tomaarsen HF staff commited on
Commit
0a5d82b
1 Parent(s): bd32e0c

Add new SentenceTransformer model.

Browse files
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ language:
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+ - en
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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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+ - loss:CosineSimilarityLoss
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+ base_model: google-bert/bert-base-uncased
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+ metrics:
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+ - pearson_cosine
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+ - spearman_cosine
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+ - pearson_manhattan
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+ - spearman_manhattan
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+ - pearson_euclidean
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+ - spearman_euclidean
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+ - pearson_dot
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+ - spearman_dot
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+ - pearson_max
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+ - spearman_max
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+ widget:
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+ - source_sentence: A woman is dancing.
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+ sentences:
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+ - An audience watches a girl dance.
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+ - A man is outside on a July day.
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+ - A man is cutting up carrots.
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+ - source_sentence: A man shoots a man.
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+ sentences:
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+ - The man is aiming a gun.
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+ - A helicopter flies over water.
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+ - a dog trots through the grass.
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+ - source_sentence: A man is spitting.
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+ sentences:
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+ - A man is crying.
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+ - A helicopter flies over water.
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+ - A slow loris hanging on a cord.
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+ - source_sentence: A boy is vacuuming.
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+ sentences:
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+ - A little boy is vacuuming the floor.
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+ - A guy is playing an instrument.
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+ - A woman equestrian riding a horse.
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+ - source_sentence: A woman is reading.
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+ sentences:
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+ - A woman is writing something.
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+ - A man is standing in the rain.
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+ - A man slices an onion.
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+ pipeline_tag: sentence-similarity
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+ co2_eq_emissions:
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+ emissions: 4.738044659547021
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+ energy_consumed: 0.012189401288254294
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+ source: codecarbon
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+ training_type: fine-tuning
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+ on_cloud: false
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+ cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
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+ ram_total_size: 31.777088165283203
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+ hours_used: 0.058
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+ hardware_used: 1 x NVIDIA GeForce RTX 3090
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+ model-index:
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+ - name: SentenceTransformer based on google-bert/bert-base-uncased
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+ results:
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: sts test
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+ type: sts-test
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.8682431647858876
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8703313606188837
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+ name: Spearman Cosine
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+ - type: pearson_manhattan
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+ value: 0.8385159885167599
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+ name: Pearson Manhattan
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+ - type: spearman_manhattan
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+ value: 0.8435007318066774
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+ name: Spearman Manhattan
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+ - type: pearson_euclidean
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+ value: 0.8391102057706885
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+ name: Pearson Euclidean
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+ - type: spearman_euclidean
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+ value: 0.8441165556372876
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+ name: Spearman Euclidean
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+ - type: pearson_dot
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+ value: 0.8140605796498762
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+ name: Pearson Dot
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+ - type: spearman_dot
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+ value: 0.8174591525223206
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+ name: Spearman Dot
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+ - type: pearson_max
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+ value: 0.8682431647858876
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+ name: Pearson Max
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+ - type: spearman_max
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+ value: 0.8703313606188837
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+ name: Spearman Max
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+ - type: pearson_cosine
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+ value: 0.8418519780467144
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8363102079867478
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+ name: Spearman Cosine
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+ - type: pearson_manhattan
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+ value: 0.8282641539296681
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+ name: Pearson Manhattan
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+ - type: spearman_manhattan
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+ value: 0.8261442750405601
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+ name: Spearman Manhattan
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+ - type: pearson_euclidean
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+ value: 0.8279900369159026
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+ name: Pearson Euclidean
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+ - type: spearman_euclidean
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+ value: 0.8258841934048688
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+ name: Spearman Euclidean
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+ - type: pearson_dot
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+ value: 0.7681509901549408
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+ name: Pearson Dot
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+ - type: spearman_dot
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+ value: 0.757455580460212
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+ name: Spearman Dot
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+ - type: pearson_max
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+ value: 0.8418519780467144
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+ name: Pearson Max
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+ - type: spearman_max
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+ value: 0.8363102079867478
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+ name: Spearman Max
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+ ---
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+
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+ # SentenceTransformer based on google-bert/bert-base-uncased
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) 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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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** Sentence Transformer
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+ - **Base model:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
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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/stsb](https://huggingface.co/datasets/sentence-transformers/stsb)
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+ - **Language:** en
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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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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+
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+ ### Full Model Architecture
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+
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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: 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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+ )
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+ ```
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+
163
+ ## Usage
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+
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+ ### Direct Usage (Sentence Transformers)
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+
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+ First install the Sentence Transformers library:
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+
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+ ```bash
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+ pip install -U sentence-transformers
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+ ```
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+
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+ Then you can load this model and run inference.
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+ ```python
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+ from sentence_transformers import SentenceTransformer
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+
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+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("tomaarsen/bert-base-uncased-augmentation-indomain-nlpaug-sts")
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+ # Run inference
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+ sentences = [
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+ 'A woman is reading.',
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+ 'A woman is writing something.',
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+ 'A man is standing in the rain.',
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+ ]
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+ embeddings = model.encode(sentences)
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+ print(embeddings.shape)
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+ # [3, 768]
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+
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+ # Get the similarity scores for the embeddings
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+ similarities = model.similarity(embeddings)
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+ print(similarities.shape)
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+ # [3, 3]
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+ ```
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+
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+ <!--
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+ ### Direct Usage (Transformers)
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+
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+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
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+ </details>
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+ -->
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+
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+ <!--
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+ ### Downstream Usage (Sentence Transformers)
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+
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+ You can finetune this model on your own dataset.
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+
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+ <details><summary>Click to expand</summary>
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+
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+ </details>
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+
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+ #### Semantic Similarity
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+ * Dataset: `sts-test`
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+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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+
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+ | Metric | Value |
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+ |:--------------------|:-----------|
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+ | pearson_cosine | 0.8682 |
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+ | **spearman_cosine** | **0.8703** |
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+ | pearson_manhattan | 0.8385 |
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+ | spearman_manhattan | 0.8435 |
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+ | pearson_euclidean | 0.8391 |
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+ | spearman_euclidean | 0.8441 |
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+ | pearson_dot | 0.8141 |
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+ | spearman_dot | 0.8175 |
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+ | pearson_max | 0.8682 |
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+ | spearman_max | 0.8703 |
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+
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+ #### Semantic Similarity
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+ * Dataset: `sts-test`
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+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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+
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+ | Metric | Value |
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+ |:--------------------|:-----------|
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+ | pearson_cosine | 0.8419 |
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+ | **spearman_cosine** | **0.8363** |
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+ | pearson_manhattan | 0.8283 |
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+ | spearman_manhattan | 0.8261 |
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+ | pearson_euclidean | 0.828 |
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+ | spearman_euclidean | 0.8259 |
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+ | pearson_dot | 0.7682 |
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+ | spearman_dot | 0.7575 |
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+ | pearson_max | 0.8419 |
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+ | spearman_max | 0.8363 |
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
260
+ *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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+ <!--
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+ ### Recommendations
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+
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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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+
269
+ ## Training Details
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+
271
+ ### Training Dataset
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+
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+ #### sentence-transformers/stsb
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+
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+ * Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [d999f12](https://huggingface.co/datasets/sentence-transformers/stsb/tree/d999f12281623b0925506817d9bd85e88289218a)
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+ * Size: 11,498 training samples
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+ * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | sentence1 | sentence2 | score |
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+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
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+ | type | string | string | float |
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+ | details | <ul><li>min: 6 tokens</li><li>mean: 10.0 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.95 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
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+ * Samples:
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+ | sentence1 | sentence2 | score |
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+ |:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------|
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+ | <code>A plane is taking off.</code> | <code>An air plane is taking off.</code> | <code>1.0</code> |
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+ | <code>A man is playing a large flute.</code> | <code>A man is playing a flute.</code> | <code>0.76</code> |
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+ | <code>A man is spreading shreded cheese on a pizza.</code> | <code>A man is spreading shredded cheese on an uncooked pizza.</code> | <code>0.76</code> |
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+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/losses.html#cosinesimilarityloss) with these parameters:
290
+ ```json
291
+ {
292
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
293
+ }
294
+ ```
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+
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+ ### Evaluation Dataset
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+
298
+ #### sentence-transformers/stsb
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+
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+ * Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [d999f12](https://huggingface.co/datasets/sentence-transformers/stsb/tree/d999f12281623b0925506817d9bd85e88289218a)
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+ * Size: 1,500 evaluation samples
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+ * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
303
+ * Approximate statistics based on the first 1000 samples:
304
+ | | sentence1 | sentence2 | score |
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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: 15.1 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.11 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
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+ * Samples:
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+ | sentence1 | sentence2 | score |
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+ |:--------------------------------------------------|:------------------------------------------------------|:------------------|
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+ | <code>A man with a hard hat is dancing.</code> | <code>A man wearing a hard hat is dancing.</code> | <code>1.0</code> |
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+ | <code>A young child is riding a horse.</code> | <code>A child is riding a horse.</code> | <code>0.95</code> |
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+ | <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code> |
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+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/losses.html#cosinesimilarityloss) with these parameters:
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+ ```json
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+ {
317
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
318
+ }
319
+ ```
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+
321
+ ### Training Hyperparameters
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+ #### Non-Default Hyperparameters
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+
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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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+ - `num_train_epochs`: 1
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+ - `warmup_ratio`: 0.1
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+ - `fp16`: True
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+
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+ #### All Hyperparameters
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+ <details><summary>Click to expand</summary>
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+
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+ - `overwrite_output_dir`: False
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+ - `do_predict`: False
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+ - `eval_strategy`: steps
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+ - `prediction_loss_only`: False
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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`: 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`: 1.0
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+ - `num_train_epochs`: 1
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+ - `max_steps`: -1
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+ - `lr_scheduler_type`: linear
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+ - `lr_scheduler_kwargs`: {}
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+ - `warmup_ratio`: 0.1
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+ - `warmup_steps`: 0
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+ - `log_level`: passive
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+ - `log_level_replica`: warning
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+ - `log_on_each_node`: True
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+ - `logging_nan_inf_filter`: True
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+ - `save_safetensors`: True
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+ - `save_on_each_node`: False
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+ - `save_only_model`: False
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+ - `no_cuda`: False
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+ - `use_cpu`: False
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+ - `use_mps_device`: False
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+ - `seed`: 42
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+ - `data_seed`: None
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+ - `jit_mode_eval`: False
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+ - `use_ipex`: False
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+ - `bf16`: False
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+ - `fp16`: True
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+ - `fp16_opt_level`: O1
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+ - `half_precision_backend`: auto
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+ - `bf16_full_eval`: False
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+ - `fp16_full_eval`: False
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+ - `tf32`: None
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+ - `local_rank`: 0
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+ - `ddp_backend`: None
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+ - `tpu_num_cores`: None
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+ - `tpu_metrics_debug`: False
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+ - `debug`: []
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+ - `dataloader_drop_last`: False
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+ - `dataloader_num_workers`: 0
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+ - `dataloader_prefetch_factor`: None
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+ - `past_index`: -1
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+ - `disable_tqdm`: False
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+ - `remove_unused_columns`: True
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+ - `label_names`: None
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+ - `load_best_model_at_end`: False
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+ - `ignore_data_skip`: False
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+ - `fsdp`: []
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+ - `fsdp_min_num_params`: 0
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+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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+ - `fsdp_transformer_layer_cls_to_wrap`: None
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+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, '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
399
+ - `optim_args`: None
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+ - `adafactor`: False
401
+ - `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`: None
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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
426
+ - `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_sampler`: batch_sampler
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+ - `multi_dataset_batch_sampler`: proportional
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+
441
+ </details>
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+
443
+ ### Training Logs
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+ | Epoch | Step | Training Loss | loss | sts-test_spearman_cosine |
445
+ |:------:|:----:|:-------------:|:------:|:------------------------:|
446
+ | 0.1391 | 100 | 0.0572 | 0.0427 | 0.8222 |
447
+ | 0.2782 | 200 | 0.0316 | 0.0342 | 0.8450 |
448
+ | 0.4172 | 300 | 0.0276 | 0.0324 | 0.8621 |
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+ | 0.5563 | 400 | 0.0246 | 0.0300 | 0.8661 |
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+ | 0.6954 | 500 | 0.0206 | 0.0288 | 0.8650 |
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+ | 0.8345 | 600 | 0.0186 | 0.0301 | 0.8696 |
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+ | 0.9736 | 700 | 0.0185 | 0.0286 | 0.8703 |
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+ | 1.0 | 719 | - | - | 0.8363 |
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+
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+
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+ ### Environmental Impact
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+ Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
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+ - **Energy Consumed**: 0.012 kWh
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+ - **Carbon Emitted**: 0.005 kg of CO2
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+ - **Hours Used**: 0.058 hours
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+
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+ ### Training Hardware
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+ - **On Cloud**: No
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+ - **GPU Model**: 1 x NVIDIA GeForce RTX 3090
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+ - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
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+ - **RAM Size**: 31.78 GB
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+
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+ ### Framework Versions
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+ - Python: 3.11.6
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+ - Sentence Transformers: 3.0.0.dev0
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+ - Transformers: 4.41.0.dev0
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+ - PyTorch: 2.3.0+cu121
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+ - Accelerate: 0.26.1
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+ - Datasets: 2.18.0
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+ - Tokenizers: 0.19.1
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+
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+ ## Citation
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+
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+ ### BibTeX
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+
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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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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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