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---
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language:
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- en
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license: apache-2.0
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library_name: sentence-transformers
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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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- dataset_size:100K<n<1M
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- loss:MultipleNegativesRankingLoss
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base_model: microsoft/mpnet-base
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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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widget:
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- source_sentence: The truth?
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sentences:
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- Is that true?
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- Some kids are napping.
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- A dog is taking a nap.
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- source_sentence: Just a bike
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sentences:
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- A child is riding a bike.
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- A man is wearing white.
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- A man is sleeping.
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- source_sentence: girl sleeps
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sentences:
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- A girl sleeps
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- That doesn't seem fair.
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- A man is running a race
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- source_sentence: Double pig.
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sentences:
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- The pig tripled.
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- I hated talking to you.
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- a woman is sleeping
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- source_sentence: a dog sleeps
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sentences:
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- a dog sleep under a tree.
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- Tommy didn't know, who.
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- A man is on a canoe.
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pipeline_tag: sentence-similarity
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model-index:
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- name: MPNet base trained on AllNLI triplets
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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: 0.917831105710814
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name: Cosine Accuracy
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- type: dot_accuracy
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value: 0.07867557715674361
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name: Dot Accuracy
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- type: manhattan_accuracy
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value: 0.9138821385176185
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name: Manhattan Accuracy
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- type: euclidean_accuracy
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value: 0.9147934386391251
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name: Euclidean Accuracy
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- type: max_accuracy
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value: 0.917831105710814
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name: Max Accuracy
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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 test
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type: all-nli-test
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metrics:
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- type: cosine_accuracy
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value: 0.9276743834165532
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name: Cosine Accuracy
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- type: dot_accuracy
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value: 0.06733242548040551
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name: Dot Accuracy
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- type: manhattan_accuracy
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value: 0.9255560599182933
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name: Manhattan Accuracy
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- type: euclidean_accuracy
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value: 0.9234377364200332
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name: Euclidean Accuracy
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- type: max_accuracy
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value: 0.9276743834165532
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name: Max Accuracy
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---
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# MPNet base trained on AllNLI triplets
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-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.
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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:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
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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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- [sentence-transformers/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
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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': 512, '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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)
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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("ayberkuckun/mpnet-base-all-nli-triplet")
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# Run inference
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sentences = [
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'a dog sleeps',
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'a dog sleep under a tree.',
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"Tommy didn't know, who.",
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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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<!--
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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.
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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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## Evaluation
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### Metrics
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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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| Metric | Value |
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|:-------------------|:-----------|
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| cosine_accuracy | 0.9178 |
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| dot_accuracy | 0.0787 |
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| manhattan_accuracy | 0.9139 |
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| euclidean_accuracy | 0.9148 |
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| **max_accuracy** | **0.9178** |
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#### Triplet
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* Dataset: `all-nli-test`
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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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| Metric | Value |
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|:-------------------|:-----------|
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| cosine_accuracy | 0.9277 |
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| dot_accuracy | 0.0673 |
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| manhattan_accuracy | 0.9256 |
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| euclidean_accuracy | 0.9234 |
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| **max_accuracy** | **0.9277** |
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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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## Training Details
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### Training Dataset
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#### sentence-transformers/all-nli
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* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
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* Size: 100,000 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: 10.46 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.81 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 5 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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### Evaluation Dataset
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#### sentence-transformers/all-nli
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* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
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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:
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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: 6 tokens</li><li>mean: 17.95 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.78 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.35 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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- `eval_strategy`: steps
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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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#### 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`: steps
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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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- `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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- `batch_sampler`: no_duplicates
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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 | loss | all-nli-dev_max_accuracy | all-nli-test_max_accuracy |
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|:-----:|:----:|:-------------:|:------:|:------------------------:|:-------------------------:|
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| 0 | 0 | - | - | 0.6832 | - |
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| 0.016 | 100 | 3.1461 | 1.6989 | 0.7708 | - |
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| 0.032 | 200 | 1.3308 | 0.9213 | 0.8010 | - |
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| 0.048 | 300 | 1.4333 | 0.8036 | 0.8117 | - |
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| 0.064 | 400 | 0.8862 | 0.7591 | 0.8132 | - |
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| 0.08 | 500 | 0.8292 | 0.8372 | 0.8045 | - |
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| 0.096 | 600 | 1.0852 | 0.8512 | 0.8018 | - |
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| 0.112 | 700 | 0.9157 | 0.8736 | 0.8118 | - |
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| 0.128 | 800 | 1.0996 | 0.9799 | 0.7924 | - |
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| 0.144 | 900 | 1.1212 | 0.9036 | 0.8171 | - |
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| 0.16 | 1000 | 1.0296 | 0.8890 | 0.7922 | - |
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| 0.176 | 1100 | 1.1005 | 1.0113 | 0.7922 | - |
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| 0.192 | 1200 | 1.03 | 0.8993 | 0.8068 | - |
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| 0.208 | 1300 | 0.824 | 0.8918 | 0.7966 | - |
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| 0.224 | 1400 | 0.7829 | 0.8369 | 0.8070 | - |
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| 0.24 | 1500 | 0.8878 | 0.7897 | 0.8098 | - |
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| 0.256 | 1600 | 0.7346 | 0.8386 | 0.8127 | - |
|
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| 0.272 | 1700 | 0.892 | 0.9013 | 0.8092 | - |
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| 0.288 | 1800 | 0.8553 | 0.8347 | 0.8130 | - |
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| 0.304 | 1900 | 0.8208 | 0.8359 | 0.8150 | - |
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| 0.32 | 2000 | 0.737 | 0.7469 | 0.8636 | - |
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| 0.336 | 2100 | 0.6301 | 0.7850 | 0.8442 | - |
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| 0.352 | 2200 | 0.662 | 0.6924 | 0.8648 | - |
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| 0.368 | 2300 | 0.8195 | 0.7686 | 0.8509 | - |
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| 0.384 | 2400 | 0.7525 | 0.7049 | 0.8603 | - |
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| 0.4 | 2500 | 0.6834 | 0.7109 | 0.8618 | - |
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| 0.416 | 2600 | 0.5977 | 0.6715 | 0.8589 | - |
|
|
| 0.432 | 2700 | 0.8432 | 0.7482 | 0.8597 | - |
|
|
| 0.448 | 2800 | 0.8676 | 0.6765 | 0.8575 | - |
|
|
| 0.464 | 2900 | 0.8342 | 0.6336 | 0.8773 | - |
|
|
| 0.48 | 3000 | 0.7155 | 0.6320 | 0.8789 | - |
|
|
| 0.496 | 3100 | 0.762 | 0.6094 | 0.8697 | - |
|
|
| 0.512 | 3200 | 0.5909 | 0.5915 | 0.8748 | - |
|
|
| 0.528 | 3300 | 0.5679 | 0.5382 | 0.8881 | - |
|
|
| 0.544 | 3400 | 0.5163 | 0.5617 | 0.8891 | - |
|
|
| 0.56 | 3500 | 0.5164 | 0.5627 | 0.8960 | - |
|
|
| 0.576 | 3600 | 0.519 | 0.5236 | 0.8917 | - |
|
|
| 0.592 | 3700 | 0.5327 | 0.5305 | 0.8998 | - |
|
|
| 0.608 | 3800 | 0.4958 | 0.5071 | 0.8990 | - |
|
|
| 0.624 | 3900 | 0.503 | 0.5242 | 0.8919 | - |
|
|
| 0.64 | 4000 | 0.7307 | 0.5176 | 0.9033 | - |
|
|
| 0.656 | 4100 | 0.9127 | 0.5168 | 0.9039 | - |
|
|
| 0.672 | 4200 | 0.8677 | 0.4683 | 0.9102 | - |
|
|
| 0.688 | 4300 | 0.6641 | 0.4549 | 0.9083 | - |
|
|
| 0.704 | 4400 | 0.586 | 0.4447 | 0.9092 | - |
|
|
| 0.72 | 4500 | 0.5447 | 0.4516 | 0.9084 | - |
|
|
| 0.736 | 4600 | 0.5895 | 0.4432 | 0.9104 | - |
|
|
| 0.752 | 4700 | 0.643 | 0.4479 | 0.9089 | - |
|
|
| 0.768 | 4800 | 0.6011 | 0.4310 | 0.9110 | - |
|
|
| 0.784 | 4900 | 0.5494 | 0.4417 | 0.9048 | - |
|
|
| 0.8 | 5000 | 0.6382 | 0.4628 | 0.9102 | - |
|
|
| 0.816 | 5100 | 0.5265 | 0.4355 | 0.9137 | - |
|
|
| 0.832 | 5200 | 0.5791 | 0.4165 | 0.9111 | - |
|
|
| 0.848 | 5300 | 0.5133 | 0.4276 | 0.9137 | - |
|
|
| 0.864 | 5400 | 0.634 | 0.4434 | 0.9083 | - |
|
|
| 0.88 | 5500 | 0.5405 | 0.4266 | 0.9086 | - |
|
|
| 0.896 | 5600 | 0.5374 | 0.4239 | 0.9102 | - |
|
|
| 0.912 | 5700 | 0.5969 | 0.4134 | 0.9137 | - |
|
|
| 0.928 | 5800 | 0.5549 | 0.4029 | 0.9159 | - |
|
|
| 0.944 | 5900 | 0.6575 | 0.4032 | 0.9165 | - |
|
|
| 0.96 | 6000 | 0.756 | 0.4116 | 0.9172 | - |
|
|
| 0.976 | 6100 | 0.6343 | 0.4069 | 0.9177 | - |
|
|
| 0.992 | 6200 | 0.0003 | 0.4065 | 0.9178 | - |
|
|
| 1.0 | 6250 | - | - | - | 0.9277 |
|
|
|
|
|
|
### Framework Versions
|
|
- Python: 3.11.7
|
|
- Sentence Transformers: 3.0.0
|
|
- Transformers: 4.41.2
|
|
- PyTorch: 2.1.2+cu121
|
|
- Accelerate: 0.30.1
|
|
- Datasets: 2.19.2
|
|
- Tokenizers: 0.19.1
|
|
|
|
## Citation
|
|
|
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### BibTeX
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|
|
|
#### 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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