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--- |
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base_model: sentence-transformers/all-mpnet-base-v2 |
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datasets: [] |
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language: [] |
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library_name: sentence-transformers |
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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:178829 |
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- loss:MultipleNegativesRankingLoss |
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widget: |
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- source_sentence: who was actor larry parks |
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sentences: |
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- American stage and movie actor.e eventually did so in tears, only to be blacklisted |
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anyway. |
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- 'A possum (plural form: possums) is any of about 70 small-to medium-sized arboreal |
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marsupial species native to Australia, New Guinea, and Sulawesi (and introduced |
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to New Zealand and China). The common brushtail possum was introduced to New Zealand |
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by European settlers in an attempt to establish a fur industry. There are no native |
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predators of the possum in New Zealand, so its numbers in New Zealand have risen |
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to the point where it is considered a serious pest.' |
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- A document used to change one or more minor provisions of a living trust or joint |
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living trust as an alternative to preparing a new living trust. |
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- source_sentence: what is the salary of a person with a biology degree |
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sentences: |
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- $10 to $25 per hour. |
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- $25,290 (2014-2015 academic year) |
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- Biology majors who don’t attend a graduate program make a median salary of $51,000 |
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per year, which is a little below the median salary for graduates from all other |
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majors combined. Don’t let that fact stop you from pursuing a degree in biology |
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if it’s what you’re passionate about, though. Career Options for Biology Majors. |
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Below is a list of common career options for biology majors. This isn’t a comprehensive |
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list, as students who major in biology go on to do many interesting things. However, |
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this list should give you an idea of the types of work that would be available |
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to you with a degree in biology. |
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- source_sentence: definition of pretext |
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sentences: |
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- Peanut butter is an excellent source of nutrition. Required to contain at least |
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90 percent peanuts, it includes more than 30 vitamins and minerals. Peanut butter |
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contains no cholesterol or trans fats, according to the National Peanut Board. |
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In fact, studies show that peanut butter may even improve your levels of good |
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cholesterol. |
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- Pretext generally refers to a reason for an action which is false, and offered |
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to cover up true motives or intentions. It is a concept sometimes brought up in |
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the context of employment discrimination. |
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- 20.5 degrees Celsius (68.8 degrees Fahrenheit). |
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- source_sentence: what is cyber spoofing |
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sentences: |
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- Once your question has been posted for at least 1 hour and has at least one answer, |
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click on 'Award Best Answer' button next to your chosen answer. 1 Upload failed. |
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2 Please upload a file larger than 100x100 pixels. 3 We are experiencing some |
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problems, please try again. |
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- Though some vegetable sources of protein contain sufficient values of all essential |
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amino acids, many are lower in one or more essential amino acids than animal sources, |
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especially lysine, and to a lesser extent methionine and threonine. 1 Proteins |
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derived from plant foods (legumes, seeds, grains, and vegetables) can be complete |
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as well (examples include chickpeas, black beans, pumpkin seeds, cashews, cauliflower, |
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quinoa, pistachios, turnip greens, black-eyed peas, and soy). 2 Most plant foods |
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tend to have less of one or more essential amino acid |
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- A spoofing attack is a situation in which one person or program successfully masquerades |
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as another by falsifying data and thereby gaining an illegitimate advantage. |
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- source_sentence: what type of reaction is iron plus oxygen |
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sentences: |
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- Pearl |
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- 'Yes' |
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- 'When a metal undergos a combination reaction with oxygen, a metal oxide is formed |
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(similarily, a metal halide is formed if reacted with one of the halogens). You |
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see the products of this type of reaction whenever you see rust. Rust is the product |
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of a combination reaction of iron and oxygen: ' |
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--- |
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# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). 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-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 84f2bcc00d77236f9e89c8a360a00fb1139bf47d --> |
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- **Maximum Sequence Length:** 384 tokens |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **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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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel |
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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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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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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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# Download from the 🤗 Hub |
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model = SentenceTransformer("DashReza7/all-mpnet-base-v2_FINETUNED") |
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# Run inference |
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sentences = [ |
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'what type of reaction is iron plus oxygen', |
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'When a metal undergos a combination reaction with oxygen, a metal oxide is formed (similarily, a metal halide is formed if reacted with one of the halogens). You see the products of this type of reaction whenever you see rust. Rust is the product of a combination reaction of iron and oxygen: ', |
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'Pearl', |
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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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# 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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### 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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### 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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### 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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## 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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### 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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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 178,829 training samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | |
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|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 5 tokens</li><li>mean: 9.37 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 60.48 tokens</li><li>max: 197 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | |
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|:------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>what is rba</code> | <code>Results-Based Accountability is a disciplined way of thinking and taking action that communities can use to improve the lives of children, youth, families, adults and the community as a whole.</code> | |
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| <code>what is rba</code> | <code>Results-Based Accountability® (also known as RBA) is a disciplined way of thinking and taking action that communities can use to improve the lives of children, youth, families, adults and the community as a whole. RBA is also used by organizations to improve the performance of their programs. Creating Community Impact with RBA. Community impact focuses on conditions of well-being for children, families and the community as a whole that a group of leaders is working collectively to improve. For example: “Residents with good jobs,” “Children ready for school,” or “A safe and clean neighborhood”.</code> | |
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| <code>was ronald reagan a democrat</code> | <code>Yes</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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- `per_device_train_batch_size`: 64 |
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- `per_device_eval_batch_size`: 64 |
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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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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 64 |
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- `per_device_eval_batch_size`: 64 |
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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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- `restore_callback_states_from_checkpoint`: 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, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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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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- `eval_do_concat_batches`: True |
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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 |
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- `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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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: proportional |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | |
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|:------:|:----:|:-------------:| |
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| 0.1789 | 500 | 0.279 | |
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| 0.3578 | 1000 | 0.2194 | |
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| 0.5367 | 1500 | 0.21 | |
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| 0.7156 | 2000 | 0.207 | |
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| 0.8945 | 2500 | 0.198 | |
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.42.4 |
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- PyTorch: 2.3.1+cu121 |
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- Accelerate: 0.32.1 |
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- Datasets: 2.21.0 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@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", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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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}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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