tomaarsen HF Staff commited on
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Add new SentenceTransformer model

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  *.zip filter=lfs diff=lfs merge=lfs -text
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1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 384,
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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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+ 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:300000
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+ - loss:CachedMultipleNegativesRankingLoss
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+ base_model: jhu-clsp/mmBERT-small
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+ widget:
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+ - source_sentence: what county is lyndhurst, ohio in
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+ sentences:
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+ - This article is about the song written by Kenneth Gamble, Leon Huff and Cary Gilbert.
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+ For the Tina Turner song, see Don't Leave Me This Way (Tina Turner song). Don't
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+ Leave Me This Way is a song written by Kenneth Gamble, Leon Huff and Cary Gilbert.
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+ First charting as a hit for Harold Melvin & the Blue Notes featuring Teddy Pendergrass,
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+ an act on Gamble & Huff's Philadelphia International label in 1975, Don't Leave
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+ Me This Way was later a huge disco hit for Motown artist Thelma Houston in 1977.
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+ - "Lyndhurst is a city in Cuyahoga County, Ohio, United States. The population was\
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+ \ 14,001 at the 2010 census. Lyndhurst is located in northeastern Ohio, and is\
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+ \ a suburb of Cleveland. A small part of Lyndhurst was originally part of Mayfield\
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+ \ Township. It used to be called Euclidville before Lyndhurst was chosen. Lyndhurst\
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+ \ is located at 41°31â\x80²17â\x80³N 81°29â\x80²25â\x80³W / 41.52139°N 81.49028°W\
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+ \ / 41.52139; -81.49028 (41.521352, -81.490141)."
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+ - Welcome to Trumbull County... Trumbull County, the county seat, located in Warren,
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+ Ohio, consists of a combination of both urban and rural communities situated in
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+ the northeast corner of Ohio. It is situated roughly between the Youngstown, Cleveland
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+ and Akron corridors.
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+ - source_sentence: who founded the american graphophone company
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+ sentences:
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+ - In 1886, Graham Bell and Charles Sumner Tainter founded the American Graphophone
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+ Company to distribute and sell graphophones in the US and Canada under license
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+ from the Volta Graphophone Company. In 1890, the American Graphophone Company
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+ stopped production of new phonographs due to sagging orders.
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+ - ShelfGenie How much does a ShelfGenie franchise cost? ShelfGenie has a franchise
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+ fee of up to $45,000, with a total initial investment range of $70,100 to $107,750.
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+ Local ShelfGenie franchise opportunities. ShelfGenie is looking to grow in a number
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+ of cities around the country. To find out if there's a franchise opportunity in
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+ your city, unlock more information.
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+ - "A+E Networks. The technology that made the modern music business possible came\
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+ \ into existence in the New Jersey laboratory where Thomas Alva Edison created\
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+ \ the first device to both record sound and play it back. He was awarded U.S.\
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+ \ Patent No. 200,521 for his inventionâ\x80\x93the phonographâ\x80\x93on this\
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+ \ day in 1878."
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+ - source_sentence: is housekeeping camp flooded?
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+ sentences:
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+ - 'What is the importance of housekeeping at work? A: Workplace housekeeping promotes
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+ sanitation, safety, organization and productivity. It also boosts morale. Daily
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+ housekeeping maintenance keeps the workplac... Full Answer >'
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+ - The back patio area of a cabin is partially submerged in flood water at Housekeeping
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+ Camp on Monday, Jan. 9, 2017, in Yosemite National Park. The Merced River, swollen
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+ with storm runoff, crested at 12.7 feet at 4 a.m. SILVIA FLORES [email protected].
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+ - "1 Bake for 8 minutes, then rotate the pan and check the underside of the bagels.\
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+ \ 2 If theyâ\x80\x99re getting too dark, place another pan under the baking sheet.\
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+ \ ( 3 Doubling the pan will insulate the first baking sheet.) Bake for another\
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+ \ 8 to 12 minutes, until the bagels are a golden brown. 4 13."
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+ - source_sentence: causes for infection in the nerve of tooth
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+ sentences:
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+ - If a cavity is causing the toothache, your dentist will fill the cavity or possibly
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+ extract the tooth, if necessary. A root canal might be needed if the cause of
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+ the toothache is determined to be an infection of the tooth's nerve. Bacteria
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+ that have worked their way into the inner aspects of the tooth cause such an infection.
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+ An antibiotic may be prescribed if there is fever or swelling of the jaw.
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+ - "According to Article III, Section 1 of the Constitution, judges and justices\
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+ \ of the Judicial Branch serve during good behavior.. This means they are appointed\
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+ \ for life, unles â\x80¦ s they are impeached and removed from office. + 50 others\
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+ \ found this useful.he term length for members of the House are two years and\
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+ \ a staggering six years for members of the Senate."
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+ - Inflamed or infected pulp (pulpitis) most often causes a toothache. To relieve
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+ the pain and prevent further complications, the tooth may be extracted (surgically
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+ removed) or saved by root canal treatment.
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+ - source_sentence: what county is hayden in
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+ sentences:
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+ - Normally, the Lead Agency is the agency with general governmental powers such
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+ as a city or a county. Agencies with limited powers or districts that provide
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+ a public service/utility such as a recreation and park district will tend to be
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+ a Responsible Agency.
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+ - According to the United States Census Bureau, the city has a total area of 9.61
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+ square miles (24.89 km2), of which 9.60 square miles (24.86 km2) is land and 0.01
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+ square miles (0.03 km2) is water. It lies at the southwestern end of Hayden Lake,
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+ and the elevation of the city is 2,287 feet (697 m) above sea level. Hayden is
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+ located on U.S. Route 95 at the junction of Route 41. It is also four miles (6
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+ km) north of Interstate 90 and Coeur d'Alene. The Coeur d'Alene airport is northwest
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+ of Hayden.
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+ - Hayden is a city in Kootenai County, Idaho, United States. Located in the northern
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+ portion of the state, just north of Coeur d'Alene, its population was 13,294 at
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+ the 2010 census.
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+ datasets:
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+ - sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
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+ pipeline_tag: sentence-similarity
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+ library_name: sentence-transformers
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+ metrics:
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+ - cosine_accuracy
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+ co2_eq_emissions:
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+ emissions: 127.97200216001644
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+ energy_consumed: 0.47818192136676524
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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: 1.202
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+ hardware_used: 1 x NVIDIA GeForce RTX 3090
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+ model-index:
108
+ - name: SentenceTransformer based on jhu-clsp/mmBERT-small
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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: msmarco co condenser eval triplet
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+ type: msmarco-co-condenser-eval-triplet
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+ metrics:
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+ - type: cosine_accuracy
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+ value: 0.9259999990463257
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+ name: Cosine Accuracy
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+ ---
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+
122
+ # SentenceTransformer based on jhu-clsp/mmBERT-small
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+
124
+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jhu-clsp/mmBERT-small](https://huggingface.co/jhu-clsp/mmBERT-small) on the [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) dataset. It maps sentences & paragraphs to a 384-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:** [jhu-clsp/mmBERT-small](https://huggingface.co/jhu-clsp/mmBERT-small) <!-- at revision 5b24071769c41bfbee7fb59c4701e1f12c288c1a -->
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+ - **Maximum Sequence Length:** 8192 tokens
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+ - **Output Dimensionality:** 384 dimensions
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+ - **Similarity Function:** Cosine Similarity
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+ - **Training Dataset:**
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+ - [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1)
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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': 8192, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
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+ (1): Pooling({'word_embedding_dimension': 384, '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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+
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+ ## Usage
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+
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+ ### Direct Usage (Sentence Transformers)
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+
158
+ First install the Sentence Transformers library:
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+
160
+ ```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/mmBERT-small-msmarco")
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+ # Run inference
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+ queries = [
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+ "what county is hayden in",
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+ ]
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+ documents = [
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+ "Hayden is a city in Kootenai County, Idaho, United States. Located in the northern portion of the state, just north of Coeur d'Alene, its population was 13,294 at the 2010 census.",
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+ "According to the United States Census Bureau, the city has a total area of 9.61 square miles (24.89 km2), of which 9.60 square miles (24.86 km2) is land and 0.01 square miles (0.03 km2) is water. It lies at the southwestern end of Hayden Lake, and the elevation of the city is 2,287 feet (697 m) above sea level. Hayden is located on U.S. Route 95 at the junction of Route 41. It is also four miles (6 km) north of Interstate 90 and Coeur d'Alene. The Coeur d'Alene airport is northwest of Hayden.",
177
+ 'Normally, the Lead Agency is the agency with general governmental powers such as a city or a county. Agencies with limited powers or districts that provide a public service/utility such as a recreation and park district will tend to be a Responsible Agency.',
178
+ ]
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+ query_embeddings = model.encode_query(queries)
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+ document_embeddings = model.encode_document(documents)
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+ print(query_embeddings.shape, document_embeddings.shape)
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+ # [1, 384] [3, 384]
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+
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+ # Get the similarity scores for the embeddings
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+ similarities = model.similarity(query_embeddings, document_embeddings)
186
+ print(similarities)
187
+ # tensor([[0.7909, 0.6648, 0.1302]])
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+ ```
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+
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+ <!--
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+ ### Direct Usage (Transformers)
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+
193
+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
195
+ </details>
196
+ -->
197
+
198
+ <!--
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+ ### Downstream Usage (Sentence Transformers)
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+
201
+ You can finetune this model on your own dataset.
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+
203
+ <details><summary>Click to expand</summary>
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+
205
+ </details>
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+ -->
207
+
208
+ <!--
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+ ### Out-of-Scope Use
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+
211
+ *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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+
216
+ ### Metrics
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+
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+ #### Triplet
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+
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+ * Dataset: `msmarco-co-condenser-eval-triplet`
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+ * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
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+
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+ | Metric | Value |
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+ |:--------------------|:----------|
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+ | **cosine_accuracy** | **0.926** |
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+
227
+ <!--
228
+ ## Bias, Risks and Limitations
229
+
230
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
231
+ -->
232
+
233
+ <!--
234
+ ### Recommendations
235
+
236
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
237
+ -->
238
+
239
+ ## Training Details
240
+
241
+ ### Training Dataset
242
+
243
+ #### msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
244
+
245
+ * Dataset: [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2)
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+ * Size: 300,000 training samples
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+ * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
248
+ * Approximate statistics based on the first 1000 samples:
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+ | | query | positive | negative |
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+ |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
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+ | type | string | string | string |
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+ | details | <ul><li>min: 4 tokens</li><li>mean: 8.61 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 79.71 tokens</li><li>max: 237 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 80.99 tokens</li><li>max: 417 tokens</li></ul> |
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+ * Samples:
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+ | query | positive | negative |
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+ |:---------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | <code>what is the meaning of menu planning</code> | <code>Menu planning is the selection of a menu for an event. Such as picking out the dinner for your wedding or even a meal at a Birthday Party. Menu planning is when you are preparing a calendar of meals and you have to sit down and decide what meat and veggies you want to serve on each certain day.</code> | <code>Menu Costs. In economics, a menu cost is the cost to a firm resulting from changing its prices. The name stems from the cost of restaurants literally printing new menus, but economists use it to refer to the costs of changing nominal prices in general.</code> |
257
+ | <code>how old is brett butler</code> | <code>Brett Butler is 59 years old. To be more precise (and nerdy), the current age as of right now is 21564 days or (even more geeky) 517536 hours. That's a lot of hours!</code> | <code>Passed in: St. John's, Newfoundland and Labrador, Canada. Passed on: 16/07/2016. Published in the St. John's Telegram. Passed away suddenly at the Health Sciences Centre surrounded by his loving family, on July 16, 2016 Robert (Bobby) Joseph Butler, age 52 years. Predeceased by his special aunt Geri Murrin and uncle Mike Mchugh; grandparents Joe and Margaret Murrin and Jack and Theresa Butler.</code> |
258
+ | <code>when was the last navajo treaty sign?</code> | <code>In Executive Session, Senate of the United States, July 25, 1868. Resolved, (two-thirds of the senators present concurring,) That the Senate advise and consent to the ratification of the treaty between the United States and the Navajo Indians, concluded at Fort Sumner, New Mexico, on the first day of June, 1868.</code> | <code>Share Treaty of Greenville. The Treaty of Greenville was signed August 3, 1795, between the United States, represented by Gen. Anthony Wayne, and chiefs of the Indian tribes located in the Northwest Territory, including the Wyandots, Delawares, Shawnees, Ottawas, Miamis, and others.</code> |
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+ * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
260
+ ```json
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+ {
262
+ "scale": 20.0,
263
+ "similarity_fct": "cos_sim",
264
+ "mini_batch_size": 64,
265
+ "gather_across_devices": false
266
+ }
267
+ ```
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+
269
+ ### Evaluation Dataset
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+
271
+ #### msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
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+
273
+ * Dataset: [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2)
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+ * Size: 1,000 evaluation samples
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+ * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
276
+ * Approximate statistics based on the first 1000 samples:
277
+ | | query | positive | negative |
278
+ |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
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+ | type | string | string | string |
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+ | details | <ul><li>min: 4 tokens</li><li>mean: 8.57 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 81.26 tokens</li><li>max: 242 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 81.33 tokens</li><li>max: 281 tokens</li></ul> |
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+ * Samples:
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+ | query | positive | negative |
283
+ |:------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
284
+ | <code>what county is holly springs nc in</code> | <code>Holly Springs, North Carolina. Holly Springs is a town in Wake County, North Carolina, United States. As of the 2010 census, the town population was 24,661, over 2½ times its population in 2000. Contents.</code> | <code>The Mt. Holly Springs Park & Resort. One of the numerous trolley routes that carried people around the county at the turn of the century was the Carlisle & Mt. Holly Railway Company. The “Holly Trolley” as it came to be known was put into service by Patricio Russo and made its first run on May 14, 1901.</code> |
285
+ | <code>how long does nyquil stay in your system</code> | <code>In order to understand exactly how long Nyquil lasts, it is absolutely vital to learn about the various ingredients in the drug. One of the ingredients found in Nyquil is Doxylamine, which is an antihistamine. This specific medication has a biological half-life or 6 to 12 hours. With this in mind, it is possible for the drug to remain in the system for a period of 12 to 24 hours. It should be known that the specifics will depend on a wide variety of different factors, including your age and metabolism.</code> | <code>I confirmed that NyQuil is about 10% alcohol, a higher content than most domestic beers. When I asked about the relatively high proof, I was told that the alcohol dilutes the active ingredients. The alcohol free version is there for customers with addiction issues.. also found that in that version there is twice the amount of DXM. When I asked if I could speak to a chemist or scientist, I was told they didn't have anyone who fit that description there. It’s been eight years since I kicked NyQuil. I've been sober from alcohol for four years.</code> |
286
+ | <code>what are mineral water</code> | <code>1 Mineral water – water from a mineral spring that contains various minerals, such as salts and sulfur compounds. 2 It comes from a source tapped at one or more bore holes or spring, and originates from a geologically and physically protected underground water source. Mineral water – water from a mineral spring that contains various minerals, such as salts and sulfur compounds. 2 It comes from a source tapped at one or more bore holes or spring, and originates from a geologically and physically protected underground water source.</code> | <code>Minerals for Your Body. Drinking mineral water is beneficial to health and well-being. But it is not only the amount of water you drink that is important-what the water contains is even more essential.inerals for Your Body. Drinking mineral water is beneficial to health and well-being. But it is not only the amount of water you drink that is important-what the water contains is even more essential.</code> |
287
+ * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
288
+ ```json
289
+ {
290
+ "scale": 20.0,
291
+ "similarity_fct": "cos_sim",
292
+ "mini_batch_size": 64,
293
+ "gather_across_devices": false
294
+ }
295
+ ```
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+
297
+ ### 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`: 2048
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+ - `per_device_eval_batch_size`: 2048
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+ - `learning_rate`: 8e-05
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+ - `num_train_epochs`: 1
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+ - `warmup_ratio`: 0.05
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+ - `bf16`: True
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+ - `batch_sampler`: no_duplicates
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+
309
+ #### 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`: True
316
+ - `per_device_train_batch_size`: 2048
317
+ - `per_device_eval_batch_size`: 2048
318
+ - `per_gpu_train_batch_size`: None
319
+ - `per_gpu_eval_batch_size`: None
320
+ - `gradient_accumulation_steps`: 1
321
+ - `eval_accumulation_steps`: None
322
+ - `torch_empty_cache_steps`: None
323
+ - `learning_rate`: 8e-05
324
+ - `weight_decay`: 0.0
325
+ - `adam_beta1`: 0.9
326
+ - `adam_beta2`: 0.999
327
+ - `adam_epsilon`: 1e-08
328
+ - `max_grad_norm`: 1.0
329
+ - `num_train_epochs`: 1
330
+ - `max_steps`: -1
331
+ - `lr_scheduler_type`: linear
332
+ - `lr_scheduler_kwargs`: {}
333
+ - `warmup_ratio`: 0.05
334
+ - `warmup_steps`: 0
335
+ - `log_level`: passive
336
+ - `log_level_replica`: warning
337
+ - `log_on_each_node`: True
338
+ - `logging_nan_inf_filter`: True
339
+ - `save_safetensors`: True
340
+ - `save_on_each_node`: False
341
+ - `save_only_model`: False
342
+ - `restore_callback_states_from_checkpoint`: False
343
+ - `no_cuda`: False
344
+ - `use_cpu`: False
345
+ - `use_mps_device`: False
346
+ - `seed`: 42
347
+ - `data_seed`: None
348
+ - `jit_mode_eval`: False
349
+ - `use_ipex`: False
350
+ - `bf16`: True
351
+ - `fp16`: False
352
+ - `fp16_opt_level`: O1
353
+ - `half_precision_backend`: auto
354
+ - `bf16_full_eval`: False
355
+ - `fp16_full_eval`: False
356
+ - `tf32`: None
357
+ - `local_rank`: 0
358
+ - `ddp_backend`: None
359
+ - `tpu_num_cores`: None
360
+ - `tpu_metrics_debug`: False
361
+ - `debug`: []
362
+ - `dataloader_drop_last`: False
363
+ - `dataloader_num_workers`: 0
364
+ - `dataloader_prefetch_factor`: None
365
+ - `past_index`: -1
366
+ - `disable_tqdm`: False
367
+ - `remove_unused_columns`: True
368
+ - `label_names`: None
369
+ - `load_best_model_at_end`: False
370
+ - `ignore_data_skip`: False
371
+ - `fsdp`: []
372
+ - `fsdp_min_num_params`: 0
373
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
374
+ - `fsdp_transformer_layer_cls_to_wrap`: None
375
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
376
+ - `parallelism_config`: None
377
+ - `deepspeed`: None
378
+ - `label_smoothing_factor`: 0.0
379
+ - `optim`: adamw_torch
380
+ - `optim_args`: None
381
+ - `adafactor`: False
382
+ - `group_by_length`: False
383
+ - `length_column_name`: length
384
+ - `ddp_find_unused_parameters`: None
385
+ - `ddp_bucket_cap_mb`: None
386
+ - `ddp_broadcast_buffers`: False
387
+ - `dataloader_pin_memory`: True
388
+ - `dataloader_persistent_workers`: False
389
+ - `skip_memory_metrics`: True
390
+ - `use_legacy_prediction_loop`: False
391
+ - `push_to_hub`: False
392
+ - `resume_from_checkpoint`: None
393
+ - `hub_model_id`: None
394
+ - `hub_strategy`: every_save
395
+ - `hub_private_repo`: None
396
+ - `hub_always_push`: False
397
+ - `hub_revision`: None
398
+ - `gradient_checkpointing`: False
399
+ - `gradient_checkpointing_kwargs`: None
400
+ - `include_inputs_for_metrics`: False
401
+ - `include_for_metrics`: []
402
+ - `eval_do_concat_batches`: True
403
+ - `fp16_backend`: auto
404
+ - `push_to_hub_model_id`: None
405
+ - `push_to_hub_organization`: None
406
+ - `mp_parameters`:
407
+ - `auto_find_batch_size`: False
408
+ - `full_determinism`: False
409
+ - `torchdynamo`: None
410
+ - `ray_scope`: last
411
+ - `ddp_timeout`: 1800
412
+ - `torch_compile`: False
413
+ - `torch_compile_backend`: None
414
+ - `torch_compile_mode`: None
415
+ - `include_tokens_per_second`: False
416
+ - `include_num_input_tokens_seen`: False
417
+ - `neftune_noise_alpha`: None
418
+ - `optim_target_modules`: None
419
+ - `batch_eval_metrics`: False
420
+ - `eval_on_start`: False
421
+ - `use_liger_kernel`: False
422
+ - `liger_kernel_config`: None
423
+ - `eval_use_gather_object`: False
424
+ - `average_tokens_across_devices`: False
425
+ - `prompts`: None
426
+ - `batch_sampler`: no_duplicates
427
+ - `multi_dataset_batch_sampler`: proportional
428
+ - `router_mapping`: {}
429
+ - `learning_rate_mapping`: {}
430
+
431
+ </details>
432
+
433
+ ### Training Logs
434
+ | Epoch | Step | Training Loss | Validation Loss | msmarco-co-condenser-eval-triplet_cosine_accuracy |
435
+ |:------:|:----:|:-------------:|:---------------:|:-------------------------------------------------:|
436
+ | -1 | -1 | - | - | 0.5940 |
437
+ | 0.0136 | 2 | 8.0077 | - | - |
438
+ | 0.0272 | 4 | 7.8328 | - | - |
439
+ | 0.0408 | 6 | 7.0324 | - | - |
440
+ | 0.0544 | 8 | 5.4394 | - | - |
441
+ | 0.0680 | 10 | 4.006 | - | - |
442
+ | 0.0816 | 12 | 2.9163 | - | - |
443
+ | 0.0952 | 14 | 1.9654 | - | - |
444
+ | 0.1020 | 15 | - | 1.0977 | 0.8170 |
445
+ | 0.1088 | 16 | 1.4945 | - | - |
446
+ | 0.1224 | 18 | 1.1823 | - | - |
447
+ | 0.1361 | 20 | 1.0182 | - | - |
448
+ | 0.1497 | 22 | 0.9313 | - | - |
449
+ | 0.1633 | 24 | 0.8635 | - | - |
450
+ | 0.1769 | 26 | 0.7985 | - | - |
451
+ | 0.1905 | 28 | 0.7625 | - | - |
452
+ | 0.2041 | 30 | 0.7046 | 0.5229 | 0.8880 |
453
+ | 0.2177 | 32 | 0.6632 | - | - |
454
+ | 0.2313 | 34 | 0.647 | - | - |
455
+ | 0.2449 | 36 | 0.6527 | - | - |
456
+ | 0.2585 | 38 | 0.6135 | - | - |
457
+ | 0.2721 | 40 | 0.594 | - | - |
458
+ | 0.2857 | 42 | 0.5754 | - | - |
459
+ | 0.2993 | 44 | 0.5775 | - | - |
460
+ | 0.3061 | 45 | - | 0.4092 | 0.9080 |
461
+ | 0.3129 | 46 | 0.5679 | - | - |
462
+ | 0.3265 | 48 | 0.5255 | - | - |
463
+ | 0.3401 | 50 | 0.5108 | - | - |
464
+ | 0.3537 | 52 | 0.5494 | - | - |
465
+ | 0.3673 | 54 | 0.5411 | - | - |
466
+ | 0.3810 | 56 | 0.5154 | - | - |
467
+ | 0.3946 | 58 | 0.4887 | - | - |
468
+ | 0.4082 | 60 | 0.487 | 0.3600 | 0.9160 |
469
+ | 0.4218 | 62 | 0.475 | - | - |
470
+ | 0.4354 | 64 | 0.4719 | - | - |
471
+ | 0.4490 | 66 | 0.4609 | - | - |
472
+ | 0.4626 | 68 | 0.4693 | - | - |
473
+ | 0.4762 | 70 | 0.4533 | - | - |
474
+ | 0.4898 | 72 | 0.4437 | - | - |
475
+ | 0.5034 | 74 | 0.4651 | - | - |
476
+ | 0.5102 | 75 | - | 0.3328 | 0.9190 |
477
+ | 0.5170 | 76 | 0.4548 | - | - |
478
+ | 0.5306 | 78 | 0.4567 | - | - |
479
+ | 0.5442 | 80 | 0.4366 | - | - |
480
+ | 0.5578 | 82 | 0.4414 | - | - |
481
+ | 0.5714 | 84 | 0.4632 | - | - |
482
+ | 0.5850 | 86 | 0.4213 | - | - |
483
+ | 0.5986 | 88 | 0.4343 | - | - |
484
+ | 0.6122 | 90 | 0.4232 | 0.3158 | 0.9230 |
485
+ | 0.6259 | 92 | 0.3798 | - | - |
486
+ | 0.6395 | 94 | 0.4089 | - | - |
487
+ | 0.6531 | 96 | 0.3942 | - | - |
488
+ | 0.6667 | 98 | 0.4007 | - | - |
489
+ | 0.6803 | 100 | 0.4037 | - | - |
490
+ | 0.6939 | 102 | 0.4121 | - | - |
491
+ | 0.7075 | 104 | 0.4022 | - | - |
492
+ | 0.7143 | 105 | - | 0.2994 | 0.9210 |
493
+ | 0.7211 | 106 | 0.4081 | - | - |
494
+ | 0.7347 | 108 | 0.3978 | - | - |
495
+ | 0.7483 | 110 | 0.3905 | - | - |
496
+ | 0.7619 | 112 | 0.3879 | - | - |
497
+ | 0.7755 | 114 | 0.3963 | - | - |
498
+ | 0.7891 | 116 | 0.3892 | - | - |
499
+ | 0.8027 | 118 | 0.3906 | - | - |
500
+ | 0.8163 | 120 | 0.3867 | 0.2913 | 0.9250 |
501
+ | 0.8299 | 122 | 0.3821 | - | - |
502
+ | 0.8435 | 124 | 0.39 | - | - |
503
+ | 0.8571 | 126 | 0.4016 | - | - |
504
+ | 0.8707 | 128 | 0.3917 | - | - |
505
+ | 0.8844 | 130 | 0.3867 | - | - |
506
+ | 0.8980 | 132 | 0.39 | - | - |
507
+ | 0.9116 | 134 | 0.3795 | - | - |
508
+ | 0.9184 | 135 | - | 0.2826 | 0.9260 |
509
+ | 0.9252 | 136 | 0.3935 | - | - |
510
+ | 0.9388 | 138 | 0.3811 | - | - |
511
+ | 0.9524 | 140 | 0.3604 | - | - |
512
+ | 0.9660 | 142 | 0.3694 | - | - |
513
+ | 0.9796 | 144 | 0.3509 | - | - |
514
+ | 0.9932 | 146 | 0.3727 | - | - |
515
+ | -1 | -1 | - | - | 0.9260 |
516
+
517
+
518
+ ### Environmental Impact
519
+ Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
520
+ - **Energy Consumed**: 0.478 kWh
521
+ - **Carbon Emitted**: 0.128 kg of CO2
522
+ - **Hours Used**: 1.202 hours
523
+
524
+ ### Training Hardware
525
+ - **On Cloud**: No
526
+ - **GPU Model**: 1 x NVIDIA GeForce RTX 3090
527
+ - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
528
+ - **RAM Size**: 31.78 GB
529
+
530
+ ### Framework Versions
531
+ - Python: 3.11.6
532
+ - Sentence Transformers: 5.2.0.dev0
533
+ - Transformers: 4.57.0.dev0
534
+ - PyTorch: 2.7.1+cu126
535
+ - Accelerate: 1.6.0
536
+ - Datasets: 3.6.0
537
+ - Tokenizers: 0.22.0
538
+
539
+ ## Citation
540
+
541
+ ### BibTeX
542
+
543
+ #### Sentence Transformers
544
+ ```bibtex
545
+ @inproceedings{reimers-2019-sentence-bert,
546
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
547
+ author = "Reimers, Nils and Gurevych, Iryna",
548
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
549
+ month = "11",
550
+ year = "2019",
551
+ publisher = "Association for Computational Linguistics",
552
+ url = "https://arxiv.org/abs/1908.10084",
553
+ }
554
+ ```
555
+
556
+ #### CachedMultipleNegativesRankingLoss
557
+ ```bibtex
558
+ @misc{gao2021scaling,
559
+ title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
560
+ author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
561
+ year={2021},
562
+ eprint={2101.06983},
563
+ archivePrefix={arXiv},
564
+ primaryClass={cs.LG}
565
+ }
566
+ ```
567
+
568
+ <!--
569
+ ## Glossary
570
+
571
+ *Clearly define terms in order to be accessible across audiences.*
572
+ -->
573
+
574
+ <!--
575
+ ## Model Card Authors
576
+
577
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
578
+ -->
579
+
580
+ <!--
581
+ ## Model Card Contact
582
+
583
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
584
+ -->
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+ }
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