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lv12
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esci-nomic-embed-text-v1_5

Sentence Similarity
sentence-transformers
Safetensors
nomic_bert
feature-extraction
dataset_size:100K<n<1M
loss:CachedMultipleNegativesRankingLoss
custom_code
Eval Results
text-embeddings-inference
Model card Files
xet
Community
lv12 commited on
Commit
459a40b
·
verified ·
1 Parent(s): 6de8616

Add new SentenceTransformer model.

Browse files
Files changed (3) hide show
  1. README.md +110 -10
  2. config.json +1 -1
  3. model.safetensors +1 -1
README.md CHANGED
@@ -37,7 +37,7 @@ widget:
37
  - 'search_query: 12 pomos sin tornillos'
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  - source_sentence: 'search_query: 傘 鬼滅の刃'
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  sentences:
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- - 'search_query: ノースフェイス リュック'
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  - 'search_query: お札 を 折ら ない ミニ 財布'
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  - 'search_query: buffalo plaid earrings'
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  pipeline_tag: sentence-similarity
@@ -52,19 +52,19 @@ model-index:
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  type: triplet-esci
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  metrics:
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  - type: cosine_accuracy
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- value: 0.655
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  name: Cosine Accuracy
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  - type: dot_accuracy
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- value: 0.343
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  name: Dot Accuracy
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  - type: manhattan_accuracy
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  value: 0.657
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  name: Manhattan Accuracy
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  - type: euclidean_accuracy
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- value: 0.656
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  name: Euclidean Accuracy
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  - type: max_accuracy
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- value: 0.657
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  name: Max Accuracy
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  ---
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@@ -118,7 +118,7 @@ model = SentenceTransformer("sentence_transformers_model_id")
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  # Run inference
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  sentences = [
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  'search_query: 傘 鬼滅の刃',
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- 'search_query: ノースフェイス リュック',
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  'search_query: お札 を 折ら ない ミニ 財布',
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  ]
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  embeddings = model.encode(sentences)
@@ -165,11 +165,11 @@ You can finetune this model on your own dataset.
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  | Metric | Value |
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  |:--------------------|:----------|
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- | **cosine_accuracy** | **0.655** |
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- | dot_accuracy | 0.343 |
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  | manhattan_accuracy | 0.657 |
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- | euclidean_accuracy | 0.656 |
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- | max_accuracy | 0.657 |
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  <!--
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  ## Bias, Risks and Limitations
@@ -638,6 +638,106 @@ You can finetune this model on your own dataset.
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  | 2.184 | 54600 | 0.3678 | - | - |
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  | 2.192 | 54800 | 0.2965 | - | - |
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  | 2.2 | 55000 | 0.3691 | 3.8108 | 0.655 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
641
 
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  </details>
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  - 'search_query: 12 pomos sin tornillos'
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  - source_sentence: 'search_query: 傘 鬼滅の刃'
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  sentences:
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+ - 'search_query: 充電器のコード'
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  - 'search_query: お札 を 折ら ない ミニ 財布'
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  - 'search_query: buffalo plaid earrings'
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  pipeline_tag: sentence-similarity
 
52
  type: triplet-esci
53
  metrics:
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  - type: cosine_accuracy
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+ value: 0.658
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  name: Cosine Accuracy
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  - type: dot_accuracy
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+ value: 0.342
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  name: Dot Accuracy
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  - type: manhattan_accuracy
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  value: 0.657
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  name: Manhattan Accuracy
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  - type: euclidean_accuracy
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+ value: 0.657
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  name: Euclidean Accuracy
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  - type: max_accuracy
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+ value: 0.658
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  name: Max Accuracy
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  ---
70
 
 
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  # Run inference
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  sentences = [
120
  'search_query: 傘 鬼滅の刃',
121
+ 'search_query: 充電器のコード',
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  'search_query: お札 を 折ら ない ミニ 財布',
123
  ]
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  embeddings = model.encode(sentences)
 
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  | Metric | Value |
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  |:--------------------|:----------|
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+ | **cosine_accuracy** | **0.658** |
169
+ | dot_accuracy | 0.342 |
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  | manhattan_accuracy | 0.657 |
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+ | euclidean_accuracy | 0.657 |
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+ | max_accuracy | 0.658 |
173
 
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  <!--
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  ## Bias, Risks and Limitations
 
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  | 2.184 | 54600 | 0.3678 | - | - |
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  | 2.192 | 54800 | 0.2965 | - | - |
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  | 2.2 | 55000 | 0.3691 | 3.8108 | 0.655 |
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+ | 2.208 | 55200 | 0.2739 | - | - |
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+ | 2.216 | 55400 | 0.3283 | - | - |
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+ | 2.224 | 55600 | 0.2133 | - | - |
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+ | 2.232 | 55800 | 0.2582 | - | - |
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+ | 2.24 | 56000 | 0.3234 | 3.7370 | 0.665 |
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+ | 2.248 | 56200 | 0.2702 | - | - |
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+ | 2.2560 | 56400 | 0.2713 | - | - |
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+ | 2.2640 | 56600 | 0.2988 | - | - |
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+ | 2.2720 | 56800 | 0.2338 | - | - |
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+ | 2.2800 | 57000 | 0.183 | 3.7459 | 0.658 |
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+ | 2.288 | 57200 | 0.2517 | - | - |
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+ | 2.296 | 57400 | 0.2585 | - | - |
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+ | 2.304 | 57600 | 0.2113 | - | - |
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+ | 2.312 | 57800 | 0.1935 | - | - |
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+ | 2.32 | 58000 | 0.2307 | 3.7409 | 0.661 |
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+ | 2.328 | 58200 | 0.2353 | - | - |
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+ | 2.336 | 58400 | 0.2099 | - | - |
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+ | 2.344 | 58600 | 0.2823 | - | - |
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+ | 2.352 | 58800 | 0.2071 | - | - |
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+ | 2.36 | 59000 | 0.1928 | 3.7614 | 0.65 |
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+ | 2.368 | 59200 | 0.1616 | - | - |
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+ | 2.376 | 59400 | 0.1727 | - | - |
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+ | 2.384 | 59600 | 0.1745 | - | - |
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+ | 2.392 | 59800 | 0.1736 | - | - |
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+ | 2.4 | 60000 | 0.2186 | 3.7309 | 0.659 |
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+ | 2.408 | 60200 | 0.1637 | - | - |
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+ | 2.416 | 60400 | 0.1957 | - | - |
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+ | 2.424 | 60600 | 0.1512 | - | - |
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+ | 2.432 | 60800 | 0.2133 | - | - |
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+ | 2.44 | 61000 | 0.2122 | 3.7318 | 0.658 |
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+ | 2.448 | 61200 | 0.1876 | - | - |
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+ | 2.456 | 61400 | 0.2201 | - | - |
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+ | 2.464 | 61600 | 0.1581 | - | - |
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+ | 2.472 | 61800 | 0.1856 | - | - |
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+ | 2.48 | 62000 | 0.1426 | 3.7491 | 0.657 |
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+ | 2.488 | 62200 | 0.1769 | - | - |
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+ | 2.496 | 62400 | 0.1706 | - | - |
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+ | 2.504 | 62600 | 0.2492 | - | - |
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+ | 2.512 | 62800 | 0.2026 | - | - |
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+ | 2.52 | 63000 | 0.1612 | 3.7638 | 0.66 |
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+ | 2.528 | 63200 | 0.21 | - | - |
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+ | 2.536 | 63400 | 0.1183 | - | - |
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+ | 2.544 | 63600 | 0.2244 | - | - |
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+ | 2.552 | 63800 | 0.1503 | - | - |
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+ | 2.56 | 64000 | 0.1581 | 3.7668 | 0.661 |
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+ | 2.568 | 64200 | 0.1887 | - | - |
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+ | 2.576 | 64400 | 0.1873 | - | - |
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+ | 2.584 | 64600 | 0.1939 | - | - |
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+ | 2.592 | 64800 | 0.2089 | - | - |
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+ | 2.6 | 65000 | 0.1839 | 3.7631 | 0.657 |
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+ | 2.608 | 65200 | 0.1508 | - | - |
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+ | 2.616 | 65400 | 0.1247 | - | - |
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+ | 2.624 | 65600 | 0.1457 | - | - |
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+ | 2.632 | 65800 | 0.1267 | - | - |
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+ | 2.64 | 66000 | 0.1327 | 3.7712 | 0.656 |
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+ | 2.648 | 66200 | 0.1295 | - | - |
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+ | 2.656 | 66400 | 0.1222 | - | - |
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+ | 2.664 | 66600 | 0.1227 | - | - |
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+ | 2.672 | 66800 | 0.1445 | - | - |
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+ | 2.68 | 67000 | 0.1107 | 3.7753 | 0.659 |
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+ | 2.6880 | 67200 | 0.1173 | - | - |
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+ | 2.6960 | 67400 | 0.1743 | - | - |
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+ | 2.7040 | 67600 | 0.1521 | - | - |
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+ | 2.7120 | 67800 | 0.1516 | - | - |
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+ | 2.7200 | 68000 | 0.1537 | 3.7786 | 0.658 |
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+ | 2.7280 | 68200 | 0.108 | - | - |
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+ | 2.7360 | 68400 | 0.1636 | - | - |
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+ | 2.7440 | 68600 | 0.146 | - | - |
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+ | 2.752 | 68800 | 0.1342 | - | - |
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+ | 2.76 | 69000 | 0.0997 | 3.7753 | 0.658 |
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+ | 2.768 | 69200 | 0.0952 | - | - |
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+ | 2.776 | 69400 | 0.1372 | - | - |
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+ | 2.784 | 69600 | 0.1558 | - | - |
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+ | 2.792 | 69800 | 0.1352 | - | - |
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+ | 2.8 | 70000 | 0.1723 | 3.7772 | 0.656 |
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+ | 2.808 | 70200 | 0.1253 | - | - |
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+ | 2.816 | 70400 | 0.1756 | - | - |
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+ | 2.824 | 70600 | 0.1477 | - | - |
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+ | 2.832 | 70800 | 0.1305 | - | - |
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+ | 2.84 | 71000 | 0.1292 | 3.7787 | 0.656 |
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+ | 2.848 | 71200 | 0.0797 | - | - |
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+ | 2.856 | 71400 | 0.0955 | - | - |
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+ | 2.864 | 71600 | 0.1214 | - | - |
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+ | 2.872 | 71800 | 0.1704 | - | - |
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+ | 2.88 | 72000 | 0.1291 | 3.7794 | 0.658 |
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+ | 2.888 | 72200 | 0.0839 | - | - |
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+ | 2.896 | 72400 | 0.1142 | - | - |
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+ | 2.904 | 72600 | 0.0836 | - | - |
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+ | 2.912 | 72800 | 0.1011 | - | - |
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+ | 2.92 | 73000 | 0.153 | 3.7803 | 0.66 |
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+ | 2.928 | 73200 | 0.0975 | - | - |
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+ | 2.936 | 73400 | 0.1276 | - | - |
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+ | 2.944 | 73600 | 0.0993 | - | - |
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+ | 2.952 | 73800 | 0.1419 | - | - |
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+ | 2.96 | 74000 | 0.1414 | 3.7807 | 0.658 |
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+ | 2.968 | 74200 | 0.1105 | - | - |
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+ | 2.976 | 74400 | 0.1085 | - | - |
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+ | 2.984 | 74600 | 0.1281 | - | - |
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+ | 2.992 | 74800 | 0.1057 | - | - |
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+ | 3.0 | 75000 | 0.1197 | 3.7807 | 0.658 |
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  </details>
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config.json CHANGED
@@ -1,5 +1,5 @@
1
  {
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- "_name_or_path": "models/nomic-embed-text-esci/checkpoint-55000",
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  "activation_function": "swiglu",
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  "architectures": [
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  "NomicBertModel"
 
1
  {
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+ "_name_or_path": "models/nomic-embed-text-esci/checkpoint-75000",
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  "activation_function": "swiglu",
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  "architectures": [
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  "NomicBertModel"
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
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- oid sha256:414b031b610e8f3df6786386dde6ba3ce370027798e807e9e3d93ac4764e355d
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  size 546938168
 
1
  version https://git-lfs.github.com/spec/v1
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+ oid sha256:ff8a9331c4fe4be2be01610969d1ba1816b10402a95b939d6d2390fb0395d2f4
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  size 546938168