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---
base_model: intfloat/e5-large-v2
datasets:
- sentence-transformers/all-nli
language:
- en
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10000
- loss:SoftmaxLoss
widget:
- source_sentence: A man selling donuts to a customer during a world exhibition event
held in the city of Angeles
sentences:
- The man is doing tricks.
- A woman drinks her coffee in a small cafe.
- The building is made of logs.
- source_sentence: A group of people prepare hot air balloons for takeoff.
sentences:
- There are hot air balloons on the ground and air.
- A man is in an art museum.
- People watch another person do a trick.
- source_sentence: Three workers are trimming down trees.
sentences:
- The goalie is sleeping at home.
- There are three workers
- The girl has brown hair.
- source_sentence: Two brown-haired men wearing short-sleeved shirts and shorts are
climbing stairs.
sentences:
- The men have blonde hair.
- A bicyclist passes an esthetically beautiful building on a sunny day
- Two men are dancing.
- source_sentence: A man is sitting in on the side of the street with brass pots.
sentences:
- a younger boy looks at his father
- Children are at the beach.
- a man does not have brass pots
model-index:
- name: SentenceTransformer based on intfloat/e5-large-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.25153764364319275
name: Pearson Cosine
- type: spearman_cosine
value: 0.3291921844406249
name: Spearman Cosine
- type: pearson_manhattan
value: 0.2966881773862295
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.32789142408327193
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.29957914563527244
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.3291921844406249
name: Spearman Euclidean
- type: pearson_dot
value: 0.2515376443724997
name: Pearson Dot
- type: spearman_dot
value: 0.3291921844406249
name: Spearman Dot
- type: pearson_max
value: 0.29957914563527244
name: Pearson Max
- type: spearman_max
value: 0.3291921844406249
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.27914347241714155
name: Pearson Cosine
- type: spearman_cosine
value: 0.30504478158921217
name: Spearman Cosine
- type: pearson_manhattan
value: 0.3034422953603654
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.30482947439377617
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.30503064655519824
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.30504478158921217
name: Spearman Euclidean
- type: pearson_dot
value: 0.2791434684526028
name: Pearson Dot
- type: spearman_dot
value: 0.30504478158921217
name: Spearman Dot
- type: pearson_max
value: 0.30503064655519824
name: Pearson Max
- type: spearman_max
value: 0.30504478158921217
name: Spearman Max
---
# SentenceTransformer based on intfloat/e5-large-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/e5-large-v2](https://huggingface.co./intfloat/e5-large-v2) on the [sentence-transformers/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 semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [intfloat/e5-large-v2](https://huggingface.co./intfloat/e5-large-v2) <!-- at revision b322e09026e4ea05f42beadf4d661fb4e101d311 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [sentence-transformers/all-nli](https://huggingface.co./datasets/sentence-transformers/all-nli)
- **Language:** en
<!-- - **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: BertModel
(1): Pooling({'word_embedding_dimension': 1024, '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})
(2): Normalize()
)
```
## 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("hongming/e5-large-v2-nli-v1")
# Run inference
sentences = [
'A man is sitting in on the side of the street with brass pots.',
'a man does not have brass pots',
'Children are at the beach.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.2515 |
| **spearman_cosine** | **0.3292** |
| pearson_manhattan | 0.2967 |
| spearman_manhattan | 0.3279 |
| pearson_euclidean | 0.2996 |
| spearman_euclidean | 0.3292 |
| pearson_dot | 0.2515 |
| spearman_dot | 0.3292 |
| pearson_max | 0.2996 |
| spearman_max | 0.3292 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.2791 |
| **spearman_cosine** | **0.305** |
| pearson_manhattan | 0.3034 |
| spearman_manhattan | 0.3048 |
| pearson_euclidean | 0.305 |
| spearman_euclidean | 0.305 |
| pearson_dot | 0.2791 |
| spearman_dot | 0.305 |
| pearson_max | 0.305 |
| spearman_max | 0.305 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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## Training Details
### Training Dataset
#### sentence-transformers/all-nli
* Dataset: [sentence-transformers/all-nli](https://huggingface.co./datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co./datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 10,000 training samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | premise | hypothesis | label |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 17.38 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.7 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>0: ~33.40%</li><li>1: ~33.30%</li><li>2: ~33.30%</li></ul> |
* Samples:
| premise | hypothesis | label |
|:--------------------------------------------------------------------|:---------------------------------------------------------------|:---------------|
| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is training his horse for a competition.</code> | <code>1</code> |
| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is at a diner, ordering an omelette.</code> | <code>2</code> |
| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
### Evaluation Dataset
#### sentence-transformers/all-nli
* Dataset: [sentence-transformers/all-nli](https://huggingface.co./datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co./datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 1,000 evaluation samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | premise | hypothesis | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 18.44 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.57 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>0: ~33.10%</li><li>1: ~33.30%</li><li>2: ~33.60%</li></ul> |
* Samples:
| premise | hypothesis | label |
|:-------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------|
| <code>Two women are embracing while holding to go packages.</code> | <code>The sisters are hugging goodbye while holding to go packages after just eating lunch.</code> | <code>1</code> |
| <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>0</code> |
| <code>Two women are embracing while holding to go packages.</code> | <code>The men are fighting outside a deli.</code> | <code>2</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
### 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_ratio`: 0.1
- `fp16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `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`: {}
- `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
- `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`: True
- `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
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:-----:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:|
| 0 | 0 | - | - | 0.8888 | - |
| 0.16 | 100 | 1.0934 | 1.0656 | 0.5733 | - |
| 0.32 | 200 | 1.0461 | 1.0245 | 0.3466 | - |
| 0.48 | 300 | 1.037 | 1.0152 | 0.3391 | - |
| 0.64 | 400 | 1.0013 | 0.9931 | 0.3333 | - |
| 0.8 | 500 | 1.0014 | 0.9871 | 0.3825 | - |
| 0.96 | 600 | 0.9827 | 0.9705 | 0.3292 | - |
| 1.0 | 625 | - | - | - | 0.3050 |
### Framework Versions
- Python: 3.8.13
- Sentence Transformers: 3.1.0.dev0
- Transformers: 4.43.3
- PyTorch: 2.1.2
- Accelerate: 0.33.0
- Datasets: 2.16.1
- 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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