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README.md
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---
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language:
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- en
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license: apache-2.0
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library_name: sentence-transformers
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tags:
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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- dataset_size:1M<n<10M
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- loss:MultipleNegativesRankingLoss
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base_model: microsoft/mpnet-base
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datasets:
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- sentence-transformers/gooaq
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metrics:
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- cosine_accuracy@1
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- cosine_accuracy@3
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- cosine_accuracy@5
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- cosine_accuracy@10
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- cosine_precision@1
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- cosine_precision@3
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- cosine_precision@5
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- cosine_precision@10
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- cosine_recall@1
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- cosine_recall@3
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- cosine_recall@5
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- cosine_recall@10
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- cosine_ndcg@10
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- cosine_mrr@10
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- cosine_map@100
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- dot_accuracy@1
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- dot_accuracy@3
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- dot_accuracy@5
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- dot_accuracy@10
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- dot_precision@1
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- dot_precision@3
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- dot_precision@5
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- dot_precision@10
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- dot_recall@1
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- dot_recall@3
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- dot_recall@5
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- dot_recall@10
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- dot_ndcg@10
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- dot_mrr@10
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- dot_map@100
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widget:
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- source_sentence:
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sentences:
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|
| 609 |
-
| 0.
|
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-
| 0.
|
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-
| 0.
|
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-
| 0.
|
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-
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|
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|
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|
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|
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|
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|
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-
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|
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|
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|
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-
| 0.
|
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-
| 0.
|
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-
| 0.
|
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|
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| 0.
|
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-
| 0.
|
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|
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-
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|
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| 0.
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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-
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|
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-
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|
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-
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
| 700 |
-
|
|
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-
|
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|
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-
|
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-
|
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-
|
| 711 |
-
|
| 712 |
-
|
| 713 |
-
- **
|
| 714 |
-
- **
|
| 715 |
-
|
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|
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-
|
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-
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|
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-
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-
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|
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|
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|
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|
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|
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|
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|
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|
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|
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-
|
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-
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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-
|
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-
|
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-
|
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-
|
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|
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-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 770 |
-->
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
license: apache-2.0
|
| 5 |
+
library_name: sentence-transformers
|
| 6 |
+
tags:
|
| 7 |
+
- sentence-transformers
|
| 8 |
+
- sentence-similarity
|
| 9 |
+
- feature-extraction
|
| 10 |
+
- dataset_size:1M<n<10M
|
| 11 |
+
- loss:MultipleNegativesRankingLoss
|
| 12 |
+
base_model: microsoft/mpnet-base
|
| 13 |
+
datasets:
|
| 14 |
+
- sentence-transformers/gooaq
|
| 15 |
+
metrics:
|
| 16 |
+
- cosine_accuracy@1
|
| 17 |
+
- cosine_accuracy@3
|
| 18 |
+
- cosine_accuracy@5
|
| 19 |
+
- cosine_accuracy@10
|
| 20 |
+
- cosine_precision@1
|
| 21 |
+
- cosine_precision@3
|
| 22 |
+
- cosine_precision@5
|
| 23 |
+
- cosine_precision@10
|
| 24 |
+
- cosine_recall@1
|
| 25 |
+
- cosine_recall@3
|
| 26 |
+
- cosine_recall@5
|
| 27 |
+
- cosine_recall@10
|
| 28 |
+
- cosine_ndcg@10
|
| 29 |
+
- cosine_mrr@10
|
| 30 |
+
- cosine_map@100
|
| 31 |
+
- dot_accuracy@1
|
| 32 |
+
- dot_accuracy@3
|
| 33 |
+
- dot_accuracy@5
|
| 34 |
+
- dot_accuracy@10
|
| 35 |
+
- dot_precision@1
|
| 36 |
+
- dot_precision@3
|
| 37 |
+
- dot_precision@5
|
| 38 |
+
- dot_precision@10
|
| 39 |
+
- dot_recall@1
|
| 40 |
+
- dot_recall@3
|
| 41 |
+
- dot_recall@5
|
| 42 |
+
- dot_recall@10
|
| 43 |
+
- dot_ndcg@10
|
| 44 |
+
- dot_mrr@10
|
| 45 |
+
- dot_map@100
|
| 46 |
+
widget:
|
| 47 |
+
- source_sentence: 11 is what of 8?
|
| 48 |
+
sentences:
|
| 49 |
+
- '*RARE* CANDY AXE AND RED NOSED RAIDER IS BACK - FORTNITE ITEM SHOP 8TH DECEMBER
|
| 50 |
+
2019.'
|
| 51 |
+
- 'Convert fraction (ratio) 8 / 11 Answer: 72.727272727273%'
|
| 52 |
+
- Old-age pensions are not included in taxable income under the personal income
|
| 53 |
+
tax.
|
| 54 |
+
- source_sentence: is 50 shades of grey on prime?
|
| 55 |
+
sentences:
|
| 56 |
+
- 'Amazon.com: Watch Fifty Shades of Grey. Prime Video.'
|
| 57 |
+
- 'How much is 22 out of 100 written as a percentage? Convert fraction (ratio) 22
|
| 58 |
+
/ 100 Answer: 22%'
|
| 59 |
+
- Petco ferrets are neutered and as social animals, they enjoy each other's company.
|
| 60 |
+
- source_sentence: 20 of what is 18?
|
| 61 |
+
sentences:
|
| 62 |
+
- '20 percent (calculated percentage %) of what number equals 18? Answer: 90.'
|
| 63 |
+
- There are 3.35 x 1019 H2O molecules in a 1 mg snowflake.
|
| 64 |
+
- There are 104 total Power Moons and 100 Purple Coins in the Mushroom Kingdom.
|
| 65 |
+
- source_sentence: 63 up itv when is it on?
|
| 66 |
+
sentences:
|
| 67 |
+
- Mark Twain Quotes If you tell the truth, you don't have to remember anything.
|
| 68 |
+
- 63 Up is on ITV for three consecutive nights, Tuesday 4 – Thursday 6 June, at
|
| 69 |
+
9pm.
|
| 70 |
+
- In a language, the smallest units of meaning are. Morphemes.
|
| 71 |
+
- source_sentence: what is ikit in tagalog?
|
| 72 |
+
sentences:
|
| 73 |
+
- 'Definition: aunt. the sister of one''s father or mother; the wife of one''s uncle
|
| 74 |
+
(n.)'
|
| 75 |
+
- 'How much is 12 out of 29 written as a percentage? Convert fraction (ratio) 12
|
| 76 |
+
/ 29 Answer: 41.379310344828%'
|
| 77 |
+
- Iberia offers Wi-Fi on all long-haul aircraft so that you can stay connected using
|
| 78 |
+
your own devices.
|
| 79 |
+
pipeline_tag: sentence-similarity
|
| 80 |
+
co2_eq_emissions:
|
| 81 |
+
emissions: 636.2415070661234
|
| 82 |
+
energy_consumed: 1.636836206312608
|
| 83 |
+
source: codecarbon
|
| 84 |
+
training_type: fine-tuning
|
| 85 |
+
on_cloud: false
|
| 86 |
+
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
|
| 87 |
+
ram_total_size: 31.777088165283203
|
| 88 |
+
hours_used: 4.514
|
| 89 |
+
hardware_used: 1 x NVIDIA GeForce RTX 3090
|
| 90 |
+
model-index:
|
| 91 |
+
- name: MPNet base trained on GooAQ triplets
|
| 92 |
+
results:
|
| 93 |
+
- task:
|
| 94 |
+
type: information-retrieval
|
| 95 |
+
name: Information Retrieval
|
| 96 |
+
dataset:
|
| 97 |
+
name: gooaq dev
|
| 98 |
+
type: gooaq-dev
|
| 99 |
+
metrics:
|
| 100 |
+
- type: cosine_accuracy@1
|
| 101 |
+
value: 0.7198
|
| 102 |
+
name: Cosine Accuracy@1
|
| 103 |
+
- type: cosine_accuracy@3
|
| 104 |
+
value: 0.884
|
| 105 |
+
name: Cosine Accuracy@3
|
| 106 |
+
- type: cosine_accuracy@5
|
| 107 |
+
value: 0.9305
|
| 108 |
+
name: Cosine Accuracy@5
|
| 109 |
+
- type: cosine_accuracy@10
|
| 110 |
+
value: 0.9709
|
| 111 |
+
name: Cosine Accuracy@10
|
| 112 |
+
- type: cosine_precision@1
|
| 113 |
+
value: 0.7198
|
| 114 |
+
name: Cosine Precision@1
|
| 115 |
+
- type: cosine_precision@3
|
| 116 |
+
value: 0.29466666666666663
|
| 117 |
+
name: Cosine Precision@3
|
| 118 |
+
- type: cosine_precision@5
|
| 119 |
+
value: 0.1861
|
| 120 |
+
name: Cosine Precision@5
|
| 121 |
+
- type: cosine_precision@10
|
| 122 |
+
value: 0.09709000000000002
|
| 123 |
+
name: Cosine Precision@10
|
| 124 |
+
- type: cosine_recall@1
|
| 125 |
+
value: 0.7198
|
| 126 |
+
name: Cosine Recall@1
|
| 127 |
+
- type: cosine_recall@3
|
| 128 |
+
value: 0.884
|
| 129 |
+
name: Cosine Recall@3
|
| 130 |
+
- type: cosine_recall@5
|
| 131 |
+
value: 0.9305
|
| 132 |
+
name: Cosine Recall@5
|
| 133 |
+
- type: cosine_recall@10
|
| 134 |
+
value: 0.9709
|
| 135 |
+
name: Cosine Recall@10
|
| 136 |
+
- type: cosine_ndcg@10
|
| 137 |
+
value: 0.8490972112228806
|
| 138 |
+
name: Cosine Ndcg@10
|
| 139 |
+
- type: cosine_mrr@10
|
| 140 |
+
value: 0.8095713888888812
|
| 141 |
+
name: Cosine Mrr@10
|
| 142 |
+
- type: cosine_map@100
|
| 143 |
+
value: 0.8111457785591406
|
| 144 |
+
name: Cosine Map@100
|
| 145 |
+
- type: dot_accuracy@1
|
| 146 |
+
value: 0.7073
|
| 147 |
+
name: Dot Accuracy@1
|
| 148 |
+
- type: dot_accuracy@3
|
| 149 |
+
value: 0.877
|
| 150 |
+
name: Dot Accuracy@3
|
| 151 |
+
- type: dot_accuracy@5
|
| 152 |
+
value: 0.9244
|
| 153 |
+
name: Dot Accuracy@5
|
| 154 |
+
- type: dot_accuracy@10
|
| 155 |
+
value: 0.9669
|
| 156 |
+
name: Dot Accuracy@10
|
| 157 |
+
- type: dot_precision@1
|
| 158 |
+
value: 0.7073
|
| 159 |
+
name: Dot Precision@1
|
| 160 |
+
- type: dot_precision@3
|
| 161 |
+
value: 0.2923333333333333
|
| 162 |
+
name: Dot Precision@3
|
| 163 |
+
- type: dot_precision@5
|
| 164 |
+
value: 0.18488000000000002
|
| 165 |
+
name: Dot Precision@5
|
| 166 |
+
- type: dot_precision@10
|
| 167 |
+
value: 0.09669000000000003
|
| 168 |
+
name: Dot Precision@10
|
| 169 |
+
- type: dot_recall@1
|
| 170 |
+
value: 0.7073
|
| 171 |
+
name: Dot Recall@1
|
| 172 |
+
- type: dot_recall@3
|
| 173 |
+
value: 0.877
|
| 174 |
+
name: Dot Recall@3
|
| 175 |
+
- type: dot_recall@5
|
| 176 |
+
value: 0.9244
|
| 177 |
+
name: Dot Recall@5
|
| 178 |
+
- type: dot_recall@10
|
| 179 |
+
value: 0.9669
|
| 180 |
+
name: Dot Recall@10
|
| 181 |
+
- type: dot_ndcg@10
|
| 182 |
+
value: 0.8412144933973646
|
| 183 |
+
name: Dot Ndcg@10
|
| 184 |
+
- type: dot_mrr@10
|
| 185 |
+
value: 0.8004067857142795
|
| 186 |
+
name: Dot Mrr@10
|
| 187 |
+
- type: dot_map@100
|
| 188 |
+
value: 0.8022667466578848
|
| 189 |
+
name: Dot Map@100
|
| 190 |
+
---
|
| 191 |
+
|
| 192 |
+
# MPNet base trained on GooAQ triplets
|
| 193 |
+
|
| 194 |
+
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
|
| 195 |
+
|
| 196 |
+
## Model Details
|
| 197 |
+
|
| 198 |
+
### Model Description
|
| 199 |
+
- **Model Type:** Sentence Transformer
|
| 200 |
+
- **Base model:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
|
| 201 |
+
- **Maximum Sequence Length:** 512 tokens
|
| 202 |
+
- **Output Dimensionality:** 768 tokens
|
| 203 |
+
- **Similarity Function:** Cosine Similarity
|
| 204 |
+
- **Training Dataset:**
|
| 205 |
+
- [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq)
|
| 206 |
+
- **Language:** en
|
| 207 |
+
- **License:** apache-2.0
|
| 208 |
+
|
| 209 |
+
### Model Sources
|
| 210 |
+
|
| 211 |
+
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
|
| 212 |
+
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
|
| 213 |
+
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
|
| 214 |
+
|
| 215 |
+
### Full Model Architecture
|
| 216 |
+
|
| 217 |
+
```
|
| 218 |
+
SentenceTransformer(
|
| 219 |
+
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
|
| 220 |
+
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 221 |
+
)
|
| 222 |
+
```
|
| 223 |
+
|
| 224 |
+
## Usage
|
| 225 |
+
|
| 226 |
+
### Direct Usage (Sentence Transformers)
|
| 227 |
+
|
| 228 |
+
First install the Sentence Transformers library:
|
| 229 |
+
|
| 230 |
+
```bash
|
| 231 |
+
pip install -U sentence-transformers
|
| 232 |
+
```
|
| 233 |
+
|
| 234 |
+
Then you can load this model and run inference.
|
| 235 |
+
```python
|
| 236 |
+
from sentence_transformers import SentenceTransformer
|
| 237 |
+
|
| 238 |
+
# Download from the 🤗 Hub
|
| 239 |
+
model = SentenceTransformer("tomaarsen/mpnet-base-gooaq")
|
| 240 |
+
# Run inference
|
| 241 |
+
sentences = [
|
| 242 |
+
'11 is what of 8?',
|
| 243 |
+
'Convert fraction (ratio) 8 / 11 Answer: 72.727272727273%',
|
| 244 |
+
'Old-age pensions are not included in taxable income under the personal income tax.',
|
| 245 |
+
]
|
| 246 |
+
embeddings = model.encode(sentences)
|
| 247 |
+
print(embeddings.shape)
|
| 248 |
+
# [3, 768]
|
| 249 |
+
|
| 250 |
+
# Get the similarity scores for the embeddings
|
| 251 |
+
similarities = model.similarity(embeddings, embeddings)
|
| 252 |
+
print(similarities.shape)
|
| 253 |
+
# [3, 3]
|
| 254 |
+
```
|
| 255 |
+
|
| 256 |
+
<!--
|
| 257 |
+
### Direct Usage (Transformers)
|
| 258 |
+
|
| 259 |
+
<details><summary>Click to see the direct usage in Transformers</summary>
|
| 260 |
+
|
| 261 |
+
</details>
|
| 262 |
+
-->
|
| 263 |
+
|
| 264 |
+
<!--
|
| 265 |
+
### Downstream Usage (Sentence Transformers)
|
| 266 |
+
|
| 267 |
+
You can finetune this model on your own dataset.
|
| 268 |
+
|
| 269 |
+
<details><summary>Click to expand</summary>
|
| 270 |
+
|
| 271 |
+
</details>
|
| 272 |
+
-->
|
| 273 |
+
|
| 274 |
+
<!--
|
| 275 |
+
### Out-of-Scope Use
|
| 276 |
+
|
| 277 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
| 278 |
+
-->
|
| 279 |
+
|
| 280 |
+
## Evaluation
|
| 281 |
+
|
| 282 |
+
### Metrics
|
| 283 |
+
|
| 284 |
+
#### Information Retrieval
|
| 285 |
+
* Dataset: `gooaq-dev`
|
| 286 |
+
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
| 287 |
+
|
| 288 |
+
| Metric | Value |
|
| 289 |
+
|:--------------------|:-----------|
|
| 290 |
+
| cosine_accuracy@1 | 0.7198 |
|
| 291 |
+
| cosine_accuracy@3 | 0.884 |
|
| 292 |
+
| cosine_accuracy@5 | 0.9305 |
|
| 293 |
+
| cosine_accuracy@10 | 0.9709 |
|
| 294 |
+
| cosine_precision@1 | 0.7198 |
|
| 295 |
+
| cosine_precision@3 | 0.2947 |
|
| 296 |
+
| cosine_precision@5 | 0.1861 |
|
| 297 |
+
| cosine_precision@10 | 0.0971 |
|
| 298 |
+
| cosine_recall@1 | 0.7198 |
|
| 299 |
+
| cosine_recall@3 | 0.884 |
|
| 300 |
+
| cosine_recall@5 | 0.9305 |
|
| 301 |
+
| cosine_recall@10 | 0.9709 |
|
| 302 |
+
| cosine_ndcg@10 | 0.8491 |
|
| 303 |
+
| cosine_mrr@10 | 0.8096 |
|
| 304 |
+
| **cosine_map@100** | **0.8111** |
|
| 305 |
+
| dot_accuracy@1 | 0.7073 |
|
| 306 |
+
| dot_accuracy@3 | 0.877 |
|
| 307 |
+
| dot_accuracy@5 | 0.9244 |
|
| 308 |
+
| dot_accuracy@10 | 0.9669 |
|
| 309 |
+
| dot_precision@1 | 0.7073 |
|
| 310 |
+
| dot_precision@3 | 0.2923 |
|
| 311 |
+
| dot_precision@5 | 0.1849 |
|
| 312 |
+
| dot_precision@10 | 0.0967 |
|
| 313 |
+
| dot_recall@1 | 0.7073 |
|
| 314 |
+
| dot_recall@3 | 0.877 |
|
| 315 |
+
| dot_recall@5 | 0.9244 |
|
| 316 |
+
| dot_recall@10 | 0.9669 |
|
| 317 |
+
| dot_ndcg@10 | 0.8412 |
|
| 318 |
+
| dot_mrr@10 | 0.8004 |
|
| 319 |
+
| dot_map@100 | 0.8023 |
|
| 320 |
+
|
| 321 |
+
<!--
|
| 322 |
+
## Bias, Risks and Limitations
|
| 323 |
+
|
| 324 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
| 325 |
+
-->
|
| 326 |
+
|
| 327 |
+
<!--
|
| 328 |
+
### Recommendations
|
| 329 |
+
|
| 330 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
| 331 |
+
-->
|
| 332 |
+
|
| 333 |
+
## Training Details
|
| 334 |
+
|
| 335 |
+
### Training Dataset
|
| 336 |
+
|
| 337 |
+
#### sentence-transformers/gooaq
|
| 338 |
+
|
| 339 |
+
* Dataset: [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
|
| 340 |
+
* Size: 3,002,496 training samples
|
| 341 |
+
* Columns: <code>question</code> and <code>answer</code>
|
| 342 |
+
* Approximate statistics based on the first 1000 samples:
|
| 343 |
+
| | question | answer |
|
| 344 |
+
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
|
| 345 |
+
| type | string | string |
|
| 346 |
+
| details | <ul><li>min: 8 tokens</li><li>mean: 11.89 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 60.37 tokens</li><li>max: 147 tokens</li></ul> |
|
| 347 |
+
* Samples:
|
| 348 |
+
| question | answer |
|
| 349 |
+
|:-----------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
| 350 |
+
| <code>biotechnology is best defined as?</code> | <code>Biotechnology is best defined as_______________? The science that involves using living organisms to produce needed materials. Which of the following tools of biotechnology, to do investigation, is used when trying crime?</code> |
|
| 351 |
+
| <code>how to open xye file?</code> | <code>Firstly, use File then Open and make sure that you can see All Files (*. *) and not just Excel files (the default option!) in the folder containing the *. xye file: Select the file you wish to open and Excel will bring up a wizard menu for importing plain text data into Excel (as shown below).</code> |
|
| 352 |
+
| <code>how much does california spend?</code> | <code>Estimated 2016 expenditures The total estimated government spending in California in fiscal year 2016 was $265.9 billion. Per-capita figures are calculated by taking the state's total spending and dividing by the number of state residents according to United States Census Bureau estimates.</code> |
|
| 353 |
+
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
|
| 354 |
+
```json
|
| 355 |
+
{
|
| 356 |
+
"scale": 20.0,
|
| 357 |
+
"similarity_fct": "cos_sim"
|
| 358 |
+
}
|
| 359 |
+
```
|
| 360 |
+
|
| 361 |
+
### Evaluation Dataset
|
| 362 |
+
|
| 363 |
+
#### sentence-transformers/gooaq
|
| 364 |
+
|
| 365 |
+
* Dataset: [sentence-transformers/gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
|
| 366 |
+
* Size: 10,000 evaluation samples
|
| 367 |
+
* Columns: <code>question</code> and <code>answer</code>
|
| 368 |
+
* Approximate statistics based on the first 1000 samples:
|
| 369 |
+
| | question | answer |
|
| 370 |
+
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
|
| 371 |
+
| type | string | string |
|
| 372 |
+
| details | <ul><li>min: 8 tokens</li><li>mean: 11.86 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 60.82 tokens</li><li>max: 166 tokens</li></ul> |
|
| 373 |
+
* Samples:
|
| 374 |
+
| question | answer |
|
| 375 |
+
|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
| 376 |
+
| <code>how to open nx file?</code> | <code>['Click File > Open. The File Open dialog box opens.', 'Select NX File (*. prt) in the Type box. ... ', 'Select an NX . ... ', 'Select Import in the File Open dialog box. ... ', 'If you do not want to retain the import profile in use, select an import profile from the Profile list. ... ', 'Click OK in the Import New Model dialog box.']</code> |
|
| 377 |
+
| <code>how to recover deleted photos from blackberry priv?</code> | <code>['Run Android Data Recovery. ... ', 'Enable USB Debugging Mode. ... ', 'Scan Your BlackBerry PRIV to Find Deleted Photos. ... ', 'Recover Deleted Photos from BlackBerry PRIV.']</code> |
|
| 378 |
+
| <code>which subatomic particles are found within the nucleus of an atom?</code> | <code>In the middle of every atom is the nucleus. The nucleus contains two types of subatomic particles, protons and neutrons. The protons have a positive electrical charge and the neutrons have no electrical charge. A third type of subatomic particle, electrons, move around the nucleus.</code> |
|
| 379 |
+
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
|
| 380 |
+
```json
|
| 381 |
+
{
|
| 382 |
+
"scale": 20.0,
|
| 383 |
+
"similarity_fct": "cos_sim"
|
| 384 |
+
}
|
| 385 |
+
```
|
| 386 |
+
|
| 387 |
+
### Training Hyperparameters
|
| 388 |
+
#### Non-Default Hyperparameters
|
| 389 |
+
|
| 390 |
+
- `eval_strategy`: steps
|
| 391 |
+
- `per_device_train_batch_size`: 64
|
| 392 |
+
- `per_device_eval_batch_size`: 64
|
| 393 |
+
- `learning_rate`: 2e-05
|
| 394 |
+
- `num_train_epochs`: 1
|
| 395 |
+
- `warmup_ratio`: 0.1
|
| 396 |
+
- `bf16`: True
|
| 397 |
+
- `batch_sampler`: no_duplicates
|
| 398 |
+
|
| 399 |
+
#### All Hyperparameters
|
| 400 |
+
<details><summary>Click to expand</summary>
|
| 401 |
+
|
| 402 |
+
- `overwrite_output_dir`: False
|
| 403 |
+
- `do_predict`: False
|
| 404 |
+
- `eval_strategy`: steps
|
| 405 |
+
- `prediction_loss_only`: True
|
| 406 |
+
- `per_device_train_batch_size`: 64
|
| 407 |
+
- `per_device_eval_batch_size`: 64
|
| 408 |
+
- `per_gpu_train_batch_size`: None
|
| 409 |
+
- `per_gpu_eval_batch_size`: None
|
| 410 |
+
- `gradient_accumulation_steps`: 1
|
| 411 |
+
- `eval_accumulation_steps`: None
|
| 412 |
+
- `learning_rate`: 2e-05
|
| 413 |
+
- `weight_decay`: 0.0
|
| 414 |
+
- `adam_beta1`: 0.9
|
| 415 |
+
- `adam_beta2`: 0.999
|
| 416 |
+
- `adam_epsilon`: 1e-08
|
| 417 |
+
- `max_grad_norm`: 1.0
|
| 418 |
+
- `num_train_epochs`: 1
|
| 419 |
+
- `max_steps`: -1
|
| 420 |
+
- `lr_scheduler_type`: linear
|
| 421 |
+
- `lr_scheduler_kwargs`: {}
|
| 422 |
+
- `warmup_ratio`: 0.1
|
| 423 |
+
- `warmup_steps`: 0
|
| 424 |
+
- `log_level`: passive
|
| 425 |
+
- `log_level_replica`: warning
|
| 426 |
+
- `log_on_each_node`: True
|
| 427 |
+
- `logging_nan_inf_filter`: True
|
| 428 |
+
- `save_safetensors`: True
|
| 429 |
+
- `save_on_each_node`: False
|
| 430 |
+
- `save_only_model`: False
|
| 431 |
+
- `restore_callback_states_from_checkpoint`: False
|
| 432 |
+
- `no_cuda`: False
|
| 433 |
+
- `use_cpu`: False
|
| 434 |
+
- `use_mps_device`: False
|
| 435 |
+
- `seed`: 42
|
| 436 |
+
- `data_seed`: None
|
| 437 |
+
- `jit_mode_eval`: False
|
| 438 |
+
- `use_ipex`: False
|
| 439 |
+
- `bf16`: True
|
| 440 |
+
- `fp16`: False
|
| 441 |
+
- `fp16_opt_level`: O1
|
| 442 |
+
- `half_precision_backend`: auto
|
| 443 |
+
- `bf16_full_eval`: False
|
| 444 |
+
- `fp16_full_eval`: False
|
| 445 |
+
- `tf32`: None
|
| 446 |
+
- `local_rank`: 0
|
| 447 |
+
- `ddp_backend`: None
|
| 448 |
+
- `tpu_num_cores`: None
|
| 449 |
+
- `tpu_metrics_debug`: False
|
| 450 |
+
- `debug`: []
|
| 451 |
+
- `dataloader_drop_last`: False
|
| 452 |
+
- `dataloader_num_workers`: 0
|
| 453 |
+
- `dataloader_prefetch_factor`: None
|
| 454 |
+
- `past_index`: -1
|
| 455 |
+
- `disable_tqdm`: False
|
| 456 |
+
- `remove_unused_columns`: True
|
| 457 |
+
- `label_names`: None
|
| 458 |
+
- `load_best_model_at_end`: False
|
| 459 |
+
- `ignore_data_skip`: False
|
| 460 |
+
- `fsdp`: []
|
| 461 |
+
- `fsdp_min_num_params`: 0
|
| 462 |
+
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
| 463 |
+
- `fsdp_transformer_layer_cls_to_wrap`: None
|
| 464 |
+
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
| 465 |
+
- `deepspeed`: None
|
| 466 |
+
- `label_smoothing_factor`: 0.0
|
| 467 |
+
- `optim`: adamw_torch
|
| 468 |
+
- `optim_args`: None
|
| 469 |
+
- `adafactor`: False
|
| 470 |
+
- `group_by_length`: False
|
| 471 |
+
- `length_column_name`: length
|
| 472 |
+
- `ddp_find_unused_parameters`: None
|
| 473 |
+
- `ddp_bucket_cap_mb`: None
|
| 474 |
+
- `ddp_broadcast_buffers`: False
|
| 475 |
+
- `dataloader_pin_memory`: True
|
| 476 |
+
- `dataloader_persistent_workers`: False
|
| 477 |
+
- `skip_memory_metrics`: True
|
| 478 |
+
- `use_legacy_prediction_loop`: False
|
| 479 |
+
- `push_to_hub`: False
|
| 480 |
+
- `resume_from_checkpoint`: None
|
| 481 |
+
- `hub_model_id`: None
|
| 482 |
+
- `hub_strategy`: every_save
|
| 483 |
+
- `hub_private_repo`: False
|
| 484 |
+
- `hub_always_push`: False
|
| 485 |
+
- `gradient_checkpointing`: False
|
| 486 |
+
- `gradient_checkpointing_kwargs`: None
|
| 487 |
+
- `include_inputs_for_metrics`: False
|
| 488 |
+
- `eval_do_concat_batches`: True
|
| 489 |
+
- `fp16_backend`: auto
|
| 490 |
+
- `push_to_hub_model_id`: None
|
| 491 |
+
- `push_to_hub_organization`: None
|
| 492 |
+
- `mp_parameters`:
|
| 493 |
+
- `auto_find_batch_size`: False
|
| 494 |
+
- `full_determinism`: False
|
| 495 |
+
- `torchdynamo`: None
|
| 496 |
+
- `ray_scope`: last
|
| 497 |
+
- `ddp_timeout`: 1800
|
| 498 |
+
- `torch_compile`: False
|
| 499 |
+
- `torch_compile_backend`: None
|
| 500 |
+
- `torch_compile_mode`: None
|
| 501 |
+
- `dispatch_batches`: None
|
| 502 |
+
- `split_batches`: None
|
| 503 |
+
- `include_tokens_per_second`: False
|
| 504 |
+
- `include_num_input_tokens_seen`: False
|
| 505 |
+
- `neftune_noise_alpha`: None
|
| 506 |
+
- `optim_target_modules`: None
|
| 507 |
+
- `batch_eval_metrics`: False
|
| 508 |
+
- `batch_sampler`: no_duplicates
|
| 509 |
+
- `multi_dataset_batch_sampler`: proportional
|
| 510 |
+
|
| 511 |
+
</details>
|
| 512 |
+
|
| 513 |
+
### Training Logs
|
| 514 |
+
<details><summary>Click to expand</summary>
|
| 515 |
+
|
| 516 |
+
| Epoch | Step | Training Loss | loss | gooaq-dev_cosine_map@100 |
|
| 517 |
+
|:------:|:-----:|:-------------:|:------:|:------------------------:|
|
| 518 |
+
| 0 | 0 | - | - | 0.1379 |
|
| 519 |
+
| 0.0000 | 1 | 3.6452 | - | - |
|
| 520 |
+
| 0.0053 | 250 | 2.4418 | - | - |
|
| 521 |
+
| 0.0107 | 500 | 0.373 | - | - |
|
| 522 |
+
| 0.0160 | 750 | 0.183 | - | - |
|
| 523 |
+
| 0.0213 | 1000 | 0.1286 | 0.0805 | 0.6796 |
|
| 524 |
+
| 0.0266 | 1250 | 0.1099 | - | - |
|
| 525 |
+
| 0.0320 | 1500 | 0.091 | - | - |
|
| 526 |
+
| 0.0373 | 1750 | 0.0768 | - | - |
|
| 527 |
+
| 0.0426 | 2000 | 0.0665 | 0.0526 | 0.7162 |
|
| 528 |
+
| 0.0480 | 2250 | 0.0659 | - | - |
|
| 529 |
+
| 0.0533 | 2500 | 0.0602 | - | - |
|
| 530 |
+
| 0.0586 | 2750 | 0.0548 | - | - |
|
| 531 |
+
| 0.0639 | 3000 | 0.0543 | 0.0426 | 0.7328 |
|
| 532 |
+
| 0.0693 | 3250 | 0.0523 | - | - |
|
| 533 |
+
| 0.0746 | 3500 | 0.0494 | - | - |
|
| 534 |
+
| 0.0799 | 3750 | 0.0468 | - | - |
|
| 535 |
+
| 0.0853 | 4000 | 0.0494 | 0.0362 | 0.7450 |
|
| 536 |
+
| 0.0906 | 4250 | 0.048 | - | - |
|
| 537 |
+
| 0.0959 | 4500 | 0.0442 | - | - |
|
| 538 |
+
| 0.1012 | 4750 | 0.0442 | - | - |
|
| 539 |
+
| 0.1066 | 5000 | 0.0408 | 0.0332 | 0.7519 |
|
| 540 |
+
| 0.1119 | 5250 | 0.0396 | - | - |
|
| 541 |
+
| 0.1172 | 5500 | 0.0379 | - | - |
|
| 542 |
+
| 0.1226 | 5750 | 0.0392 | - | - |
|
| 543 |
+
| 0.1279 | 6000 | 0.0395 | 0.0300 | 0.7505 |
|
| 544 |
+
| 0.1332 | 6250 | 0.0349 | - | - |
|
| 545 |
+
| 0.1386 | 6500 | 0.0383 | - | - |
|
| 546 |
+
| 0.1439 | 6750 | 0.0335 | - | - |
|
| 547 |
+
| 0.1492 | 7000 | 0.0323 | 0.0253 | 0.7624 |
|
| 548 |
+
| 0.1545 | 7250 | 0.0342 | - | - |
|
| 549 |
+
| 0.1599 | 7500 | 0.0292 | - | - |
|
| 550 |
+
| 0.1652 | 7750 | 0.0309 | - | - |
|
| 551 |
+
| 0.1705 | 8000 | 0.0335 | 0.0249 | 0.7631 |
|
| 552 |
+
| 0.1759 | 8250 | 0.0304 | - | - |
|
| 553 |
+
| 0.1812 | 8500 | 0.0318 | - | - |
|
| 554 |
+
| 0.1865 | 8750 | 0.0271 | - | - |
|
| 555 |
+
| 0.1918 | 9000 | 0.029 | 0.0230 | 0.7615 |
|
| 556 |
+
| 0.1972 | 9250 | 0.0309 | - | - |
|
| 557 |
+
| 0.2025 | 9500 | 0.0305 | - | - |
|
| 558 |
+
| 0.2078 | 9750 | 0.0237 | - | - |
|
| 559 |
+
| 0.2132 | 10000 | 0.0274 | 0.0220 | 0.7667 |
|
| 560 |
+
| 0.2185 | 10250 | 0.0248 | - | - |
|
| 561 |
+
| 0.2238 | 10500 | 0.0249 | - | - |
|
| 562 |
+
| 0.2291 | 10750 | 0.0272 | - | - |
|
| 563 |
+
| 0.2345 | 11000 | 0.0289 | 0.0230 | 0.7664 |
|
| 564 |
+
| 0.2398 | 11250 | 0.027 | - | - |
|
| 565 |
+
| 0.2451 | 11500 | 0.0259 | - | - |
|
| 566 |
+
| 0.2505 | 11750 | 0.0237 | - | - |
|
| 567 |
+
| 0.2558 | 12000 | 0.0245 | 0.0220 | 0.7694 |
|
| 568 |
+
| 0.2611 | 12250 | 0.0251 | - | - |
|
| 569 |
+
| 0.2664 | 12500 | 0.0243 | - | - |
|
| 570 |
+
| 0.2718 | 12750 | 0.0229 | - | - |
|
| 571 |
+
| 0.2771 | 13000 | 0.0273 | 0.0201 | 0.7725 |
|
| 572 |
+
| 0.2824 | 13250 | 0.0244 | - | - |
|
| 573 |
+
| 0.2878 | 13500 | 0.0248 | - | - |
|
| 574 |
+
| 0.2931 | 13750 | 0.0255 | - | - |
|
| 575 |
+
| 0.2984 | 14000 | 0.0244 | 0.0192 | 0.7729 |
|
| 576 |
+
| 0.3037 | 14250 | 0.0242 | - | - |
|
| 577 |
+
| 0.3091 | 14500 | 0.0235 | - | - |
|
| 578 |
+
| 0.3144 | 14750 | 0.0231 | - | - |
|
| 579 |
+
| 0.3197 | 15000 | 0.0228 | 0.0190 | 0.7823 |
|
| 580 |
+
| 0.3251 | 15250 | 0.0229 | - | - |
|
| 581 |
+
| 0.3304 | 15500 | 0.0224 | - | - |
|
| 582 |
+
| 0.3357 | 15750 | 0.0216 | - | - |
|
| 583 |
+
| 0.3410 | 16000 | 0.0218 | 0.0186 | 0.7787 |
|
| 584 |
+
| 0.3464 | 16250 | 0.022 | - | - |
|
| 585 |
+
| 0.3517 | 16500 | 0.0233 | - | - |
|
| 586 |
+
| 0.3570 | 16750 | 0.0216 | - | - |
|
| 587 |
+
| 0.3624 | 17000 | 0.0226 | 0.0169 | 0.7862 |
|
| 588 |
+
| 0.3677 | 17250 | 0.0215 | - | - |
|
| 589 |
+
| 0.3730 | 17500 | 0.0212 | - | - |
|
| 590 |
+
| 0.3784 | 17750 | 0.0178 | - | - |
|
| 591 |
+
| 0.3837 | 18000 | 0.0217 | 0.0161 | 0.7813 |
|
| 592 |
+
| 0.3890 | 18250 | 0.0217 | - | - |
|
| 593 |
+
| 0.3943 | 18500 | 0.0191 | - | - |
|
| 594 |
+
| 0.3997 | 18750 | 0.0216 | - | - |
|
| 595 |
+
| 0.4050 | 19000 | 0.022 | 0.0157 | 0.7868 |
|
| 596 |
+
| 0.4103 | 19250 | 0.0223 | - | - |
|
| 597 |
+
| 0.4157 | 19500 | 0.021 | - | - |
|
| 598 |
+
| 0.4210 | 19750 | 0.0176 | - | - |
|
| 599 |
+
| 0.4263 | 20000 | 0.021 | 0.0162 | 0.7873 |
|
| 600 |
+
| 0.4316 | 20250 | 0.0206 | - | - |
|
| 601 |
+
| 0.4370 | 20500 | 0.0196 | - | - |
|
| 602 |
+
| 0.4423 | 20750 | 0.0186 | - | - |
|
| 603 |
+
| 0.4476 | 21000 | 0.0197 | 0.0158 | 0.7907 |
|
| 604 |
+
| 0.4530 | 21250 | 0.0156 | - | - |
|
| 605 |
+
| 0.4583 | 21500 | 0.0178 | - | - |
|
| 606 |
+
| 0.4636 | 21750 | 0.0175 | - | - |
|
| 607 |
+
| 0.4689 | 22000 | 0.0187 | 0.0151 | 0.7937 |
|
| 608 |
+
| 0.4743 | 22250 | 0.0182 | - | - |
|
| 609 |
+
| 0.4796 | 22500 | 0.0185 | - | - |
|
| 610 |
+
| 0.4849 | 22750 | 0.0217 | - | - |
|
| 611 |
+
| 0.4903 | 23000 | 0.0179 | 0.0156 | 0.7937 |
|
| 612 |
+
| 0.4956 | 23250 | 0.0193 | - | - |
|
| 613 |
+
| 0.5009 | 23500 | 0.015 | - | - |
|
| 614 |
+
| 0.5062 | 23750 | 0.0181 | - | - |
|
| 615 |
+
| 0.5116 | 24000 | 0.0173 | 0.0150 | 0.7924 |
|
| 616 |
+
| 0.5169 | 24250 | 0.0177 | - | - |
|
| 617 |
+
| 0.5222 | 24500 | 0.0183 | - | - |
|
| 618 |
+
| 0.5276 | 24750 | 0.0171 | - | - |
|
| 619 |
+
| 0.5329 | 25000 | 0.0185 | 0.0140 | 0.7955 |
|
| 620 |
+
| 0.5382 | 25250 | 0.0178 | - | - |
|
| 621 |
+
| 0.5435 | 25500 | 0.015 | - | - |
|
| 622 |
+
| 0.5489 | 25750 | 0.017 | - | - |
|
| 623 |
+
| 0.5542 | 26000 | 0.0171 | 0.0139 | 0.7931 |
|
| 624 |
+
| 0.5595 | 26250 | 0.0164 | - | - |
|
| 625 |
+
| 0.5649 | 26500 | 0.0175 | - | - |
|
| 626 |
+
| 0.5702 | 26750 | 0.0175 | - | - |
|
| 627 |
+
| 0.5755 | 27000 | 0.0163 | 0.0133 | 0.7954 |
|
| 628 |
+
| 0.5809 | 27250 | 0.0179 | - | - |
|
| 629 |
+
| 0.5862 | 27500 | 0.016 | - | - |
|
| 630 |
+
| 0.5915 | 27750 | 0.0155 | - | - |
|
| 631 |
+
| 0.5968 | 28000 | 0.0162 | 0.0138 | 0.7979 |
|
| 632 |
+
| 0.6022 | 28250 | 0.0164 | - | - |
|
| 633 |
+
| 0.6075 | 28500 | 0.0148 | - | - |
|
| 634 |
+
| 0.6128 | 28750 | 0.0152 | - | - |
|
| 635 |
+
| 0.6182 | 29000 | 0.0166 | 0.0134 | 0.7987 |
|
| 636 |
+
| 0.6235 | 29250 | 0.0159 | - | - |
|
| 637 |
+
| 0.6288 | 29500 | 0.0168 | - | - |
|
| 638 |
+
| 0.6341 | 29750 | 0.0187 | - | - |
|
| 639 |
+
| 0.6395 | 30000 | 0.017 | 0.0137 | 0.7980 |
|
| 640 |
+
| 0.6448 | 30250 | 0.0168 | - | - |
|
| 641 |
+
| 0.6501 | 30500 | 0.0149 | - | - |
|
| 642 |
+
| 0.6555 | 30750 | 0.0159 | - | - |
|
| 643 |
+
| 0.6608 | 31000 | 0.0149 | 0.0131 | 0.8017 |
|
| 644 |
+
| 0.6661 | 31250 | 0.0149 | - | - |
|
| 645 |
+
| 0.6714 | 31500 | 0.0147 | - | - |
|
| 646 |
+
| 0.6768 | 31750 | 0.0157 | - | - |
|
| 647 |
+
| 0.6821 | 32000 | 0.0151 | 0.0125 | 0.8011 |
|
| 648 |
+
| 0.6874 | 32250 | 0.015 | - | - |
|
| 649 |
+
| 0.6928 | 32500 | 0.0157 | - | - |
|
| 650 |
+
| 0.6981 | 32750 | 0.0153 | - | - |
|
| 651 |
+
| 0.7034 | 33000 | 0.0141 | 0.0123 | 0.8012 |
|
| 652 |
+
| 0.7087 | 33250 | 0.0143 | - | - |
|
| 653 |
+
| 0.7141 | 33500 | 0.0121 | - | - |
|
| 654 |
+
| 0.7194 | 33750 | 0.0164 | - | - |
|
| 655 |
+
| 0.7247 | 34000 | 0.014 | 0.0121 | 0.8014 |
|
| 656 |
+
| 0.7301 | 34250 | 0.0147 | - | - |
|
| 657 |
+
| 0.7354 | 34500 | 0.0149 | - | - |
|
| 658 |
+
| 0.7407 | 34750 | 0.014 | - | - |
|
| 659 |
+
| 0.7460 | 35000 | 0.0156 | 0.0117 | 0.8022 |
|
| 660 |
+
| 0.7514 | 35250 | 0.0153 | - | - |
|
| 661 |
+
| 0.7567 | 35500 | 0.0146 | - | - |
|
| 662 |
+
| 0.7620 | 35750 | 0.0144 | - | - |
|
| 663 |
+
| 0.7674 | 36000 | 0.0139 | 0.0111 | 0.8035 |
|
| 664 |
+
| 0.7727 | 36250 | 0.0134 | - | - |
|
| 665 |
+
| 0.7780 | 36500 | 0.013 | - | - |
|
| 666 |
+
| 0.7833 | 36750 | 0.0156 | - | - |
|
| 667 |
+
| 0.7887 | 37000 | 0.0144 | 0.0108 | 0.8048 |
|
| 668 |
+
| 0.7940 | 37250 | 0.0133 | - | - |
|
| 669 |
+
| 0.7993 | 37500 | 0.0154 | - | - |
|
| 670 |
+
| 0.8047 | 37750 | 0.0132 | - | - |
|
| 671 |
+
| 0.8100 | 38000 | 0.013 | 0.0108 | 0.8063 |
|
| 672 |
+
| 0.8153 | 38250 | 0.0126 | - | - |
|
| 673 |
+
| 0.8207 | 38500 | 0.0135 | - | - |
|
| 674 |
+
| 0.8260 | 38750 | 0.014 | - | - |
|
| 675 |
+
| 0.8313 | 39000 | 0.013 | 0.0109 | 0.8086 |
|
| 676 |
+
| 0.8366 | 39250 | 0.0136 | - | - |
|
| 677 |
+
| 0.8420 | 39500 | 0.0141 | - | - |
|
| 678 |
+
| 0.8473 | 39750 | 0.0155 | - | - |
|
| 679 |
+
| 0.8526 | 40000 | 0.0153 | 0.0106 | 0.8075 |
|
| 680 |
+
| 0.8580 | 40250 | 0.0131 | - | - |
|
| 681 |
+
| 0.8633 | 40500 | 0.0128 | - | - |
|
| 682 |
+
| 0.8686 | 40750 | 0.013 | - | - |
|
| 683 |
+
| 0.8739 | 41000 | 0.0133 | 0.0109 | 0.8060 |
|
| 684 |
+
| 0.8793 | 41250 | 0.0119 | - | - |
|
| 685 |
+
| 0.8846 | 41500 | 0.0144 | - | - |
|
| 686 |
+
| 0.8899 | 41750 | 0.0142 | - | - |
|
| 687 |
+
| 0.8953 | 42000 | 0.0138 | 0.0105 | 0.8083 |
|
| 688 |
+
| 0.9006 | 42250 | 0.014 | - | - |
|
| 689 |
+
| 0.9059 | 42500 | 0.0134 | - | - |
|
| 690 |
+
| 0.9112 | 42750 | 0.0134 | - | - |
|
| 691 |
+
| 0.9166 | 43000 | 0.0124 | 0.0106 | 0.8113 |
|
| 692 |
+
| 0.9219 | 43250 | 0.0122 | - | - |
|
| 693 |
+
| 0.9272 | 43500 | 0.0126 | - | - |
|
| 694 |
+
| 0.9326 | 43750 | 0.0121 | - | - |
|
| 695 |
+
| 0.9379 | 44000 | 0.0137 | 0.0103 | 0.8105 |
|
| 696 |
+
| 0.9432 | 44250 | 0.0132 | - | - |
|
| 697 |
+
| 0.9485 | 44500 | 0.012 | - | - |
|
| 698 |
+
| 0.9539 | 44750 | 0.0136 | - | - |
|
| 699 |
+
| 0.9592 | 45000 | 0.0133 | 0.0104 | 0.8112 |
|
| 700 |
+
| 0.9645 | 45250 | 0.0118 | - | - |
|
| 701 |
+
| 0.9699 | 45500 | 0.0132 | - | - |
|
| 702 |
+
| 0.9752 | 45750 | 0.0118 | - | - |
|
| 703 |
+
| 0.9805 | 46000 | 0.012 | 0.0102 | 0.8104 |
|
| 704 |
+
| 0.9858 | 46250 | 0.0127 | - | - |
|
| 705 |
+
| 0.9912 | 46500 | 0.0134 | - | - |
|
| 706 |
+
| 0.9965 | 46750 | 0.0121 | - | - |
|
| 707 |
+
| 1.0 | 46914 | - | - | 0.8111 |
|
| 708 |
+
|
| 709 |
+
</details>
|
| 710 |
+
|
| 711 |
+
### Environmental Impact
|
| 712 |
+
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
|
| 713 |
+
- **Energy Consumed**: 1.637 kWh
|
| 714 |
+
- **Carbon Emitted**: 0.636 kg of CO2
|
| 715 |
+
- **Hours Used**: 4.514 hours
|
| 716 |
+
|
| 717 |
+
### Training Hardware
|
| 718 |
+
- **On Cloud**: No
|
| 719 |
+
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
|
| 720 |
+
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
|
| 721 |
+
- **RAM Size**: 31.78 GB
|
| 722 |
+
|
| 723 |
+
### Framework Versions
|
| 724 |
+
- Python: 3.11.6
|
| 725 |
+
- Sentence Transformers: 3.1.0.dev0
|
| 726 |
+
- Transformers: 4.41.2
|
| 727 |
+
- PyTorch: 2.3.0+cu121
|
| 728 |
+
- Accelerate: 0.30.1
|
| 729 |
+
- Datasets: 2.19.1
|
| 730 |
+
- Tokenizers: 0.19.1
|
| 731 |
+
|
| 732 |
+
## Citation
|
| 733 |
+
|
| 734 |
+
### BibTeX
|
| 735 |
+
|
| 736 |
+
#### Sentence Transformers
|
| 737 |
+
```bibtex
|
| 738 |
+
@inproceedings{reimers-2019-sentence-bert,
|
| 739 |
+
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
| 740 |
+
author = "Reimers, Nils and Gurevych, Iryna",
|
| 741 |
+
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
| 742 |
+
month = "11",
|
| 743 |
+
year = "2019",
|
| 744 |
+
publisher = "Association for Computational Linguistics",
|
| 745 |
+
url = "https://arxiv.org/abs/1908.10084",
|
| 746 |
+
}
|
| 747 |
+
```
|
| 748 |
+
|
| 749 |
+
#### MultipleNegativesRankingLoss
|
| 750 |
+
```bibtex
|
| 751 |
+
@misc{henderson2017efficient,
|
| 752 |
+
title={Efficient Natural Language Response Suggestion for Smart Reply},
|
| 753 |
+
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
|
| 754 |
+
year={2017},
|
| 755 |
+
eprint={1705.00652},
|
| 756 |
+
archivePrefix={arXiv},
|
| 757 |
+
primaryClass={cs.CL}
|
| 758 |
+
}
|
| 759 |
+
```
|
| 760 |
+
|
| 761 |
+
<!--
|
| 762 |
+
## Glossary
|
| 763 |
+
|
| 764 |
+
*Clearly define terms in order to be accessible across audiences.*
|
| 765 |
+
-->
|
| 766 |
+
|
| 767 |
+
<!--
|
| 768 |
+
## Model Card Authors
|
| 769 |
+
|
| 770 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
| 771 |
+
-->
|
| 772 |
+
|
| 773 |
+
<!--
|
| 774 |
+
## Model Card Contact
|
| 775 |
+
|
| 776 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
| 777 |
-->
|