Commit
•
ceffb86
1
Parent(s):
ba8d6d9
Add new SentenceTransformer model.
Browse files- README.md +370 -77
- config.json +1 -1
- model.safetensors +1 -1
README.md
CHANGED
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@@ -6,7 +6,7 @@ tags:
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| 6 |
- sentence-similarity
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| 7 |
- feature-extraction
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| 8 |
- generated_from_trainer
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-
- dataset_size:
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- loss:CoSENTLoss
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base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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datasets: []
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@@ -59,34 +59,34 @@ model-index:
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type: MiniLM-dev
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metrics:
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- type: pearson_cosine
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-
value: 0.
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name: Pearson Cosine
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- type: spearman_cosine
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-
value: 0.
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name: Spearman Cosine
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- type: pearson_manhattan
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-
value: 0.
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name: Pearson Manhattan
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- type: spearman_manhattan
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-
value: 0.
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name: Spearman Manhattan
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- type: pearson_euclidean
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-
value: 0.
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name: Pearson Euclidean
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- type: spearman_euclidean
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-
value: 0.
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name: Spearman Euclidean
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- type: pearson_dot
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-
value: 0.
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name: Pearson Dot
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- type: spearman_dot
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-
value: 0.
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name: Spearman Dot
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- type: pearson_max
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-
value: 0.
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name: Pearson Max
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- type: spearman_max
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-
value: 0.
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name: Spearman Max
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- task:
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type: semantic-similarity
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@@ -96,34 +96,34 @@ model-index:
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type: MiniLM-test
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metrics:
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- type: pearson_cosine
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-
value: 0.
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name: Pearson Cosine
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- type: spearman_cosine
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-
value: 0.
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name: Spearman Cosine
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- type: pearson_manhattan
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-
value: 0.
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name: Pearson Manhattan
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- type: spearman_manhattan
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-
value: 0.
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name: Spearman Manhattan
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- type: pearson_euclidean
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-
value: 0.
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name: Pearson Euclidean
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- type: spearman_euclidean
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-
value: 0.
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name: Spearman Euclidean
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- type: pearson_dot
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-
value: 0.
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name: Pearson Dot
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- type: spearman_dot
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-
value: 0.
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name: Spearman Dot
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- type: pearson_max
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-
value: 0.
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name: Pearson Max
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- type: spearman_max
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-
value: 0.
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name: Spearman Max
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---
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@@ -224,33 +224,33 @@ You can finetune this model on your own dataset.
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| Metric | Value |
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|:--------------------|:-----------|
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| 227 |
-
| pearson_cosine | 0.
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-
| **spearman_cosine** | **0.
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| 229 |
-
| pearson_manhattan | 0.
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| 230 |
-
| spearman_manhattan | 0.
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| 231 |
-
| pearson_euclidean | 0.
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| 232 |
-
| spearman_euclidean | 0.
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| 233 |
-
| pearson_dot | 0.
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| 234 |
-
| spearman_dot | 0.
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| 235 |
-
| pearson_max | 0.
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| 236 |
-
| spearman_max | 0.
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| 237 |
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| 238 |
#### Semantic Similarity
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* Dataset: `MiniLM-test`
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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-
| Metric | Value
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-
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-
| pearson_cosine | 0.
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-
| **spearman_cosine** | **0.
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-
| pearson_manhattan | 0.
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| 247 |
-
| spearman_manhattan | 0.
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-
| pearson_euclidean | 0.
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-
| spearman_euclidean | 0.
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-
| pearson_dot | 0.
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-
| spearman_dot | 0.
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-
| pearson_max | 0.
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-
| spearman_max | 0.
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<!--
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## Bias, Risks and Limitations
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@@ -393,41 +393,334 @@ You can finetune this model on your own dataset.
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</details>
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### Training Logs
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-
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-
|:-----:|:----:|:-------------:|:------:|:--------------------------:|:---------------------------:|
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-
| 0.032 | 100 | 6.7664 | - | - | - |
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| 0.064 | 200 | 4.3148 | - | - | - |
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-
| 0.096 | 300 | 3.0991 | - | - | - |
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-
| 0.128 | 400 | 3.0274 | - | - | - |
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-
| 0.16 | 500 | 3.6869 | - | - | - |
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-
| 0.192 | 600 | 4.9801 | - | - | - |
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-
| 0.224 | 700 | 3.5306 | - | - | - |
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-
| 0.256 | 800 | 2.8376 | - | - | - |
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-
| 0.288 | 900 | 4.0961 | - | - | - |
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-
| 0.32 | 1000 | 2.7293 | 2.4118 | 0.7245 | - |
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-
| 0.352 | 1100 | 3.657 | - | - | - |
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-
| 0.384 | 1200 | 4.0484 | - | - | - |
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-
| 0.416 | 1300 | 3.2268 | - | - | - |
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-
| 0.448 | 1400 | 2.6421 | - | - | - |
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-
| 0.48 | 1500 | 3.3672 | - | - | - |
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-
| 0.512 | 1600 | 3.285 | - | - | - |
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-
| 0.544 | 1700 | 3.6787 | - | - | - |
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-
| 0.576 | 1800 | 3.8738 | - | - | - |
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-
| 0.608 | 1900 | 2.7925 | - | - | - |
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-
| 0.64 | 2000 | 2.4805 | 1.8042 | 0.7901 | - |
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| 0.672 | 2100 | 3.2279 | - | - | - |
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-
| 0.704 | 2200 | 2.8016 | - | - | - |
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-
| 0.736 | 2300 | 4.1615 | - | - | - |
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-
| 0.768 | 2400 | 3.5664 | - | - | - |
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-
| 0.8 | 2500 | 2.9362 | - | - | - |
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| 0.832 | 2600 | 3.0684 | - | - | - |
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| 0.864 | 2700 | 3.168 | - | - | - |
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| 0.896 | 2800 | 2.707 | - | - | - |
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| 0.928 | 2900 | 2.2231 | - | - | - |
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| 0.96 | 3000 | 1.2541 | 1.6753 | 0.7952 | - |
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| 0.992 | 3100 | 0.5943 | - | - | - |
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| 1.0 | 3125 | - | - | - | 0.8040 |
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| 431 |
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| 432 |
### Framework Versions
|
| 433 |
- Python: 3.10.14
|
|
|
|
| 6 |
- sentence-similarity
|
| 7 |
- feature-extraction
|
| 8 |
- generated_from_trainer
|
| 9 |
+
- dataset_size:1027471
|
| 10 |
- loss:CoSENTLoss
|
| 11 |
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
|
| 12 |
datasets: []
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|
| 59 |
type: MiniLM-dev
|
| 60 |
metrics:
|
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- type: pearson_cosine
|
| 62 |
+
value: 0.8464008477003933
|
| 63 |
name: Pearson Cosine
|
| 64 |
- type: spearman_cosine
|
| 65 |
+
value: 0.8128883563290172
|
| 66 |
name: Spearman Cosine
|
| 67 |
- type: pearson_manhattan
|
| 68 |
+
value: 0.8204825552661638
|
| 69 |
name: Pearson Manhattan
|
| 70 |
- type: spearman_manhattan
|
| 71 |
+
value: 0.8069612779979122
|
| 72 |
name: Spearman Manhattan
|
| 73 |
- type: pearson_euclidean
|
| 74 |
+
value: 0.8207664286968728
|
| 75 |
name: Pearson Euclidean
|
| 76 |
- type: spearman_euclidean
|
| 77 |
+
value: 0.806851537985582
|
| 78 |
name: Spearman Euclidean
|
| 79 |
- type: pearson_dot
|
| 80 |
+
value: 0.7927608791449223
|
| 81 |
name: Pearson Dot
|
| 82 |
- type: spearman_dot
|
| 83 |
+
value: 0.8078229606916496
|
| 84 |
name: Spearman Dot
|
| 85 |
- type: pearson_max
|
| 86 |
+
value: 0.8464008477003933
|
| 87 |
name: Pearson Max
|
| 88 |
- type: spearman_max
|
| 89 |
+
value: 0.8128883563290172
|
| 90 |
name: Spearman Max
|
| 91 |
- task:
|
| 92 |
type: semantic-similarity
|
|
|
|
| 96 |
type: MiniLM-test
|
| 97 |
metrics:
|
| 98 |
- type: pearson_cosine
|
| 99 |
+
value: 0.9079517679775697
|
| 100 |
name: Pearson Cosine
|
| 101 |
- type: spearman_cosine
|
| 102 |
+
value: 0.842595786650747
|
| 103 |
name: Spearman Cosine
|
| 104 |
- type: pearson_manhattan
|
| 105 |
+
value: 0.885352838846903
|
| 106 |
name: Pearson Manhattan
|
| 107 |
- type: spearman_manhattan
|
| 108 |
+
value: 0.8389283098138718
|
| 109 |
name: Spearman Manhattan
|
| 110 |
- type: pearson_euclidean
|
| 111 |
+
value: 0.8858228063346806
|
| 112 |
name: Pearson Euclidean
|
| 113 |
- type: spearman_euclidean
|
| 114 |
+
value: 0.8390847286161828
|
| 115 |
name: Spearman Euclidean
|
| 116 |
- type: pearson_dot
|
| 117 |
+
value: 0.8618645999355777
|
| 118 |
name: Pearson Dot
|
| 119 |
- type: spearman_dot
|
| 120 |
+
value: 0.8389938584674199
|
| 121 |
name: Spearman Dot
|
| 122 |
- type: pearson_max
|
| 123 |
+
value: 0.9079517679775697
|
| 124 |
name: Pearson Max
|
| 125 |
- type: spearman_max
|
| 126 |
+
value: 0.842595786650747
|
| 127 |
name: Spearman Max
|
| 128 |
---
|
| 129 |
|
|
|
|
| 224 |
|
| 225 |
| Metric | Value |
|
| 226 |
|:--------------------|:-----------|
|
| 227 |
+
| pearson_cosine | 0.8464 |
|
| 228 |
+
| **spearman_cosine** | **0.8129** |
|
| 229 |
+
| pearson_manhattan | 0.8205 |
|
| 230 |
+
| spearman_manhattan | 0.807 |
|
| 231 |
+
| pearson_euclidean | 0.8208 |
|
| 232 |
+
| spearman_euclidean | 0.8069 |
|
| 233 |
+
| pearson_dot | 0.7928 |
|
| 234 |
+
| spearman_dot | 0.8078 |
|
| 235 |
+
| pearson_max | 0.8464 |
|
| 236 |
+
| spearman_max | 0.8129 |
|
| 237 |
|
| 238 |
#### Semantic Similarity
|
| 239 |
* Dataset: `MiniLM-test`
|
| 240 |
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
|
| 241 |
|
| 242 |
+
| Metric | Value |
|
| 243 |
+
|:--------------------|:-----------|
|
| 244 |
+
| pearson_cosine | 0.908 |
|
| 245 |
+
| **spearman_cosine** | **0.8426** |
|
| 246 |
+
| pearson_manhattan | 0.8854 |
|
| 247 |
+
| spearman_manhattan | 0.8389 |
|
| 248 |
+
| pearson_euclidean | 0.8858 |
|
| 249 |
+
| spearman_euclidean | 0.8391 |
|
| 250 |
+
| pearson_dot | 0.8619 |
|
| 251 |
+
| spearman_dot | 0.839 |
|
| 252 |
+
| pearson_max | 0.908 |
|
| 253 |
+
| spearman_max | 0.8426 |
|
| 254 |
|
| 255 |
<!--
|
| 256 |
## Bias, Risks and Limitations
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|
| 393 |
</details>
|
| 394 |
|
| 395 |
### Training Logs
|
| 396 |
+
<details><summary>Click to expand</summary>
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|
| 397 |
|
| 398 |
+
| Epoch | Step | Training Loss | loss | MiniLM-dev_spearman_cosine | MiniLM-test_spearman_cosine |
|
| 399 |
+
|:------:|:-----:|:-------------:|:------:|:--------------------------:|:---------------------------:|
|
| 400 |
+
| 0.0031 | 100 | 7.4879 | - | - | - |
|
| 401 |
+
| 0.0062 | 200 | 6.4531 | - | - | - |
|
| 402 |
+
| 0.0093 | 300 | 6.4185 | - | - | - |
|
| 403 |
+
| 0.0125 | 400 | 4.5043 | - | - | - |
|
| 404 |
+
| 0.0156 | 500 | 5.1274 | - | - | - |
|
| 405 |
+
| 0.0187 | 600 | 6.0006 | - | - | - |
|
| 406 |
+
| 0.0218 | 700 | 4.8066 | - | - | - |
|
| 407 |
+
| 0.0249 | 800 | 3.9536 | - | - | - |
|
| 408 |
+
| 0.0280 | 900 | 4.7259 | - | - | - |
|
| 409 |
+
| 0.0311 | 1000 | 3.7583 | 2.6440 | 0.6640 | - |
|
| 410 |
+
| 0.0343 | 1100 | 3.9905 | - | - | - |
|
| 411 |
+
| 0.0374 | 1200 | 4.8914 | - | - | - |
|
| 412 |
+
| 0.0405 | 1300 | 3.895 | - | - | - |
|
| 413 |
+
| 0.0436 | 1400 | 3.1582 | - | - | - |
|
| 414 |
+
| 0.0467 | 1500 | 3.7172 | - | - | - |
|
| 415 |
+
| 0.0498 | 1600 | 3.6785 | - | - | - |
|
| 416 |
+
| 0.0529 | 1700 | 3.9632 | - | - | - |
|
| 417 |
+
| 0.0561 | 1800 | 3.9643 | - | - | - |
|
| 418 |
+
| 0.0592 | 1900 | 2.829 | - | - | - |
|
| 419 |
+
| 0.0623 | 2000 | 2.5923 | 2.3344 | 0.7459 | - |
|
| 420 |
+
| 0.0654 | 2100 | 3.1617 | - | - | - |
|
| 421 |
+
| 0.0685 | 2200 | 2.6366 | - | - | - |
|
| 422 |
+
| 0.0716 | 2300 | 4.3751 | - | - | - |
|
| 423 |
+
| 0.0747 | 2400 | 3.4732 | - | - | - |
|
| 424 |
+
| 0.0779 | 2500 | 2.5695 | - | - | - |
|
| 425 |
+
| 0.0810 | 2600 | 2.7479 | - | - | - |
|
| 426 |
+
| 0.0841 | 2700 | 2.5274 | - | - | - |
|
| 427 |
+
| 0.0872 | 2800 | 2.4204 | - | - | - |
|
| 428 |
+
| 0.0903 | 2900 | 4.1305 | - | - | - |
|
| 429 |
+
| 0.0934 | 3000 | 4.091 | 2.0951 | 0.7426 | - |
|
| 430 |
+
| 0.0965 | 3100 | 3.7972 | - | - | - |
|
| 431 |
+
| 0.0997 | 3200 | 2.6029 | - | - | - |
|
| 432 |
+
| 0.1028 | 3300 | 3.2422 | - | - | - |
|
| 433 |
+
| 0.1059 | 3400 | 3.3747 | - | - | - |
|
| 434 |
+
| 0.1090 | 3500 | 3.3358 | - | - | - |
|
| 435 |
+
| 0.1121 | 3600 | 2.8658 | - | - | - |
|
| 436 |
+
| 0.1152 | 3700 | 2.6436 | - | - | - |
|
| 437 |
+
| 0.1183 | 3800 | 2.2006 | - | - | - |
|
| 438 |
+
| 0.1215 | 3900 | 2.0549 | - | - | - |
|
| 439 |
+
| 0.1246 | 4000 | 2.4642 | 3.4108 | 0.7236 | - |
|
| 440 |
+
| 0.1277 | 4100 | 2.9219 | - | - | - |
|
| 441 |
+
| 0.1308 | 4200 | 2.6581 | - | - | - |
|
| 442 |
+
| 0.1339 | 4300 | 2.2697 | - | - | - |
|
| 443 |
+
| 0.1370 | 4400 | 2.7215 | - | - | - |
|
| 444 |
+
| 0.1401 | 4500 | 2.6023 | - | - | - |
|
| 445 |
+
| 0.1433 | 4600 | 1.8772 | - | - | - |
|
| 446 |
+
| 0.1464 | 4700 | 2.6885 | - | - | - |
|
| 447 |
+
| 0.1495 | 4800 | 2.6005 | - | - | - |
|
| 448 |
+
| 0.1526 | 4900 | 1.4849 | - | - | - |
|
| 449 |
+
| 0.1557 | 5000 | 2.4896 | 3.4860 | 0.7117 | - |
|
| 450 |
+
| 0.1588 | 5100 | 2.6038 | - | - | - |
|
| 451 |
+
| 0.1619 | 5200 | 2.0584 | - | - | - |
|
| 452 |
+
| 0.1651 | 5300 | 1.9156 | - | - | - |
|
| 453 |
+
| 0.1682 | 5400 | 1.467 | - | - | - |
|
| 454 |
+
| 0.1713 | 5500 | 0.5799 | - | - | - |
|
| 455 |
+
| 0.1744 | 5600 | 1.617 | - | - | - |
|
| 456 |
+
| 0.1775 | 5700 | 1.3764 | - | - | - |
|
| 457 |
+
| 0.1806 | 5800 | 3.067 | - | - | - |
|
| 458 |
+
| 0.1837 | 5900 | 2.2463 | - | - | - |
|
| 459 |
+
| 0.1869 | 6000 | 1.5466 | 2.5326 | 0.7721 | - |
|
| 460 |
+
| 0.1900 | 6100 | 1.4097 | - | - | - |
|
| 461 |
+
| 0.1931 | 6200 | 1.7852 | - | - | - |
|
| 462 |
+
| 0.1962 | 6300 | 1.2715 | - | - | - |
|
| 463 |
+
| 0.1993 | 6400 | 2.5585 | - | - | - |
|
| 464 |
+
| 0.2024 | 6500 | 2.4665 | - | - | - |
|
| 465 |
+
| 0.2055 | 6600 | 1.7246 | - | - | - |
|
| 466 |
+
| 0.2087 | 6700 | 1.145 | - | - | - |
|
| 467 |
+
| 0.2118 | 6800 | 1.614 | - | - | - |
|
| 468 |
+
| 0.2149 | 6900 | 1.7206 | - | - | - |
|
| 469 |
+
| 0.2180 | 7000 | 2.6349 | 2.6824 | 0.7652 | - |
|
| 470 |
+
| 0.2211 | 7100 | 2.1896 | - | - | - |
|
| 471 |
+
| 0.2242 | 7200 | 1.9106 | - | - | - |
|
| 472 |
+
| 0.2274 | 7300 | 1.3783 | - | - | - |
|
| 473 |
+
| 0.2305 | 7400 | 0.7119 | - | - | - |
|
| 474 |
+
| 0.2336 | 7500 | 1.5037 | - | - | - |
|
| 475 |
+
| 0.2367 | 7600 | 1.8365 | - | - | - |
|
| 476 |
+
| 0.2398 | 7700 | 1.3817 | - | - | - |
|
| 477 |
+
| 0.2429 | 7800 | 1.7101 | - | - | - |
|
| 478 |
+
| 0.2460 | 7900 | 1.6716 | - | - | - |
|
| 479 |
+
| 0.2492 | 8000 | 1.3013 | 3.5864 | 0.7401 | - |
|
| 480 |
+
| 0.2523 | 8100 | 1.5131 | - | - | - |
|
| 481 |
+
| 0.2554 | 8200 | 2.3699 | - | - | - |
|
| 482 |
+
| 0.2585 | 8300 | 1.6179 | - | - | - |
|
| 483 |
+
| 0.2616 | 8400 | 1.3 | - | - | - |
|
| 484 |
+
| 0.2647 | 8500 | 1.5151 | - | - | - |
|
| 485 |
+
| 0.2678 | 8600 | 2.8703 | - | - | - |
|
| 486 |
+
| 0.2710 | 8700 | 2.5076 | - | - | - |
|
| 487 |
+
| 0.2741 | 8800 | 1.9876 | - | - | - |
|
| 488 |
+
| 0.2772 | 8900 | 1.5823 | - | - | - |
|
| 489 |
+
| 0.2803 | 9000 | 1.0845 | 2.4197 | 0.7833 | - |
|
| 490 |
+
| 0.2834 | 9100 | 1.2871 | - | - | - |
|
| 491 |
+
| 0.2865 | 9200 | 1.3901 | - | - | - |
|
| 492 |
+
| 0.2896 | 9300 | 1.1607 | - | - | - |
|
| 493 |
+
| 0.2928 | 9400 | 2.1171 | - | - | - |
|
| 494 |
+
| 0.2959 | 9500 | 1.4335 | - | - | - |
|
| 495 |
+
| 0.2990 | 9600 | 0.801 | - | - | - |
|
| 496 |
+
| 0.3021 | 9700 | 1.4567 | - | - | - |
|
| 497 |
+
| 0.3052 | 9800 | 1.7046 | - | - | - |
|
| 498 |
+
| 0.3083 | 9900 | 1.4378 | - | - | - |
|
| 499 |
+
| 0.3114 | 10000 | 2.3191 | 2.3063 | 0.7903 | - |
|
| 500 |
+
| 0.3146 | 10100 | 1.6518 | - | - | - |
|
| 501 |
+
| 0.3177 | 10200 | 0.9857 | - | - | - |
|
| 502 |
+
| 0.3208 | 10300 | 2.2052 | - | - | - |
|
| 503 |
+
| 0.3239 | 10400 | 2.0443 | - | - | - |
|
| 504 |
+
| 0.3270 | 10500 | 2.08 | - | - | - |
|
| 505 |
+
| 0.3301 | 10600 | 2.0009 | - | - | - |
|
| 506 |
+
| 0.3332 | 10700 | 1.3274 | - | - | - |
|
| 507 |
+
| 0.3364 | 10800 | 1.0298 | - | - | - |
|
| 508 |
+
| 0.3395 | 10900 | 1.7127 | - | - | - |
|
| 509 |
+
| 0.3426 | 11000 | 1.3371 | 4.0607 | 0.7211 | - |
|
| 510 |
+
| 0.3457 | 11100 | 2.7555 | - | - | - |
|
| 511 |
+
| 0.3488 | 11200 | 4.1792 | - | - | - |
|
| 512 |
+
| 0.3519 | 11300 | 2.0931 | - | - | - |
|
| 513 |
+
| 0.3550 | 11400 | 2.4591 | - | - | - |
|
| 514 |
+
| 0.3582 | 11500 | 3.4962 | - | - | - |
|
| 515 |
+
| 0.3613 | 11600 | 1.9228 | - | - | - |
|
| 516 |
+
| 0.3644 | 11700 | 2.7295 | - | - | - |
|
| 517 |
+
| 0.3675 | 11800 | 1.5425 | - | - | - |
|
| 518 |
+
| 0.3706 | 11900 | 1.1586 | - | - | - |
|
| 519 |
+
| 0.3737 | 12000 | 1.1336 | 2.2959 | 0.7890 | - |
|
| 520 |
+
| 0.3768 | 12100 | 1.572 | - | - | - |
|
| 521 |
+
| 0.3800 | 12200 | 1.2827 | - | - | - |
|
| 522 |
+
| 0.3831 | 12300 | 1.6352 | - | - | - |
|
| 523 |
+
| 0.3862 | 12400 | 1.4708 | - | - | - |
|
| 524 |
+
| 0.3893 | 12500 | 1.4719 | - | - | - |
|
| 525 |
+
| 0.3924 | 12600 | 1.4136 | - | - | - |
|
| 526 |
+
| 0.3955 | 12700 | 1.3969 | - | - | - |
|
| 527 |
+
| 0.3986 | 12800 | 1.7228 | - | - | - |
|
| 528 |
+
| 0.4018 | 12900 | 4.2842 | - | - | - |
|
| 529 |
+
| 0.4049 | 13000 | 3.5861 | 2.1113 | 0.7956 | - |
|
| 530 |
+
| 0.4080 | 13100 | 2.9718 | - | - | - |
|
| 531 |
+
| 0.4111 | 13200 | 3.1554 | - | - | - |
|
| 532 |
+
| 0.4142 | 13300 | 3.1357 | - | - | - |
|
| 533 |
+
| 0.4173 | 13400 | 2.8488 | - | - | - |
|
| 534 |
+
| 0.4204 | 13500 | 3.7433 | - | - | - |
|
| 535 |
+
| 0.4236 | 13600 | 2.4195 | - | - | - |
|
| 536 |
+
| 0.4267 | 13700 | 2.1384 | - | - | - |
|
| 537 |
+
| 0.4298 | 13800 | 2.7965 | - | - | - |
|
| 538 |
+
| 0.4329 | 13900 | 1.7869 | - | - | - |
|
| 539 |
+
| 0.4360 | 14000 | 3.0356 | 2.7234 | 0.7697 | - |
|
| 540 |
+
| 0.4391 | 14100 | 3.4984 | - | - | - |
|
| 541 |
+
| 0.4422 | 14200 | 2.4959 | - | - | - |
|
| 542 |
+
| 0.4454 | 14300 | 2.4615 | - | - | - |
|
| 543 |
+
| 0.4485 | 14400 | 2.6309 | - | - | - |
|
| 544 |
+
| 0.4516 | 14500 | 1.9831 | - | - | - |
|
| 545 |
+
| 0.4547 | 14600 | 3.25 | - | - | - |
|
| 546 |
+
| 0.4578 | 14700 | 3.3112 | - | - | - |
|
| 547 |
+
| 0.4609 | 14800 | 1.9912 | - | - | - |
|
| 548 |
+
| 0.4640 | 14900 | 1.9252 | - | - | - |
|
| 549 |
+
| 0.4672 | 15000 | 2.4545 | 2.0730 | 0.7972 | - |
|
| 550 |
+
| 0.4703 | 15100 | 1.6943 | - | - | - |
|
| 551 |
+
| 0.4734 | 15200 | 2.2851 | - | - | - |
|
| 552 |
+
| 0.4765 | 15300 | 2.4327 | - | - | - |
|
| 553 |
+
| 0.4796 | 15400 | 1.3503 | - | - | - |
|
| 554 |
+
| 0.4827 | 15500 | 1.1419 | - | - | - |
|
| 555 |
+
| 0.4858 | 15600 | 1.7906 | - | - | - |
|
| 556 |
+
| 0.4890 | 15700 | 1.6504 | - | - | - |
|
| 557 |
+
| 0.4921 | 15800 | 1.6908 | - | - | - |
|
| 558 |
+
| 0.4952 | 15900 | 3.0954 | - | - | - |
|
| 559 |
+
| 0.4983 | 16000 | 1.7151 | 2.0042 | 0.8044 | - |
|
| 560 |
+
| 0.5014 | 16100 | 1.5165 | - | - | - |
|
| 561 |
+
| 0.5045 | 16200 | 2.5573 | - | - | - |
|
| 562 |
+
| 0.5076 | 16300 | 1.3401 | - | - | - |
|
| 563 |
+
| 0.5108 | 16400 | 2.5464 | - | - | - |
|
| 564 |
+
| 0.5139 | 16500 | 2.4564 | - | - | - |
|
| 565 |
+
| 0.5170 | 16600 | 2.1667 | - | - | - |
|
| 566 |
+
| 0.5201 | 16700 | 1.2402 | - | - | - |
|
| 567 |
+
| 0.5232 | 16800 | 1.932 | - | - | - |
|
| 568 |
+
| 0.5263 | 16900 | 1.1811 | - | - | - |
|
| 569 |
+
| 0.5294 | 17000 | 2.2014 | 2.0475 | 0.8062 | - |
|
| 570 |
+
| 0.5326 | 17100 | 2.6535 | - | - | - |
|
| 571 |
+
| 0.5357 | 17200 | 1.8715 | - | - | - |
|
| 572 |
+
| 0.5388 | 17300 | 1.9385 | - | - | - |
|
| 573 |
+
| 0.5419 | 17400 | 2.0398 | - | - | - |
|
| 574 |
+
| 0.5450 | 17500 | 1.3436 | - | - | - |
|
| 575 |
+
| 0.5481 | 17600 | 2.0687 | - | - | - |
|
| 576 |
+
| 0.5512 | 17700 | 1.6224 | - | - | - |
|
| 577 |
+
| 0.5544 | 17800 | 1.0539 | - | - | - |
|
| 578 |
+
| 0.5575 | 17900 | 1.1162 | - | - | - |
|
| 579 |
+
| 0.5606 | 18000 | 1.6334 | 2.4120 | 0.7964 | - |
|
| 580 |
+
| 0.5637 | 18100 | 1.247 | - | - | - |
|
| 581 |
+
| 0.5668 | 18200 | 2.4652 | - | - | - |
|
| 582 |
+
| 0.5699 | 18300 | 1.8593 | - | - | - |
|
| 583 |
+
| 0.5730 | 18400 | 1.1875 | - | - | - |
|
| 584 |
+
| 0.5762 | 18500 | 2.1173 | - | - | - |
|
| 585 |
+
| 0.5793 | 18600 | 1.7473 | - | - | - |
|
| 586 |
+
| 0.5824 | 18700 | 2.1865 | - | - | - |
|
| 587 |
+
| 0.5855 | 18800 | 1.683 | - | - | - |
|
| 588 |
+
| 0.5886 | 18900 | 1.6522 | - | - | - |
|
| 589 |
+
| 0.5917 | 19000 | 1.0526 | 2.0743 | 0.8033 | - |
|
| 590 |
+
| 0.5948 | 19100 | 1.5001 | - | - | - |
|
| 591 |
+
| 0.5980 | 19200 | 1.2606 | - | - | - |
|
| 592 |
+
| 0.6011 | 19300 | 1.0597 | - | - | - |
|
| 593 |
+
| 0.6042 | 19400 | 1.8603 | - | - | - |
|
| 594 |
+
| 0.6073 | 19500 | 1.4883 | - | - | - |
|
| 595 |
+
| 0.6104 | 19600 | 0.6594 | - | - | - |
|
| 596 |
+
| 0.6135 | 19700 | 0.9557 | - | - | - |
|
| 597 |
+
| 0.6166 | 19800 | 0.8651 | - | - | - |
|
| 598 |
+
| 0.6198 | 19900 | 1.0326 | - | - | - |
|
| 599 |
+
| 0.6229 | 20000 | 1.2785 | 2.0868 | 0.8075 | - |
|
| 600 |
+
| 0.6260 | 20100 | 1.2881 | - | - | - |
|
| 601 |
+
| 0.6291 | 20200 | 0.5919 | - | - | - |
|
| 602 |
+
| 0.6322 | 20300 | 1.69 | - | - | - |
|
| 603 |
+
| 0.6353 | 20400 | 1.0285 | - | - | - |
|
| 604 |
+
| 0.6385 | 20500 | 0.8843 | - | - | - |
|
| 605 |
+
| 0.6416 | 20600 | 1.3756 | - | - | - |
|
| 606 |
+
| 0.6447 | 20700 | 0.9646 | - | - | - |
|
| 607 |
+
| 0.6478 | 20800 | 0.8052 | - | - | - |
|
| 608 |
+
| 0.6509 | 20900 | 0.8996 | - | - | - |
|
| 609 |
+
| 0.6540 | 21000 | 1.2207 | 2.2881 | 0.8029 | - |
|
| 610 |
+
| 0.6571 | 21100 | 1.3225 | - | - | - |
|
| 611 |
+
| 0.6603 | 21200 | 1.8101 | - | - | - |
|
| 612 |
+
| 0.6634 | 21300 | 0.8756 | - | - | - |
|
| 613 |
+
| 0.6665 | 21400 | 0.9877 | - | - | - |
|
| 614 |
+
| 0.6696 | 21500 | 1.7329 | - | - | - |
|
| 615 |
+
| 0.6727 | 21600 | 1.6885 | - | - | - |
|
| 616 |
+
| 0.6758 | 21700 | 1.2132 | - | - | - |
|
| 617 |
+
| 0.6789 | 21800 | 1.4888 | - | - | - |
|
| 618 |
+
| 0.6821 | 21900 | 1.403 | - | - | - |
|
| 619 |
+
| 0.6852 | 22000 | 0.5995 | 2.1952 | 0.8036 | - |
|
| 620 |
+
| 0.6883 | 22100 | 0.9658 | - | - | - |
|
| 621 |
+
| 0.6914 | 22200 | 1.1485 | - | - | - |
|
| 622 |
+
| 0.6945 | 22300 | 1.089 | - | - | - |
|
| 623 |
+
| 0.6976 | 22400 | 1.2719 | - | - | - |
|
| 624 |
+
| 0.7007 | 22500 | 0.9611 | - | - | - |
|
| 625 |
+
| 0.7039 | 22600 | 0.9398 | - | - | - |
|
| 626 |
+
| 0.7070 | 22700 | 0.7931 | - | - | - |
|
| 627 |
+
| 0.7101 | 22800 | 1.1224 | - | - | - |
|
| 628 |
+
| 0.7132 | 22900 | 2.032 | - | - | - |
|
| 629 |
+
| 0.7163 | 23000 | 1.3664 | 2.1043 | 0.8075 | - |
|
| 630 |
+
| 0.7194 | 23100 | 0.7777 | - | - | - |
|
| 631 |
+
| 0.7225 | 23200 | 0.9427 | - | - | - |
|
| 632 |
+
| 0.7257 | 23300 | 0.8846 | - | - | - |
|
| 633 |
+
| 0.7288 | 23400 | 1.0039 | - | - | - |
|
| 634 |
+
| 0.7319 | 23500 | 0.9344 | - | - | - |
|
| 635 |
+
| 0.7350 | 23600 | 1.3712 | - | - | - |
|
| 636 |
+
| 0.7381 | 23700 | 0.8039 | - | - | - |
|
| 637 |
+
| 0.7412 | 23800 | 1.0735 | - | - | - |
|
| 638 |
+
| 0.7443 | 23900 | 0.9851 | - | - | - |
|
| 639 |
+
| 0.7475 | 24000 | 1.8673 | 2.1547 | 0.8066 | - |
|
| 640 |
+
| 0.7506 | 24100 | 5.5805 | - | - | - |
|
| 641 |
+
| 0.7537 | 24200 | 4.1286 | - | - | - |
|
| 642 |
+
| 0.7568 | 24300 | 2.2206 | - | - | - |
|
| 643 |
+
| 0.7599 | 24400 | 3.6468 | - | - | - |
|
| 644 |
+
| 0.7630 | 24500 | 2.9307 | - | - | - |
|
| 645 |
+
| 0.7661 | 24600 | 3.8745 | - | - | - |
|
| 646 |
+
| 0.7693 | 24700 | 2.2125 | - | - | - |
|
| 647 |
+
| 0.7724 | 24800 | 2.3844 | - | - | - |
|
| 648 |
+
| 0.7755 | 24900 | 1.5081 | - | - | - |
|
| 649 |
+
| 0.7786 | 25000 | 1.5982 | 1.8491 | 0.8145 | - |
|
| 650 |
+
| 0.7817 | 25100 | 2.1563 | - | - | - |
|
| 651 |
+
| 0.7848 | 25200 | 1.8558 | - | - | - |
|
| 652 |
+
| 0.7879 | 25300 | 2.2087 | - | - | - |
|
| 653 |
+
| 0.7911 | 25400 | 2.3953 | - | - | - |
|
| 654 |
+
| 0.7942 | 25500 | 1.4072 | - | - | - |
|
| 655 |
+
| 0.7973 | 25600 | 1.4637 | - | - | - |
|
| 656 |
+
| 0.8004 | 25700 | 2.2037 | - | - | - |
|
| 657 |
+
| 0.8035 | 25800 | 1.6241 | - | - | - |
|
| 658 |
+
| 0.8066 | 25900 | 1.4882 | - | - | - |
|
| 659 |
+
| 0.8097 | 26000 | 0.9108 | 1.9292 | 0.8115 | - |
|
| 660 |
+
| 0.8129 | 26100 | 0.9198 | - | - | - |
|
| 661 |
+
| 0.8160 | 26200 | 1.2981 | - | - | - |
|
| 662 |
+
| 0.8191 | 26300 | 1.0513 | - | - | - |
|
| 663 |
+
| 0.8222 | 26400 | 1.389 | - | - | - |
|
| 664 |
+
| 0.8253 | 26500 | 5.8539 | - | - | - |
|
| 665 |
+
| 0.8284 | 26600 | 3.547 | - | - | - |
|
| 666 |
+
| 0.8315 | 26700 | 2.3285 | - | - | - |
|
| 667 |
+
| 0.8347 | 26800 | 2.8112 | - | - | - |
|
| 668 |
+
| 0.8378 | 26900 | 3.3717 | - | - | - |
|
| 669 |
+
| 0.8409 | 27000 | 2.5921 | 1.9430 | 0.8108 | - |
|
| 670 |
+
| 0.8440 | 27100 | 1.5048 | - | - | - |
|
| 671 |
+
| 0.8471 | 27200 | 1.5 | - | - | - |
|
| 672 |
+
| 0.8502 | 27300 | 0.778 | - | - | - |
|
| 673 |
+
| 0.8533 | 27400 | 0.9557 | - | - | - |
|
| 674 |
+
| 0.8565 | 27500 | 1.347 | - | - | - |
|
| 675 |
+
| 0.8596 | 27600 | 1.5882 | - | - | - |
|
| 676 |
+
| 0.8627 | 27700 | 1.7333 | - | - | - |
|
| 677 |
+
| 0.8658 | 27800 | 1.5683 | - | - | - |
|
| 678 |
+
| 0.8689 | 27900 | 0.7698 | - | - | - |
|
| 679 |
+
| 0.8720 | 28000 | 1.2758 | 1.9704 | 0.8127 | - |
|
| 680 |
+
| 0.8751 | 28100 | 1.3248 | - | - | - |
|
| 681 |
+
| 0.8783 | 28200 | 1.041 | - | - | - |
|
| 682 |
+
| 0.8814 | 28300 | 1.6066 | - | - | - |
|
| 683 |
+
| 0.8845 | 28400 | 1.9033 | - | - | - |
|
| 684 |
+
| 0.8876 | 28500 | 0.8781 | - | - | - |
|
| 685 |
+
| 0.8907 | 28600 | 0.9345 | - | - | - |
|
| 686 |
+
| 0.8938 | 28700 | 0.9209 | - | - | - |
|
| 687 |
+
| 0.8969 | 28800 | 1.1443 | - | - | - |
|
| 688 |
+
| 0.9001 | 28900 | 0.9522 | - | - | - |
|
| 689 |
+
| 0.9032 | 29000 | 1.4295 | 2.0572 | 0.8111 | - |
|
| 690 |
+
| 0.9063 | 29100 | 0.9005 | - | - | - |
|
| 691 |
+
| 0.9094 | 29200 | 1.0024 | - | - | - |
|
| 692 |
+
| 0.9125 | 29300 | 1.3573 | - | - | - |
|
| 693 |
+
| 0.9156 | 29400 | 1.0805 | - | - | - |
|
| 694 |
+
| 0.9187 | 29500 | 1.3308 | - | - | - |
|
| 695 |
+
| 0.9219 | 29600 | 1.4853 | - | - | - |
|
| 696 |
+
| 0.9250 | 29700 | 2.0785 | - | - | - |
|
| 697 |
+
| 0.9281 | 29800 | 0.9283 | - | - | - |
|
| 698 |
+
| 0.9312 | 29900 | 0.8081 | - | - | - |
|
| 699 |
+
| 0.9343 | 30000 | 0.4223 | 2.0404 | 0.8115 | - |
|
| 700 |
+
| 0.9374 | 30100 | 0.8565 | - | - | - |
|
| 701 |
+
| 0.9405 | 30200 | 0.6674 | - | - | - |
|
| 702 |
+
| 0.9437 | 30300 | 0.5499 | - | - | - |
|
| 703 |
+
| 0.9468 | 30400 | 0.3212 | - | - | - |
|
| 704 |
+
| 0.9499 | 30500 | 0.166 | - | - | - |
|
| 705 |
+
| 0.9530 | 30600 | 0.1096 | - | - | - |
|
| 706 |
+
| 0.9561 | 30700 | 0.0382 | - | - | - |
|
| 707 |
+
| 0.9592 | 30800 | 0.2927 | - | - | - |
|
| 708 |
+
| 0.9623 | 30900 | 0.4097 | - | - | - |
|
| 709 |
+
| 0.9655 | 31000 | 0.5554 | 2.0068 | 0.8130 | - |
|
| 710 |
+
| 0.9686 | 31100 | 0.5783 | - | - | - |
|
| 711 |
+
| 0.9717 | 31200 | 0.376 | - | - | - |
|
| 712 |
+
| 0.9748 | 31300 | 0.3469 | - | - | - |
|
| 713 |
+
| 0.9779 | 31400 | 0.3043 | - | - | - |
|
| 714 |
+
| 0.9810 | 31500 | 0.4023 | - | - | - |
|
| 715 |
+
| 0.9841 | 31600 | 0.1876 | - | - | - |
|
| 716 |
+
| 0.9873 | 31700 | 0.4473 | - | - | - |
|
| 717 |
+
| 0.9904 | 31800 | 0.3256 | - | - | - |
|
| 718 |
+
| 0.9935 | 31900 | 0.4875 | - | - | - |
|
| 719 |
+
| 0.9966 | 32000 | 0.1807 | 2.0122 | 0.8129 | - |
|
| 720 |
+
| 0.9997 | 32100 | 0.3249 | - | - | - |
|
| 721 |
+
| 1.0 | 32109 | - | - | - | 0.8426 |
|
| 722 |
+
|
| 723 |
+
</details>
|
| 724 |
|
| 725 |
### Framework Versions
|
| 726 |
- Python: 3.10.14
|
config.json
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
{
|
| 2 |
-
"_name_or_path": "
|
| 3 |
"architectures": [
|
| 4 |
"BertModel"
|
| 5 |
],
|
|
|
|
| 1 |
{
|
| 2 |
+
"_name_or_path": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
|
| 3 |
"architectures": [
|
| 4 |
"BertModel"
|
| 5 |
],
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 470637416
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:045f266e9f6f0aae65f8b6adc4633047fafc171ce35def4d1086599755cde290
|
| 3 |
size 470637416
|