Add new SentenceTransformer model

1_Pooling/config.json

@@ -1,7 +1,7 @@
 {
-    "word_embedding_dimension": 384,
-    "pooling_mode_cls_token": false,
-    "pooling_mode_mean_tokens": true,
+    "word_embedding_dimension": 768,
+    "pooling_mode_cls_token": true,
+    "pooling_mode_mean_tokens": false,
     "pooling_mode_max_tokens": false,
     "pooling_mode_mean_sqrt_len_tokens": false,
     "pooling_mode_weightedmean_tokens": false,

README.md

@@ -7,86 +7,112 @@ tags:
 - generated_from_trainer
 - dataset_size:111470
 - loss:MultipleNegativesRankingLoss
-base_model: thenlper/gte-small
+base_model: Alibaba-NLP/gte-modernbert-base
 widget:
-- source_sentence: why are some rocks radioactive
+- source_sentence: when was the first elephant brought to america
   sentences:
-  - Radioactive accessory minerals such as zircon may contribute to the radioactivity
-    of a mineral which is otherwise non-radioactive by calculation. Many granites
-    or other igneous rocks contain some radioactivity because of minor, but highly
-    radioactive, accessory minerals.re = mineral density (S Atomic number / Molecular
-    Weight) where re is the electron density in grams/cc.efinition. Radioactivity
-    in minerals are caused by the inclusion of naturally-occurring radioactive elements
-    in the mineral's composition. The degree of radioactivity is dependent on the
-    concentration and isotope present in the mineral.
-  - Taking B-complex vitamins, which include vitamin B12, can cause urine to have
-    a bright yellow or even orange color, but check with your doctor to be sure that's
-    what is going on in your case. B vitamins are water-soluble vitamins, which means
-    that what your body doesn't use is excreted in your urine. Riboflavin (vitamin
-    B2) is especially likely to cause this color change in urine. Several medications
-    can also turn urine a bright yellow or orange color. Changes in urine color may
-    also signal certain health problems.
-  - Radioactive material is just another name for a group of unstable atoms that emit
-    ionizing radiation. These groups of unstable atoms emit radiation because they
-    try to become stable. Radioactive materials emit radiation in a process called
-    radioactive decay.
-- source_sentence: How was your experience of Lucid dreaming at home?
+  - Old Bet The first elephant brought to the United States was in 1796, aboard the
+    America which set sail from Calcutta for New York on December 3, 1795.[4] However,
+    it is not certain that this was Old Bet.[2] The first references to Old Bet start
+    in 1804 in Boston as part of a menagerie.[1] In 1808, while residing in Somers,
+    New York, Hachaliah Bailey purchased the menagerie elephant for $1,000 and named
+    it "Old Bet".[5][6]
+  - Cronus Rhea secretly gave birth to Zeus in Crete, and handed Cronus a stone wrapped
+    in swaddling clothes, also known as the Omphalos Stone, which he promptly swallowed,
+    thinking that it was his son.
+  - Renal artery One or two accessory renal arteries are frequently found, especially
+    on the left side since they usually arise from the aorta, and may come off above
+    (more common) or below the main artery. Instead of entering the kidney at the
+    hilus, they usually pierce the upper or lower part of the organ.
+- source_sentence: who won the india's next superstar grand finale
   sentences:
-  - How was your experience of Lucid dreaming at home?
-  - How was your experience of Lucid dreaming outside the home?
-  - "Bournemouth /Ë\x88bÉ\x94É\x99rnmÉ\x99θ/ is a large coastal resort town on the\
-    \ south coast of England directly to the east of the Jurassic Coast, a 96-mile\
-    \ (155 km) World Heritage Site. According to the 2011 census, the town has a population\
-    \ of 183,491 making it the largest settlement in Dorset.he Bournemouth Eye is\
-    \ a helium-filled balloon attached to a steel cable in the town's lower gardens.\
-    \ The spherical balloon is 69 m (226 ft) in circumference and carries an enclosed,\
-    \ steel gondola. Rising to a height of 150 m (492 ft), it provides a panoramic\
-    \ view of the surrounding area for up to 28 passengers."
-- source_sentence: what is iraq's dominant religion
+  - India's Next Superstars India's Next Superstars is a talent-search Indian reality
+    TV show, which premiered on Star Plus and is streamed on Hotstar.[1] Karan Johar
+    and Rohit Shetty are the judges for the show. [2] Aman Gandotra and Natasha Bharadwaj
+    were declared winners of 2018 season. Shruti Sharma won a 'Special Mention' award.
+    Runners up in the male category were Aashish Mehrotra and Harshvardhan Deo and
+    in the female category were Naina Singh and Shruti Sharma. [3]
+  - India national cricket team India was invited to The Imperial Cricket Council
+    in 1926, and made their debut as a Test playing nation in England in 1932, led
+    by CK Nayudu, who was considered as the best Indian batsman at the time.[14] The
+    one-off Test match between the two sides was played at Lord's in London. The team
+    was not strong in their batting at this point and went on to lose by 158 runs.[15]
+    In 1933, the first Test series in India was played between India and England with
+    matches in Bombay, Calcutta (now Kolkata) and Madras (now Chennai). England won
+    the series 2–0.[16] The Indian team continued to improve throughout the 1930s
+    and '40s but did not achieve an international victory during this period. In the
+    early 1940s, India didn't play any Test cricket due to the Second World War. The
+    team's first series as an independent country was in late 1947 against Sir Donald
+    Bradman's Invincibles (a name given to the Australia national cricket team of
+    that time). It was also the first Test series India played which was not against
+    England. Australia won the five-match series 4–0, with Bradman tormenting the
+    Indian bowling in his final Australian summer.[17] India subsequently played their
+    first Test series at home not against England against the West Indies in 1948.
+    West Indies won the 5-Test series 1–0.[18]
+  - Hindi Medium (film) Raj Batra (Irrfan Khan) is a rich businessman from Delhi staying
+    with his wife Mita (Saba Qamar). They studied in a Hindi Medium school but wants
+    their 5 year old daughter, Pia (Dishita Sehgal), to be admitted to one of the
+    top schools in Delhi. The top school, 'Delhi Grammar School', has a condition
+    that they will admit students who reside within 3km radius, so the family moves
+    to Vasant Vihar.
+- source_sentence: i am human and nothing of that which is human is alien to me meaning
   sentences:
-  - 'If you are working, consider taking maternity leave as early as you can. This
-    makes sense anyway because carrying twins is hard work, and most twins arrive
-    earlier than single babies (NCCWCH 2011: 128) . More than half of twins arrive
-    early, before 37 weeks (NCCWCH 2011: 120, Tamba 2012) .Talk to your midwife or
-    doctor if you are feeling down about your pregnancy (NICE 2011) .f you are working,
-    consider taking maternity leave as early as you can. This makes sense anyway because
-    carrying twins is hard work, and most twins arrive earlier than single babies
-    (NCCWCH 2011: 128) . More than half of twins arrive early, before 37 weeks (NCCWCH
-    2011: 120, Tamba 2012) .'
-  - "Introduction. Although Iranâ\x80\x99s state religion is Shiite Islam and the\
-    \ majority of its population is ethnically Persian, millions of minorities from\
-    \ various ethnic, religious, and linguistic backgrounds also reside in Iran. Among\
-    \ these groups are ethnic Kurds, Baluchis, and Azeris.lthough Iranâ\x80\x99s state\
-    \ religion is Shiite Islam and the majority of its population is ethnically Persian,\
-    \ millions of minorities from various ethnic, religious, and linguistic backgrounds\
-    \ also reside in Iran."
-  - In today's Republic of Iraq, where Islam is the state religion and claims the
-    beliefs of 95 percent of the population, the majority of Iraqis identify with
-    Arab culture. The second-largest cultural group is the Kurds, who are in the highlands
-    and mountain valleys of the north in a politically autonomous settlement.
-- source_sentence: how many years of education are needed to become a pediatric nurse
+  - America's Got Talent Introduced in season nine, the "Golden Buzzer" is located
+    on the center of the judges' desk and may be used once per season by each judge.
+    In season 9, a judge could press the golden buzzer to save an act from elimination,
+    regardless of the number of X's earned from the other judges. Starting in season
+    10 and onward, any act that receives a golden buzzer advances directly to the
+    live show; and in season 11, the hosts also were given the power to use the golden
+    buzzer. The golden buzzer is also used in the Judge Cuts format.
+  - You'll Never Walk Alone "You'll Never Walk Alone" is a show tune from the 1945
+    Rodgers and Hammerstein musical Carousel. In the second act of the musical, Nettie
+    Fowler, the cousin of the female protagonist Julie Jordan, sings "You'll Never
+    Walk Alone" to comfort and encourage Julie when her husband, Billy Bigelow, the
+    male lead, commits suicide after a failed robbery attempt. It is reprised in the
+    final scene to encourage a graduation class of which Louise (Billy and Julie's
+    daughter) is a member. The now invisible Billy, who has been granted the chance
+    to return to Earth for one day in order to redeem himself, watches the ceremony
+    and is able to silently motivate the unhappy Louise to join in the song.
+  - 'Terence One famous quotation by Terence reads: "Homo sum, humani nihil a me alienum
+    puto", or "I am human, and I think that nothing of that which is human is alien
+    to me." This appeared in his play Heauton Timorumenos.'
+- source_sentence: what do glial cells do in the brain
   sentences:
-  - In terms of educational background, pediatric nurse requirements include either
-    an Associate's or a Bachelor's degree in Nursing. An Associate's degree (ADN)
-    typically takes two years to complete, while a Bachelor's degree (BSN) typically
-    takes four years. ADN programs are usually offered by community colleges.
-  - "Photo of Oxford Suites Sonoma County - Rohnert Park - Rohnert Park, CA, United\
-    \ States Photo of Oxford Suites Sonoma County - Rohnert Park - Rohnert Park, CA,\
-    \ United States Living area with king bed by Monique' M. â\x80\x9CAnd there's\
-    \ a complimentary reception with 2 drinks, soup and salad bar nightly.â\x80\x9D\
-    \ in 2 reviews"
-  - 'From there, additional training specific to the care of children is required.
-    Pediatric nurses can become certified in the field and may choose to further specialize
-    in a particular area. Program Levels: Associate''s degree, bachelor''s degree.'
-- source_sentence: Schliemann recognized five shafts and cleared them like the graves
-    mentioned by Pausanias .
+  - 'Neuroglia Neuroglia, also called glial cells or simply glia, are non-neuronal
+    cells in the central nervous system (brain and spinal cord) and the peripheral
+    nervous system. They maintain homeostasis, form myelin, and provide support and
+    protection for neurons.[1] In the central nervous system, glial cells include
+    oligodendrocytes, astrocytes, ependymal cells and microglia, and in the peripheral
+    nervous system glial cells include Schwann cells and satellite cells. They have
+    four main functions: (1) To surround neurons and hold them in place (2) To supply
+    nutrients and oxygen to neurons (3) To insulate one neuron from another (4) To
+    destroy pathogens and remove dead neurons. They also play a role in neurotransmission
+    and synaptic connections,[2] and in physiological processes like breathing,[3][4]
+    .'
+  - The Mother (How I Met Your Mother) Tracy McConnell, better known as "The Mother",
+    is the title character from the CBS television sitcom How I Met Your Mother. The
+    show, narrated by Future Ted, tells the story of how Ted Mosby met The Mother.
+    Tracy McConnell appears in 8 episodes from "Lucky Penny" to "The Time Travelers"
+    as an unseen character; she was first seen fully in "Something New" and was promoted
+    to a main character in season 9. The Mother is played by Cristin Milioti.
+  - Marsupial Marsupials are any members of the mammalian infraclass Marsupialia.
+    All extant marsupials are endemic to Australasia and the Americas. A distinctive
+    characteristic common to these species is that most of the young are carried in
+    a pouch. Well-known marsupials include kangaroos, wallabies, koalas, possums,
+    opossums, wombats, and Tasmanian devils. Some lesser-known marsupials are the
+    potoroo and the quokka.
+- source_sentence: 'It was Easipower that said :'
   sentences:
-  - IBM banned the usage of the POWER5+ in its System p5 510Q, 520Q, 550Q and 560Q
-    servers.
-  - Schliemann cleared five shafts and recognized them as the graves mentioned by
-    Pausania .
-  - Schliemann recognized five shafts and cleared them like the graves mentioned by
-    Pausanias .
+  - United States presidential election However, federal law does specify that all
+    electors must be selected on the same day, which is "the first Tuesday after the
+    first Monday in November," i.e., a Tuesday no earlier than November 2 and no later
+    than November 8.[17] Today, the states and the District of Columbia each conduct
+    their own popular elections on Election Day to help determine their respective
+    slate of electors. Thus, the presidential election is really an amalgamation of
+    separate and simultaneous state elections instead of a single national election
+    run by the federal government.
+  - It is said that Easipower was ,
+  - 'It was Easipower that said :'
 pipeline_tag: sentence-similarity
 library_name: sentence-transformers
 metrics:
@@ -106,7 +132,7 @@ metrics:
 - cosine_mrr@10
 - cosine_map@100
 model-index:
-- name: SentenceTransformer based on thenlper/gte-small
+- name: SentenceTransformer based on Alibaba-NLP/gte-modernbert-base
   results:
   - task:
       type: information-retrieval
@@ -119,46 +145,46 @@ model-index:
       value: 0.24
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.46
+      value: 0.52
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.54
+      value: 0.56
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.66
+      value: 0.64
       name: Cosine Accuracy@10
     - type: cosine_precision@1
       value: 0.24
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.15333333333333332
+      value: 0.1733333333333333
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.10800000000000001
+      value: 0.11200000000000002
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.066
+      value: 0.06400000000000002
       name: Cosine Precision@10
     - type: cosine_recall@1
       value: 0.24
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.46
+      value: 0.52
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.54
+      value: 0.56
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.66
+      value: 0.64
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.44291538669701197
+      value: 0.44801117912488453
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.3744682539682539
+      value: 0.3859444444444445
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.3864402026320918
+      value: 0.39907679444975275
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -168,49 +194,49 @@ model-index:
       type: NanoNQ
     metrics:
     - type: cosine_accuracy@1
-      value: 0.24
+      value: 0.3
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.34
+      value: 0.56
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.38
+      value: 0.66
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.5
+      value: 0.78
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.24
+      value: 0.3
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.11333333333333333
+      value: 0.18666666666666668
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.08
+      value: 0.132
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.052000000000000005
+      value: 0.08199999999999999
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.22
+      value: 0.3
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.32
+      value: 0.56
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.37
+      value: 0.64
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.48
+      value: 0.76
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.3425602412795631
+      value: 0.521342140364588
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.31126984126984125
+      value: 0.44460317460317456
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.30988313931877554
+      value: 0.4511292484432019
       name: Cosine Map@100
   - task:
       type: nano-beir
@@ -220,63 +246,63 @@ model-index:
       type: NanoBEIR_mean
     metrics:
     - type: cosine_accuracy@1
-      value: 0.24
+      value: 0.27
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.4
+      value: 0.54
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.46
+      value: 0.6100000000000001
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.5800000000000001
+      value: 0.71
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.24
+      value: 0.27
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.13333333333333333
+      value: 0.18
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.094
+      value: 0.12200000000000001
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.059000000000000004
+      value: 0.07300000000000001
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.22999999999999998
+      value: 0.27
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.39
+      value: 0.54
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.455
+      value: 0.6000000000000001
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.5700000000000001
+      value: 0.7
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.39273781398828755
+      value: 0.4846766597447363
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.3428690476190476
+      value: 0.41527380952380955
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.34816167097543366
+      value: 0.4251030214464773
       name: Cosine Map@100
 ---
 
-# SentenceTransformer based on thenlper/gte-small
+# SentenceTransformer based on Alibaba-NLP/gte-modernbert-base
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [thenlper/gte-small](https://huggingface.co/thenlper/gte-small). 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.
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base). 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.
 
 ## Model Details
 
 ### Model Description
 - **Model Type:** Sentence Transformer
-- **Base model:** [thenlper/gte-small](https://huggingface.co/thenlper/gte-small) <!-- at revision 17e1f347d17fe144873b1201da91788898c639cd -->
+- **Base model:** [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) <!-- at revision e7f32e3c00f91d699e8c43b53106206bcc72bb22 -->
 - **Maximum Sequence Length:** 128 tokens
-- **Output Dimensionality:** 384 dimensions
+- **Output Dimensionality:** 768 dimensions
 - **Similarity Function:** Cosine Similarity
 <!-- - **Training Dataset:** Unknown -->
 <!-- - **Language:** Unknown -->
@@ -292,9 +318,8 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [t
 
 ```
 SentenceTransformer(
-  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False, 'architecture': 'BertModel'})
-  (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})
-  (2): Normalize()
+  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
 )
 ```
 
@@ -316,20 +341,20 @@ from sentence_transformers import SentenceTransformer
 model = SentenceTransformer("redis/model-b-structured")
 # Run inference
 sentences = [
-    'Schliemann recognized five shafts and cleared them like the graves mentioned by Pausanias .',
-    'Schliemann recognized five shafts and cleared them like the graves mentioned by Pausanias .',
-    'Schliemann cleared five shafts and recognized them as the graves mentioned by Pausania .',
+    'It was Easipower that said :',
+    'It was Easipower that said :',
+    'It is said that Easipower was ,',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
-# [3, 384]
+# [3, 768]
 
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)
 print(similarities)
-# tensor([[1.0000, 1.0000, 0.9879],
-#         [1.0000, 1.0000, 0.9879],
-#         [0.9879, 0.9879, 1.0000]])
+# tensor([[1.0001, 1.0001, 0.1242],
+#         [1.0001, 1.0001, 0.1242],
+#         [0.1242, 0.1242, 1.0001]])
 ```
 
 <!--
@@ -367,21 +392,21 @@ You can finetune this model on your own dataset.
 
 | Metric              | NanoMSMARCO | NanoNQ     |
 |:--------------------|:------------|:-----------|
-| cosine_accuracy@1   | 0.24        | 0.24       |
-| cosine_accuracy@3   | 0.46        | 0.34       |
-| cosine_accuracy@5   | 0.54        | 0.38       |
-| cosine_accuracy@10  | 0.66        | 0.5        |
-| cosine_precision@1  | 0.24        | 0.24       |
-| cosine_precision@3  | 0.1533      | 0.1133     |
-| cosine_precision@5  | 0.108       | 0.08       |
-| cosine_precision@10 | 0.066       | 0.052      |
-| cosine_recall@1     | 0.24        | 0.22       |
-| cosine_recall@3     | 0.46        | 0.32       |
-| cosine_recall@5     | 0.54        | 0.37       |
-| cosine_recall@10    | 0.66        | 0.48       |
-| **cosine_ndcg@10**  | **0.4429**  | **0.3426** |
-| cosine_mrr@10       | 0.3745      | 0.3113     |
-| cosine_map@100      | 0.3864      | 0.3099     |
+| cosine_accuracy@1   | 0.24        | 0.3        |
+| cosine_accuracy@3   | 0.52        | 0.56       |
+| cosine_accuracy@5   | 0.56        | 0.66       |
+| cosine_accuracy@10  | 0.64        | 0.78       |
+| cosine_precision@1  | 0.24        | 0.3        |
+| cosine_precision@3  | 0.1733      | 0.1867     |
+| cosine_precision@5  | 0.112       | 0.132      |
+| cosine_precision@10 | 0.064       | 0.082      |
+| cosine_recall@1     | 0.24        | 0.3        |
+| cosine_recall@3     | 0.52        | 0.56       |
+| cosine_recall@5     | 0.56        | 0.64       |
+| cosine_recall@10    | 0.64        | 0.76       |
+| **cosine_ndcg@10**  | **0.448**   | **0.5213** |
+| cosine_mrr@10       | 0.3859      | 0.4446     |
+| cosine_map@100      | 0.3991      | 0.4511     |
 
 #### Nano BEIR
 
@@ -399,21 +424,21 @@ You can finetune this model on your own dataset.
 
 | Metric              | Value      |
 |:--------------------|:-----------|
-| cosine_accuracy@1   | 0.24       |
-| cosine_accuracy@3   | 0.4        |
-| cosine_accuracy@5   | 0.46       |
-| cosine_accuracy@10  | 0.58       |
-| cosine_precision@1  | 0.24       |
-| cosine_precision@3  | 0.1333     |
-| cosine_precision@5  | 0.094      |
-| cosine_precision@10 | 0.059      |
-| cosine_recall@1     | 0.23       |
-| cosine_recall@3     | 0.39       |
-| cosine_recall@5     | 0.455      |
-| cosine_recall@10    | 0.57       |
-| **cosine_ndcg@10**  | **0.3927** |
-| cosine_mrr@10       | 0.3429     |
-| cosine_map@100      | 0.3482     |
+| cosine_accuracy@1   | 0.27       |
+| cosine_accuracy@3   | 0.54       |
+| cosine_accuracy@5   | 0.61       |
+| cosine_accuracy@10  | 0.71       |
+| cosine_precision@1  | 0.27       |
+| cosine_precision@3  | 0.18       |
+| cosine_precision@5  | 0.122      |
+| cosine_precision@10 | 0.073      |
+| cosine_recall@1     | 0.27       |
+| cosine_recall@3     | 0.54       |
+| cosine_recall@5     | 0.6        |
+| cosine_recall@10    | 0.7        |
+| **cosine_ndcg@10**  | **0.4847** |
+| cosine_mrr@10       | 0.4153     |
+| cosine_map@100      | 0.4251     |
 
 <!--
 ## Bias, Risks and Limitations
@@ -439,13 +464,13 @@ You can finetune this model on your own dataset.
   |         | anchor                                                                            | positive                                                                           | negative                                                                           |
   |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
   | type    | string                                                                            | string                                                                             | string                                                                             |
-  | details | <ul><li>min: 4 tokens</li><li>mean: 10.95 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 67.57 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 66.64 tokens</li><li>max: 128 tokens</li></ul> |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 13.83 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 91.42 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 90.36 tokens</li><li>max: 128 tokens</li></ul> |
 * Samples:
-  | anchor                                                                | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
-  |:----------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-  | <code>how far is sandos caracol eco resort from cancun airport</code> | <code>The Sandos Caracol Eco Resort is 2 miles from the Church of Guadalupe and a 45-minute drive from Cancun Cancún. Airport The Gran Coral Golf Riviera maya is located within the same estate as The. Sandos we speak your! Language Hotel: rooms, 680 Hotel: Chain Sandos & Hotels. resorts</code>                                                                                                                                                                                                                                                                | <code>Featuring a spa, 8 restaurants and 2 outdoor pools, Sandos Caracol Eco Resort is set on Playa del Carmen Beach, overlooking Cozumel Island. Its rooms have balconies overlooking the Caribbean Sea. Sandos Caracol Eco Resort is in beautiful gardens and features bright accommodations.</code>                                                                                                                                                                                                                                       |
-  | <code>can eggs expire</code>                                          | <code>Here is a link from Georgia Eggs Commission about eggs and expiration dates. The following is from Swedish Medical Center Eggs: If you ve purchased a carton of eggs before the date expires, you should be able to use them safely for three to five weeks after expiration.ere is a link from Georgia Eggs Commission about eggs and expiration dates. The following is from Swedish Medical Center Eggs: If you ve purchased a carton of eggs before the date expires, you should be able to use them safely for three to five weeks after expiration.</code> | <code>The answer to this question may surprise you: while uncooked eggs typically last four to five weeks when properly refrigerated, hard-boiled eggs will only last about a week. This is because egg shells, which are highly porous, are sprayed before sale with a thin coating of mineral oil that seals the egg.</code>                                                                                                                                                                                                               |
-  | <code>how old are first graders?</code>                               | <code>First Grade Worksheets Online. 6 and 7 year old kids get their first taste of real schooling in first grade. Help children learn the basics in math, reading, language and science with our printable first grade worksheets. Spelling Worksheets for 1st Grade.</code>                                                                                                                                                                                                                                                                                          | <code>Average BMI percentile-for-age values were 59.5 (28.8) for first-graders, 59.5 (30.5) for third-graders, and 62.4 (31.7) for fifth-graders. The number of participants classified as obese was 144 (25.6% of first-graders, 28.5% of third-graders, and 34.5% of fifth-graders). The percentage of students who reported a reasonable height or weight ranged from 20% (first grade, height) to 92% (fifth grade, weight) (Table). In general, self-report ability was better in older children and when self-reporting weight.</code> |
+  | anchor                                                                                                  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            | negative                                                                                                                                                                                                                                                                                                                                                                                                                               |
+  |:--------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>which state is home to the arizona ice tea beverage company</code>                                | <code>Arizona Beverage Company Arizona Beverages USA (stylized as AriZona) is an American producer of many flavors of iced tea, juice cocktails and energy drinks based in Woodbury, New York.[2] Arizona's first product was made available in 1992.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | <code>Arya Vaishya Arya Vaishya (Arya Vysya) is an Indian caste. Orthodox Arya Vaishyas follow rituals prescribed in the Vasavi Puranam, a religious text written in the late Middle Ages. Their kuladevata is Vasavi. The community were formerly known as Komati Chettiars in Tamil Nadu but now prefer to be referred to as Arya Vaishya.[1]</code>                                                                                 |
+  | <code>when were afro-american and africana studies programs founded in colleges and universities</code> | <code>African-American studies Programs and departments of African-American studies were first created in the 1960s and 1970s as a result of inter-ethnic student and faculty activism at many universities, sparked by a five-month strike for black studies at San Francisco State. In February 1968, San Francisco State hired sociologist Nathan Hare to coordinate the first black studies program and write a proposal for the first Department of Black Studies; the department was created in September 1968 and gained official status at the end of the five-months strike in the spring of 1969. The creation of programs and departments in Black studies was a common demand of protests and sit-ins by minority students and their allies, who felt that their cultures and interests were underserved by the traditional academic structures.</code> | <code>Maze Runner: The Death Cure Maze Runner: The Death Cure was originally set to be released on February 17, 2017, in the United States by 20th Century Fox, but the studio rescheduled the film's release for January 26, 2018 in theatres and IMAX, allowing time for O'Brien to recover from injuries he sustained during filming. The film received mixed reviews from critics and grossed over $284 million worldwide.</code> |
+  | <code>who recorded the song total eclipse of the heart</code>                                           | <code>Bonnie Tyler Bonnie Tyler (born Gaynor Hopkins; 8 June 1951) is a Welsh singer, known for her distinctive husky voice. Tyler came to prominence with the release of her 1977 album The World Starts Tonight and its singles "Lost in France" and "More Than a Lover". Her 1978 single "It's a Heartache" reached number four on the UK Singles Chart, and number three on the US Billboard Hot 100.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                    | <code>Manny Pacquiao vs. Juan Manuel Márquez IV Marquez defeated Pacquiao by knockout with one second remaining in the sixth round. It was named Fight of the Year and Knockout of the Year by Ring Magazine, with round five garnering Round of the Year honors.[2]</code>                                                                                                                                                            |
 * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
   ```json
   {
@@ -465,13 +490,13 @@ You can finetune this model on your own dataset.
   |         | anchor                                                                            | positive                                                                           | negative                                                                           |
   |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
   | type    | string                                                                            | string                                                                             | string                                                                             |
-  | details | <ul><li>min: 4 tokens</li><li>mean: 11.11 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 67.99 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 66.08 tokens</li><li>max: 128 tokens</li></ul> |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 13.69 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 90.17 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 89.67 tokens</li><li>max: 128 tokens</li></ul> |
 * Samples:
-  | anchor                                                                                                    | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
-  |:----------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-  | <code>In 1883 , the first schools were built in the vicinity for 400 white and 60 black students .</code> | <code>In 1883 , the first schools were built in the vicinity for 400 white and 60 black students .</code>                                                                                                                                                                                                                                                                                                                                                                                                                                               | <code>In 1883 , the first schools in the area were built for 400 black students and 60 white students .</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
-  | <code>what is the origin of the name haja</code>                                                          | <code>Haja is a Muslim baby Girl name, it is an Arabic originated name. Haja name meaning is In the heart condition through and the lucky number associated with Haja is 5. Find all the relevant details about the Haja Meaning, Origin, Lucky Number and Religion from this page. Average rating of Haja is 1 stars, based on 0 reviews.</code>                                                                                                                                                                                                       | <code>Synonomis with the exclamation commonly used in urban circles Holla. Haba is derived from the term, Holla Bitches, which became Haba Litches, which eventually evolved to Habalicious, and finally became just Haba. When seeing a fine female passing by, Russell exclaimed, Haba.</code>                                                                                                                                                                                                                                                                                                              |
-  | <code>what causes itch rash</code>                                                                        | <code>A rash is a noticeable change in the texture or color of the skin. The skin may become itchy, bumpy, chapped, scaly, or otherwise irritated. Rashes are caused by a wide range of conditions, including allergies, medication, cosmetics, and various diseases. The rash is often reddish and itchy, with a scaly texture. 2  bug bites: tick bites are of particular concern, as they can transmit disease. 3  psoriasis: a scaly, itchy, red rash that forms along the scalp and joints. 4  dandruff: an itchy, flaky rash on the scalp.</code> | <code>Causes of Similar Symptoms to Behind knee rash. Research the causes of these symptoms that are similar to, or related to, the symptom Behind knee rash: 1  Behind knee itch (14 causes). 2  Knee rash (18 causes).3  Knee pain (122 causes). 4  Knee tingling (6 causes). 5  Knee symptoms (149 causes). 6  Skin itch (1068 causes). 7  Skin rash (461 causes). 8  Insect bite.auses of Similar Symptoms to Behind knee rash. Research the causes of these symptoms that are similar to, or related to, the symptom Behind knee rash: 1  Behind knee itch (14 causes). 2  Knee rash (18 causes).</code> |
+  | anchor                                                                                                                                  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+  |:----------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>In early July , Steve Whitley , the criminal father of Harper Whitley and Garrett Whitley , and brother of Benny Cameron .</code> | <code>In early July , Steve Whitley , the criminal father of Harper Whitley and Garrett Whitley , and brother of Benny Cameron .</code>                                                                                                                                                                                                                                                                                                                                                                                                                          | <code>In early July , Garrett Whitley , who is the criminal father of Harper Whitley and Steve Whitley , and the brother of Benny Cameron , appeared .</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+  | <code>when will the next season of house of cards be released</code>                                                                    | <code>House of Cards (season 6) The sixth and final season of the American political drama web television series House of Cards was confirmed by Netflix on December 4, 2017, and is scheduled to be released on November 2, 2018. Unlike previous seasons that consisted of thirteen episodes each, the sixth season will consist of only eight. The season will not include former lead actor Kevin Spacey, who was fired from the show due to sexual misconduct allegations.</code>                                                                           | <code>Wild 'n Out For the first four seasons, the show filmed from Los Angeles/Hollywood and aired on MTV. The first run episodes were suspended as Mr. Renaissance Entertainment became Ncredible Entertainment in 2012. Upon being revived in 2012, the show was produced in New York City and aired on MTV2 during Seasons 5–7, it also returned to that location for Season 9. In 2016, the show returned to airing new episodes on MTV and also for the first time since Season 4, production is in Los Angeles.</code>                                                                                                                                                                                                                                                                                                                                                                  |
+  | <code>who played the father on father knows best</code>                                                                                 | <code>Father Knows Best The series began August 25, 1949, on NBC Radio. Set in the Midwest, it starred Robert Young as the General Insurance agent Jim Anderson. His wife Margaret was first portrayed by June Whitley and later by Jean Vander Pyl. The Anderson children were Betty (Rhoda Williams), Bud (Ted Donaldson), and Kathy (Norma Jean Nilsson). Others in the cast were Eleanor Audley, Herb Vigran and Sam Edwards. Sponsored through most of its run by General Foods, the series was heard Thursday evenings on NBC until March 25, 1954.</code> | <code>List of To Kill a Mockingbird characters Maycomb children believe he is a horrible person, due to the rumors spread about him and a trial he underwent as a teenager. It is implied during the story that Boo is a very lonely man who attempts to reach out to Jem and Scout for love and friendship, such as leaving them small gifts and figures in a tree knothole. Scout finally meets him at the very end of the book, when he saves the children's lives from Bob Ewell. Scout describes him as being sickly white, with a thin mouth, thin and feathery hair, and grey eyes, almost as if he were blind. During the same night, when Boo whispers to Scout to walk him back to the Radley house, Scout takes a moment to picture what it would be like to be Boo Radley. While standing on his porch, she realizes his "exile" inside his house is really not that lonely.</code> |
 * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
   ```json
   {
@@ -487,9 +512,9 @@ You can finetune this model on your own dataset.
 - `eval_strategy`: steps
 - `per_device_train_batch_size`: 128
 - `per_device_eval_batch_size`: 128
-- `learning_rate`: 8e-05
-- `weight_decay`: 0.005
-- `max_steps`: 3375
+- `learning_rate`: 4e-05
+- `weight_decay`: 0.01
+- `max_steps`: 703
 - `warmup_ratio`: 0.1
 - `fp16`: True
 - `dataloader_drop_last`: True
@@ -516,14 +541,14 @@ You can finetune this model on your own dataset.
 - `gradient_accumulation_steps`: 1
 - `eval_accumulation_steps`: None
 - `torch_empty_cache_steps`: None
-- `learning_rate`: 8e-05
-- `weight_decay`: 0.005
+- `learning_rate`: 4e-05
+- `weight_decay`: 0.01
 - `adam_beta1`: 0.9
 - `adam_beta2`: 0.999
 - `adam_epsilon`: 1e-08
 - `max_grad_norm`: 1.0
 - `num_train_epochs`: 3.0
-- `max_steps`: 3375
+- `max_steps`: 703
 - `lr_scheduler_type`: linear
 - `lr_scheduler_kwargs`: {}
 - `warmup_ratio`: 0.1
@@ -630,20 +655,9 @@ You can finetune this model on your own dataset.
 ### Training Logs
 | Epoch  | Step | Training Loss | Validation Loss | NanoMSMARCO_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
 |:------:|:----:|:-------------:|:---------------:|:--------------------------:|:---------------------:|:----------------------------:|
-| 0      | 0    | -             | 4.9037          | 0.6259                     | 0.6583                | 0.6421                       |
-| 0.2874 | 250  | 3.7176        | 3.0286          | 0.5347                     | 0.4519                | 0.4933                       |
-| 0.5747 | 500  | 3.111         | 2.9929          | 0.4499                     | 0.4041                | 0.4270                       |
-| 0.8621 | 750  | 3.0734        | 2.9741          | 0.4816                     | 0.3752                | 0.4284                       |
-| 1.1494 | 1000 | 3.0287        | 2.9680          | 0.4802                     | 0.3422                | 0.4112                       |
-| 1.4368 | 1250 | 3.0024        | 2.9618          | 0.4850                     | 0.3506                | 0.4178                       |
-| 1.7241 | 1500 | 2.9962        | 2.9568          | 0.4677                     | 0.3843                | 0.4260                       |
-| 2.0115 | 1750 | 2.9903        | 2.9532          | 0.4694                     | 0.3430                | 0.4062                       |
-| 2.2989 | 2000 | 2.9473        | 2.9527          | 0.4446                     | 0.3497                | 0.3972                       |
-| 2.5862 | 2250 | 2.9458        | 2.9519          | 0.4300                     | 0.3340                | 0.3820                       |
-| 2.8736 | 2500 | 2.9392        | 2.9500          | 0.4570                     | 0.3466                | 0.4018                       |
-| 3.1609 | 2750 | 2.9292        | 2.9500          | 0.4529                     | 0.3474                | 0.4001                       |
-| 3.4483 | 3000 | 2.9165        | 2.9502          | 0.4507                     | 0.3365                | 0.3936                       |
-| 3.7356 | 3250 | 2.9151        | 2.9491          | 0.4429                     | 0.3426                | 0.3927                       |
+| 0      | 0    | -             | 4.3452          | 0.6530                     | 0.6552                | 0.6541                       |
+| 0.2874 | 250  | 3.1166        | 2.9191          | 0.4629                     | 0.5508                | 0.5069                       |
+| 0.5747 | 500  | 2.9043        | 2.8945          | 0.4480                     | 0.5213                | 0.4847                       |
 
 
 ### Framework Versions

config_sentence_transformers.json

@@ -1,5 +1,4 @@
 {
-  "model_type": "SentenceTransformer",
   "__version__": {
     "sentence_transformers": "5.2.0",
     "transformers": "4.57.3",
@@ -10,5 +9,6 @@
     "document": ""
   },
   "default_prompt_name": null,
-  "similarity_fn_name": "cosine"
+  "similarity_fn_name": "cosine",
+  "model_type": "SentenceTransformer"
 }

modules.json

@@ -10,11 +10,5 @@
     "name": "1",
     "path": "1_Pooling",
     "type": "sentence_transformers.models.Pooling"
-  },
-  {
-    "idx": 2,
-    "name": "2",
-    "path": "2_Normalize",
-    "type": "sentence_transformers.models.Normalize"
   }
 ]