behind walking
with a sign for John's Pizza and Gyro in the background.
sentences:
- A man and a soman are eating together at John's Pizza and Gyro.
- The woman is wearing black.
- There are no women in the picture.
- source_sentence: A boy is jumping on skateboard in the middle of a red bridge.
sentences:
- Two adults walking across a road
- Two people walk away from a restaurant across a street.
- The boy skates down the sidewalk.
datasets:
- sentence-transformers/all-nli
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: -0.21298993100944025
name: Pearson Cosine
- type: spearman_cosine
value: -0.1260295943285407
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](https://huggingface.co/datasets/sentence-transformers/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](https://huggingface.co/datasets/sentence-transformers/all-nli)
- **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({'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("aamohame/hack_ai_embbedding_model")
# Run inference
sentences = [
'A boy is jumping on skateboard in the middle of a red bridge.',
'The boy skates down the sidewalk.',
'Two adults walking across a road',
]
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, 0.9936, 0.9905],
# [0.9936, 1.0000, 0.9985],
# [0.9905, 0.9985, 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 | -0.213 |
| **spearman_cosine** | **-0.126** |
## Training Details
### Training Dataset
#### all-nli
* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* 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: 24.94 tokens
- max: 52 tokens
| - min: 5 tokens
- mean: 11.95 tokens
- max: 29 tokens
| - min: 0.0
- mean: 0.51
- max: 1.0
|
* Samples:
| sentence1 | sentence2 | score |
|:---------------------------------------------------------------------|:--------------------------------------------------------|:-----------------|
| A man, woman, and child enjoying themselves on a beach. | A family of three is at the mall shopping. | 0.0 |
| Two women, holding food carryout containers, hug. | Two women hug each other. | 1.0 |
| A couple play in the tide with their young son. | The family is outside. | 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"
}
```
### Evaluation Dataset
#### all-nli
* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* 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: 14 tokens
- mean: 28.9 tokens
- max: 52 tokens
| - min: 7 tokens
- mean: 12.1 tokens
- max: 21 tokens
| - min: 0.0
- mean: 0.5
- max: 1.0
|
* Samples:
| sentence1 | sentence2 | score |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------|:-----------------|
| 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 people walk away from a restaurant across a street. | 1.0 |
| Woman in white in foreground and a man slightly behind walking with a sign for John's Pizza and Gyro in the background. | A man and a soman are eating together at John's Pizza and Gyro. | 0.0 |
| A boy is jumping on skateboard in the middle of a red bridge. | The boy skates down the sidewalk. | 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"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `num_train_epochs`: 1
- `warmup_steps`: 0.05
- `bf16`: True
- `fp16_full_eval`: True
- `load_best_model_at_end`: True
- `push_to_hub`: True
- `hub_model_id`: aamohame/hack_ai_embbedding_model
- `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`: False
- `seed`: 42
- `data_seed`: None
- `bf16`: True
- `fp16`: False
- `bf16_full_eval`: False
- `fp16_full_eval`: True
- `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`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `push_to_hub`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: aamohame/hack_ai_embbedding_model
- `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`: {}