|
--- |
|
base_model: distilbert/distilroberta-base |
|
datasets: |
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- sentence-transformers/all-nli |
|
language: |
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- en |
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library_name: sentence-transformers |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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- pearson_manhattan |
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- 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:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: A man dressed in yellow rescue gear walks in a field. |
|
sentences: |
|
- A person messes with some papers. |
|
- The man is outdoors. |
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- The man is bowling. |
|
- source_sentence: A young woman tennis player dressed in black carries many tennis |
|
balls on her racket. |
|
sentences: |
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- A young woman tennis player have many tennis balls. |
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- Two men are fishing. |
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- A young woman never wears white dress. |
|
- source_sentence: An older gentleman enjoys a scenic stroll through the countryside. |
|
sentences: |
|
- A pirate boards the spaceship. |
|
- A man walks the countryside. |
|
- Girls standing at a whiteboard in front of class. |
|
- source_sentence: A kid in a red and black coat is laying on his back in the snow |
|
with his arm in the air and a red sled is next to him. |
|
sentences: |
|
- It is a cold day. |
|
- A girl with her hands in a tub. |
|
- The kid is on a sugar high. |
|
- source_sentence: A young boy playing in the grass. |
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sentences: |
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- A woman in a restaurant. |
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- The boy is in the sand. |
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- There is a child in the grass. |
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model-index: |
|
- name: SentenceTransformer based on distilbert/distilroberta-base |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts dev |
|
type: sts-dev |
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metrics: |
|
- type: pearson_cosine |
|
value: 0.7472631211742428 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.7816148643047378 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.7462864148382337 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.7562943126527191 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.7467258010434259 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.7551090266044774 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.4680868285301815 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.48375727668644103 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.7472631211742428 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.7816148643047378 |
|
name: Spearman Max |
|
- task: |
|
type: semantic-similarity |
|
name: Semantic Similarity |
|
dataset: |
|
name: sts test |
|
type: sts-test |
|
metrics: |
|
- type: pearson_cosine |
|
value: 0.7145148981321872 |
|
name: Pearson Cosine |
|
- type: spearman_cosine |
|
value: 0.7188984625066266 |
|
name: Spearman Cosine |
|
- type: pearson_manhattan |
|
value: 0.7140610465322241 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.7047733039592011 |
|
name: Spearman Manhattan |
|
- type: pearson_euclidean |
|
value: 0.7146251167393373 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
|
value: 0.7050375569985633 |
|
name: Spearman Euclidean |
|
- type: pearson_dot |
|
value: 0.4319747161819866 |
|
name: Pearson Dot |
|
- type: spearman_dot |
|
value: 0.429842682990914 |
|
name: Spearman Dot |
|
- type: pearson_max |
|
value: 0.7146251167393373 |
|
name: Pearson Max |
|
- type: spearman_max |
|
value: 0.7188984625066266 |
|
name: Spearman Max |
|
--- |
|
|
|
# SentenceTransformer based on distilbert/distilroberta-base |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) 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:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b --> |
|
- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
- **Training Dataset:** |
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- [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( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel |
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(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}) |
|
) |
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``` |
|
|
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## 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 |
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model = SentenceTransformer("manuel-couto-pintos/distilroberta-base-nli-v2") |
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# Run inference |
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sentences = [ |
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'A young boy playing in the grass.', |
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'There is a child in the grass.', |
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'The boy is in the sand.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
|
|
|
# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
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# [3, 3] |
|
``` |
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|
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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|
</details> |
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--> |
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|
|
<!-- |
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### Downstream Usage (Sentence Transformers) |
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|
|
You can finetune this model on your own dataset. |
|
|
|
<details><summary>Click to expand</summary> |
|
|
|
</details> |
|
--> |
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|
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<!-- |
|
### Out-of-Scope Use |
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|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
|
|
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## Evaluation |
|
|
|
### Metrics |
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|
|
#### Semantic Similarity |
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* Dataset: `sts-dev` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| pearson_cosine | 0.7473 | |
|
| **spearman_cosine** | **0.7816** | |
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| pearson_manhattan | 0.7463 | |
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| spearman_manhattan | 0.7563 | |
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| pearson_euclidean | 0.7467 | |
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| spearman_euclidean | 0.7551 | |
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| pearson_dot | 0.4681 | |
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| spearman_dot | 0.4838 | |
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| pearson_max | 0.7473 | |
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| spearman_max | 0.7816 | |
|
|
|
#### Semantic Similarity |
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* Dataset: `sts-test` |
|
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
|
| Metric | Value | |
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|:--------------------|:-----------| |
|
| pearson_cosine | 0.7145 | |
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| **spearman_cosine** | **0.7189** | |
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| pearson_manhattan | 0.7141 | |
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| spearman_manhattan | 0.7048 | |
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| pearson_euclidean | 0.7146 | |
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| spearman_euclidean | 0.705 | |
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| pearson_dot | 0.432 | |
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| spearman_dot | 0.4298 | |
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| pearson_max | 0.7146 | |
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| spearman_max | 0.7189 | |
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|
|
<!-- |
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## Bias, Risks and Limitations |
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*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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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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|
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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) |
|
* Size: 10,000 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: 10.38 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.8 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 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>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) 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) |
|
* 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: 18.02 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.81 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.37 tokens</li><li>max: 29 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>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
|
```json |
|
{ |
|
"scale": 20.0, |
|
"similarity_fct": "cos_sim" |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: steps |
|
- `per_device_train_batch_size`: 128 |
|
- `per_device_eval_batch_size`: 128 |
|
- `num_train_epochs`: 1 |
|
- `warmup_ratio`: 0.1 |
|
- `fp16`: 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`: 128 |
|
- `per_device_eval_batch_size`: 128 |
|
- `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`: 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 |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |
|
|:------:|:----:|:------:|:-----------------------:|:------------------------:| |
|
| 0 | 0 | - | 0.6375 | - | |
|
| 0.1266 | 10 | 2.9835 | 0.7807 | - | |
|
| 0.2532 | 20 | 1.7046 | 0.7782 | - | |
|
| 0.3797 | 30 | 1.6654 | 0.7847 | - | |
|
| 0.5063 | 40 | 1.7359 | 0.7900 | - | |
|
| 0.6329 | 50 | 1.6403 | 0.7864 | - | |
|
| 0.7595 | 60 | 1.7291 | 0.7820 | - | |
|
| 0.8861 | 70 | 1.7057 | 0.7816 | - | |
|
| 1.0 | 79 | - | - | 0.7189 | |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.10.13 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.42.1 |
|
- PyTorch: 2.0.1+cu117 |
|
- Accelerate: 0.34.0 |
|
- Datasets: 2.15.0 |
|
- 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", |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
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*Clearly define terms in order to be accessible across audiences.* |
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