|
--- |
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base_model: sentence-transformers/all-distilroberta-v1 |
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datasets: |
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- sentence-transformers/all-nli |
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language: |
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- en |
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library_name: sentence-transformers |
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license: apache-2.0 |
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metrics: |
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- cosine_accuracy |
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- dot_accuracy |
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- manhattan_accuracy |
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- euclidean_accuracy |
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- max_accuracy |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:557850 |
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- loss:MultipleNegativesRankingLoss |
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widget: |
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- source_sentence: A man is jumping unto his filthy bed. |
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sentences: |
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- A young male is looking at a newspaper while 2 females walks past him. |
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- The bed is dirty. |
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- The man is on the moon. |
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- source_sentence: A carefully balanced male stands on one foot near a clean ocean |
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beach area. |
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sentences: |
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- A man is ouside near the beach. |
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- Three policemen patrol the streets on bikes |
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- A man is sitting on his couch. |
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- source_sentence: The man is wearing a blue shirt. |
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sentences: |
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- Near the trashcan the man stood and smoked |
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- A man in a blue shirt leans on a wall beside a road with a blue van and red car |
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with water in the background. |
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- A man in a black shirt is playing a guitar. |
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- source_sentence: The girls are outdoors. |
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sentences: |
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- Two girls riding on an amusement part ride. |
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- a guy laughs while doing laundry |
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- Three girls are standing together in a room, one is listening, one is writing |
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on a wall and the third is talking to them. |
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- source_sentence: A construction worker peeking out of a manhole while his coworker |
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sits on the sidewalk smiling. |
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sentences: |
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- A worker is looking out of a manhole. |
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- A man is giving a presentation. |
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- The workers are both inside the manhole. |
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model-index: |
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- name: test |
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results: |
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- task: |
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type: triplet |
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name: Triplet |
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dataset: |
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name: all nli dev |
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type: all-nli-dev |
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metrics: |
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- type: cosine_accuracy |
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value: 0.07790262172284644 |
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name: Cosine Accuracy |
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- type: dot_accuracy |
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value: 0.9220973782771535 |
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name: Dot Accuracy |
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- type: manhattan_accuracy |
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value: 0.078330658105939 |
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name: Manhattan Accuracy |
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- type: euclidean_accuracy |
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value: 0.07790262172284644 |
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name: Euclidean Accuracy |
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- type: max_accuracy |
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value: 0.078330658105939 |
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name: Max Accuracy |
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- type: cosine_accuracy |
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value: 0.09212121212121212 |
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name: Cosine Accuracy |
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- type: dot_accuracy |
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value: 0.9078787878787878 |
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name: Dot Accuracy |
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- type: manhattan_accuracy |
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value: 0.09696969696969697 |
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name: Manhattan Accuracy |
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- type: euclidean_accuracy |
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value: 0.09212121212121212 |
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name: Euclidean Accuracy |
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- type: max_accuracy |
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value: 0.09696969696969697 |
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name: Max Accuracy |
|
--- |
|
|
|
# test |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-distilroberta-v1](https://huggingface.co/sentence-transformers/all-distilroberta-v1) on the [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. |
|
|
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## Model Details |
|
|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [sentence-transformers/all-distilroberta-v1](https://huggingface.co/sentence-transformers/all-distilroberta-v1) <!-- at revision 14e2a3f3090548596d23882845152a951333c0e3 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) |
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- **Language:** en |
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- **License:** apache-2.0 |
|
|
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### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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|
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### 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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(2): Normalize() |
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) |
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``` |
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|
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## Usage |
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|
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### Direct Usage (Sentence Transformers) |
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|
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First install the Sentence Transformers library: |
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|
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```bash |
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pip install -U sentence-transformers |
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``` |
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|
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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|
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# Download from the 🤗 Hub |
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model = SentenceTransformer("Xavarary/mpnet-base-all-medium-triplet") |
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# Run inference |
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sentences = [ |
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'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.', |
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'A worker is looking out of a manhole.', |
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'The workers are both inside the manhole.', |
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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] |
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|
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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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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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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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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|
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## Evaluation |
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|
|
### Metrics |
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|
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#### Triplet |
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* Dataset: `all-nli-dev` |
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
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|
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| Metric | Value | |
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|:-------------------|:-----------| |
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| cosine_accuracy | 0.0779 | |
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| dot_accuracy | 0.9221 | |
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| manhattan_accuracy | 0.0783 | |
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| euclidean_accuracy | 0.0779 | |
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| **max_accuracy** | **0.0783** | |
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|
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#### Triplet |
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* Dataset: `all-nli-dev` |
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
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|
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| Metric | Value | |
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|:-------------------|:----------| |
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| cosine_accuracy | 0.0921 | |
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| dot_accuracy | 0.9079 | |
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| manhattan_accuracy | 0.097 | |
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| euclidean_accuracy | 0.0921 | |
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| **max_accuracy** | **0.097** | |
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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 |
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|
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### Training Dataset |
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|
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#### all-nli |
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|
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* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) |
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* Size: 557,850 training samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | negative | |
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|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| 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> | |
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* Samples: |
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| anchor | positive | negative | |
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|:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------| |
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| <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> | |
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| <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> | |
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| <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> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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|
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### Evaluation Dataset |
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|
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#### all-nli |
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|
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* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) |
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* Size: 6,584 evaluation samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | anchor | positive | negative | |
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|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| 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> | |
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* Samples: |
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| anchor | positive | negative | |
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|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------| |
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| <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> | |
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| <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> | |
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| <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> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
|
|
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 1 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
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|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 1 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | all-nli-dev_max_accuracy | |
|
|:-----:|:----:|:-------------:|:------------------------:| |
|
| 0 | 0 | - | 0.0783 | |
|
| 0.016 | 100 | 0.9326 | - | |
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| 0.032 | 200 | 0.7562 | - | |
|
| 0.048 | 300 | 1.0227 | - | |
|
| 0.064 | 400 | 0.6815 | - | |
|
| 0.08 | 500 | 0.7091 | - | |
|
| 0.096 | 600 | 0.8731 | - | |
|
| 0.112 | 700 | 0.8263 | - | |
|
| 0.128 | 800 | 0.9691 | - | |
|
| 0.144 | 900 | 0.9814 | - | |
|
| 0.16 | 1000 | 0.8569 | - | |
|
| 0.176 | 1100 | 0.9649 | - | |
|
| 0.192 | 1200 | 0.8079 | - | |
|
| 0.208 | 1300 | 0.6868 | - | |
|
| 0.224 | 1400 | 0.6749 | - | |
|
| 0.24 | 1500 | 0.6968 | - | |
|
| 0.256 | 1600 | 0.5537 | - | |
|
| 0.272 | 1700 | 0.7242 | - | |
|
| 0.288 | 1800 | 0.7363 | - | |
|
| 0.304 | 1900 | 0.5771 | - | |
|
| 0.32 | 2000 | 0.5519 | - | |
|
| 0.336 | 2100 | 0.4775 | - | |
|
| 0.352 | 2200 | 0.4376 | - | |
|
| 0.368 | 2300 | 0.6341 | - | |
|
| 0.384 | 2400 | 0.5207 | - | |
|
| 0.4 | 2500 | 0.5106 | - | |
|
| 0.416 | 2600 | 0.4666 | - | |
|
| 0.432 | 2700 | 0.8047 | - | |
|
| 0.448 | 2800 | 0.6638 | - | |
|
| 0.464 | 2900 | 0.6554 | - | |
|
| 0.48 | 3000 | 0.6055 | - | |
|
| 0.496 | 3100 | 0.5947 | - | |
|
| 0.512 | 3200 | 0.4352 | - | |
|
| 0.528 | 3300 | 0.4421 | - | |
|
| 0.544 | 3400 | 0.4187 | - | |
|
| 0.56 | 3500 | 0.4056 | - | |
|
| 0.576 | 3600 | 0.4046 | - | |
|
| 0.592 | 3700 | 0.3629 | - | |
|
| 0.608 | 3800 | 0.3428 | - | |
|
| 0.624 | 3900 | 0.362 | - | |
|
| 0.64 | 4000 | 0.5858 | - | |
|
| 0.656 | 4100 | 0.7457 | - | |
|
| 0.672 | 4200 | 0.7033 | - | |
|
| 0.688 | 4300 | 0.5343 | - | |
|
| 0.704 | 4400 | 0.4125 | - | |
|
| 0.72 | 4500 | 0.4567 | - | |
|
| 0.736 | 4600 | 0.4921 | - | |
|
| 0.752 | 4700 | 0.5264 | - | |
|
| 0.768 | 4800 | 0.4883 | - | |
|
| 0.784 | 4900 | 0.4231 | - | |
|
| 0.8 | 5000 | 0.5048 | - | |
|
| 0.816 | 5100 | 0.4044 | - | |
|
| 0.832 | 5200 | 0.5102 | - | |
|
| 0.848 | 5300 | 0.3751 | - | |
|
| 0.864 | 5400 | 0.5139 | - | |
|
| 0.88 | 5500 | 0.4439 | - | |
|
| 0.896 | 5600 | 0.3999 | - | |
|
| 0.912 | 5700 | 0.4932 | - | |
|
| 0.928 | 5800 | 0.4349 | - | |
|
| 0.944 | 5900 | 0.6022 | - | |
|
| 0.96 | 6000 | 0.5906 | - | |
|
| 0.976 | 6100 | 0.5021 | - | |
|
| 0.992 | 6200 | 0.0002 | - | |
|
| 1.0 | 6250 | - | 0.0970 | |
|
|
|
|
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### Framework Versions |
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- Python: 3.11.5 |
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- Sentence Transformers: 3.1.1 |
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- Transformers: 4.38.0 |
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- PyTorch: 2.2.2+cu121 |
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- Accelerate: 0.33.0 |
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- Datasets: 2.21.0 |
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- Tokenizers: 0.15.2 |
|
|
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## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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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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} |
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``` |
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|
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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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}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
|
``` |
|
|
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