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--- |
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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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- dense |
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- generated_from_trainer |
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- dataset_size:262023 |
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- loss:MultipleNegativesRankingLoss |
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base_model: intfloat/e5-base-v2 |
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widget: |
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- source_sentence: 'query: what happened at reign of hoshea' |
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sentences: |
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- 'passage: He did evil in the eyes of the Lord, but not like the kings of Israel |
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who preceded him.' |
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- 'passage: After David had finished talking with Saul, Jonathan became one in spirit |
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with David, and he loved him as himself.' |
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- 'passage: Those who trusted in Cush and boasted in Egypt will be dismayed and |
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put to shame.' |
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- source_sentence: 'query: who was God' |
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sentences: |
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- 'passage: For the pagan world runs after all such things, and your Father knows |
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that you need them.' |
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- 'passage: Then Saul prayed to the Lord, the God of Israel, “Why have you not answered |
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your servant today? If the fault is in me or my son Jonathan, respond with Urim, |
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but if the men of Israel are at fault, respond with Thummim.” Jonathan and Saul |
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were taken by lot, and the men were cleared.' |
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- 'passage: But what did you go out to see? A prophet? Yes, I tell you, and more |
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than a prophet.' |
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- source_sentence: 'query: story of holy week' |
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sentences: |
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- "passage: “Zebulun will live by the seashore\n and become a haven for ships;\n\ |
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\ his border will extend toward Sidon." |
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- 'passage: “When you see Jerusalem being surrounded by armies, you will know that |
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its desolation is near.' |
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- 'passage: Rise! Let us go! Here comes my betrayer!”' |
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- source_sentence: 'query: Tabernacle Built in the Bible' |
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sentences: |
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- 'passage: Just before dawn Paul urged them all to eat. “For the last fourteen |
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days,” he said, “you have been in constant suspense and have gone without food—you |
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haven’t eaten anything.' |
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- 'passage: When he would not be dissuaded, we gave up and said, “The Lord’s will |
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be done.”' |
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- 'passage: The poles are to be inserted into the rings so they will be on two sides |
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of the altar when it is carried.' |
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- source_sentence: 'query: what happened to Jesus' |
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sentences: |
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- "passage: Like a slave longing for the evening shadows,\n or a hired laborer\ |
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\ waiting to be paid," |
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- 'passage: Then three thousand men from Judah went down to the cave in the rock |
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of Etam and said to Samson, “Don’t you realize that the Philistines are rulers |
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over us? What have you done to us?” |
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He answered, “I merely did to them what they did to me.”' |
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- 'passage: When Jesus saw her weeping, and the Jews who had come along with her |
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also weeping, he was deeply moved in spirit and troubled.' |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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--- |
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# SentenceTransformer based on intfloat/e5-base-v2 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-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:** [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) <!-- at revision f52bf8ec8c7124536f0efb74aca902b2995e5bcd --> |
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- **Maximum Sequence Length:** 256 tokens |
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- **Output Dimensionality:** 768 dimensions |
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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/huggingface/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': 256, 'do_lower_case': False, 'architecture': 'BertModel'}) |
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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("sentence_transformers_model_id") |
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# Run inference |
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sentences = [ |
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'query: what happened to Jesus', |
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'passage: When Jesus saw her weeping, and the Jews who had come along with her also weeping, he was deeply moved in spirit and troubled.', |
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'passage: Like a slave longing for the evening shadows,\n or a hired laborer waiting to be paid,', |
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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) |
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# tensor([[1.0000, 0.7344, 0.5219], |
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# [0.7344, 1.0000, 0.5503], |
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# [0.5219, 0.5503, 1.0000]]) |
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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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<!-- |
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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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#### Unnamed Dataset |
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* Size: 262,023 training samples |
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* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | label | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------| |
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| type | string | string | float | |
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| details | <ul><li>min: 5 tokens</li><li>mean: 28.73 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 35.28 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | label | |
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|:------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| |
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| <code>query: Do not lurk like a thief near the house of the righteous,<br> do not plunder their dwelling place;</code> | <code>passage: for though the righteous fall seven times, they rise again,<br> but the wicked stumble when calamity strikes.</code> | <code>1.0</code> | |
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| <code>query: what is Bolster</code> | <code>passage: When he reached a certain place, he stopped for the night because the sun had set. Taking one of the stones there, he put it under his head and lay down to sleep.</code> | <code>1.0</code> | |
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| <code>query: The event 'Gamaliel advises the counsel and Apostles freed' as recorded in Scripture, involving Gamaliel.</code> | <code>passage: Day after day, in the temple courts and from house to house, they never stopped teaching and proclaiming the good news that Jesus is the Messiah.</code> | <code>1.0</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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"gather_across_devices": false |
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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`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `num_train_epochs`: 1 |
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- `max_steps`: 50 |
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- `multi_dataset_batch_sampler`: round_robin |
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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`: 32 |
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- `per_device_eval_batch_size`: 32 |
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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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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-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 |
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- `num_train_epochs`: 1 |
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- `max_steps`: 50 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: None |
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- `warmup_ratio`: 0.0 |
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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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- `bf16`: False |
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- `fp16`: False |
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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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- `parallelism_config`: None |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch_fused |
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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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- `project`: huggingface |
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- `trackio_space_id`: trackio |
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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`: None |
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- `hub_always_push`: False |
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- `hub_revision`: None |
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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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- `include_for_metrics`: [] |
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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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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: no |
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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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- `use_liger_kernel`: False |
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- `liger_kernel_config`: None |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: True |
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- `prompts`: None |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: round_robin |
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- `router_mapping`: {} |
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- `learning_rate_mapping`: {} |
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</details> |
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### Framework Versions |
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- Python: 3.11.14 |
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- Sentence Transformers: 5.2.0 |
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- Transformers: 4.57.6 |
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- PyTorch: 2.10.0+cpu |
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- Accelerate: 1.12.0 |
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- Datasets: 4.5.0 |
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- Tokenizers: 0.22.2 |
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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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