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---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:100K<n<1M
- loss:CachedMultipleNegativesRankingLoss
base_model: Unbabel/xlm-roberta-comet-small
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
widget:
- source_sentence: There's a dock
sentences:
- There is a door.
- the animal is running
- The woman is singing.
- source_sentence: The boy scowls
sentences:
- A boy is blowing bubbles.
- He is playing a song.
- They are driving cars.
- source_sentence: A bird flying.
sentences:
- A butterfly flys freely.
- A dog carries a bone.
- Two dogs are playing.
- source_sentence: A woman sings.
sentences:
- The woman is singing.
- The man is in a city.
- there is a man in a pool.
- source_sentence: a baby smiling
sentences:
- A baby is unhappy.
- The dog has big ears.
- They are driving cars.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on Unbabel/xlm-roberta-comet-small
results:
- task:
type: triplet
name: Triplet
dataset:
name: all nli dev
type: all-nli-dev
metrics:
- type: cosine_accuracy
value: 0.849
name: Cosine Accuracy
- type: dot_accuracy
value: 0.163
name: Dot Accuracy
- type: manhattan_accuracy
value: 0.837
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 0.841
name: Euclidean Accuracy
- type: max_accuracy
value: 0.849
name: Max Accuracy
- task:
type: triplet
name: Triplet
dataset:
name: all nli test
type: all-nli-test
metrics:
- type: cosine_accuracy
value: 0.839
name: Cosine Accuracy
- type: dot_accuracy
value: 0.15
name: Dot Accuracy
- type: manhattan_accuracy
value: 0.827
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 0.827
name: Euclidean Accuracy
- type: max_accuracy
value: 0.839
name: Max Accuracy
---
# SentenceTransformer based on Unbabel/xlm-roberta-comet-small
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Unbabel/xlm-roberta-comet-small](https://huggingface.co/Unbabel/xlm-roberta-comet-small) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 384-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:** [Unbabel/xlm-roberta-comet-small](https://huggingface.co/Unbabel/xlm-roberta-comet-small) <!-- at revision df568a015df5cefbf2f449314b61ce9afb0cb593 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 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: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 384, '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("mics-nlp/xlm-roberta-small-all-nli-triplet")
# Run inference
sentences = [
'a baby smiling',
'A baby is unhappy.',
'The dog has big ears.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Evaluation
### Metrics
#### Triplet
* Dataset: `all-nli-dev`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:-------------------|:----------|
| cosine_accuracy | 0.849 |
| dot_accuracy | 0.163 |
| manhattan_accuracy | 0.837 |
| euclidean_accuracy | 0.841 |
| **max_accuracy** | **0.849** |
#### Triplet
* Dataset: `all-nli-test`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:-------------------|:----------|
| cosine_accuracy | 0.839 |
| dot_accuracy | 0.15 |
| manhattan_accuracy | 0.827 |
| euclidean_accuracy | 0.827 |
| **max_accuracy** | **0.839** |
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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: 100,000 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 10.9 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.62 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.76 tokens</li><li>max: 55 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> |
| <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> |
| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### 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>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 20.31 tokens</li><li>max: 83 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.71 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.39 tokens</li><li>max: 32 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
| <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</code> |
| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code> |
| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>A man selling donuts to a customer.</code> | <code>A woman drinks her coffee in a small cafe.</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### 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
- `bf16`: True
- `batch_sampler`: no_duplicates
#### 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
- `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`: True
- `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, '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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | all-nli-dev_max_accuracy | all-nli-test_max_accuracy |
|:-----:|:----:|:-------------:|:------:|:------------------------:|:-------------------------:|
| 0 | 0 | - | - | 0.541 | - |
| 0.016 | 100 | 3.5308 | 3.1817 | 0.558 | - |
| 0.032 | 200 | 3.2784 | 3.0406 | 0.597 | - |
| 0.048 | 300 | 3.113 | 2.7572 | 0.635 | - |
| 0.064 | 400 | 2.8296 | 2.4646 | 0.68 | - |
| 0.08 | 500 | 2.631 | 2.3583 | 0.676 | - |
| 0.096 | 600 | 2.3247 | 2.1394 | 0.706 | - |
| 0.112 | 700 | 2.2211 | 2.0201 | 0.711 | - |
| 0.128 | 800 | 2.1263 | 1.9560 | 0.757 | - |
| 0.144 | 900 | 2.2105 | 1.9074 | 0.748 | - |
| 0.16 | 1000 | 2.0637 | 1.9289 | 0.728 | - |
| 0.176 | 1100 | 2.1772 | 1.8796 | 0.741 | - |
| 0.192 | 1200 | 2.1518 | 1.8346 | 0.761 | - |
| 0.208 | 1300 | 1.728 | 1.8213 | 0.765 | - |
| 0.224 | 1400 | 1.8101 | 1.6321 | 0.772 | - |
| 0.24 | 1500 | 1.7516 | 1.5669 | 0.793 | - |
| 0.256 | 1600 | 1.4988 | 1.5538 | 0.8 | - |
| 0.272 | 1700 | 1.6695 | 1.5462 | 0.803 | - |
| 0.288 | 1800 | 1.5971 | 1.5499 | 0.783 | - |
| 0.304 | 1900 | 1.5614 | 1.5047 | 0.788 | - |
| 0.32 | 2000 | 1.522 | 1.4957 | 0.794 | - |
| 0.336 | 2100 | 1.3624 | 1.4153 | 0.814 | - |
| 0.352 | 2200 | 1.4773 | 1.4169 | 0.809 | - |
| 0.368 | 2300 | 1.6066 | 1.3697 | 0.813 | - |
| 0.384 | 2400 | 1.5106 | 1.3203 | 0.819 | - |
| 0.4 | 2500 | 1.4783 | 1.3417 | 0.817 | - |
| 0.416 | 2600 | 1.3696 | 1.2650 | 0.824 | - |
| 0.432 | 2700 | 1.5115 | 1.2779 | 0.829 | - |
| 0.448 | 2800 | 1.4834 | 1.2668 | 0.834 | - |
| 0.464 | 2900 | 1.4823 | 1.2621 | 0.836 | - |
| 0.48 | 3000 | 1.4163 | 1.2465 | 0.837 | - |
| 0.496 | 3100 | 1.4232 | 1.2475 | 0.837 | - |
| 0.512 | 3200 | 1.2193 | 1.1975 | 0.838 | - |
| 0.528 | 3300 | 1.2569 | 1.1816 | 0.838 | - |
| 0.544 | 3400 | 1.2988 | 1.1936 | 0.839 | - |
| 0.56 | 3500 | 1.5068 | 1.2213 | 0.835 | - |
| 0.576 | 3600 | 1.3022 | 1.1799 | 0.842 | - |
| 0.592 | 3700 | 1.3823 | 1.1910 | 0.831 | - |
| 0.608 | 3800 | 1.4224 | 1.1786 | 0.834 | - |
| 0.624 | 3900 | 1.3765 | 1.1541 | 0.843 | - |
| 0.64 | 4000 | 1.4987 | 1.1365 | 0.844 | - |
| 0.656 | 4100 | 1.7525 | 1.1394 | 0.843 | - |
| 0.672 | 4200 | 1.6013 | 1.1178 | 0.841 | - |
| 0.688 | 4300 | 1.3326 | 1.0959 | 0.846 | - |
| 0.704 | 4400 | 1.355 | 1.0757 | 0.848 | - |
| 0.72 | 4500 | 1.2834 | 1.0681 | 0.846 | - |
| 0.736 | 4600 | 1.2939 | 1.0696 | 0.85 | - |
| 0.752 | 4700 | 1.4069 | 1.0645 | 0.848 | - |
| 0.768 | 4800 | 1.4503 | 1.0609 | 0.849 | - |
| 0.784 | 4900 | 1.2833 | 1.0587 | 0.847 | - |
| 0.8 | 5000 | 1.3321 | 1.0563 | 0.849 | - |
| 0.816 | 5100 | 1.3006 | 1.0539 | 0.847 | - |
| 0.832 | 5200 | 1.4332 | 1.0527 | 0.847 | - |
| 0.848 | 5300 | 1.3101 | 1.0505 | 0.848 | - |
| 0.864 | 5400 | 1.3658 | 1.0523 | 0.849 | - |
| 0.88 | 5500 | 1.353 | 1.0520 | 0.849 | - |
| 0.896 | 5600 | 1.2429 | 1.0521 | 0.848 | - |
| 0.912 | 5700 | 1.3512 | 1.0505 | 0.848 | - |
| 0.928 | 5800 | 1.2995 | 1.0501 | 0.848 | - |
| 0.944 | 5900 | 1.3514 | 1.0491 | 0.849 | - |
| 0.96 | 6000 | 1.3976 | 1.0490 | 0.848 | - |
| 0.976 | 6100 | 1.2112 | 1.0487 | 0.848 | - |
| 0.992 | 6200 | 0.0033 | 1.0492 | 0.849 | - |
| 1.0 | 6250 | - | - | - | 0.839 |
### Framework Versions
- Python: 3.9.10
- Sentence Transformers: 3.0.0
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.26.1
- Datasets: 2.16.1
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```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",
}
```
#### CachedMultipleNegativesRankingLoss
```bibtex
@misc{gao2021scaling,
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
year={2021},
eprint={2101.06983},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
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