Add SetFit model
Browse files- README.md +651 -683
- config.json +1 -1
- model_head.pkl +1 -1
- pytorch_model.bin +1 -1
README.md
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@@ -9,11 +9,12 @@ tags:
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- text-classification
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- generated_from_setfit_trainer
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widget:
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- text:
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- text:
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inference: true
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model-index:
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- name: SetFit
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split: test
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metrics:
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- type: accuracy
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value: 0.
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name: Accuracy
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---
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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### Model Labels
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| Label | Examples
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-
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| μ€νμ νμ§ | <ul><li>'
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| μμ½ | <ul><li>'(νΉμ νΉνλ²νΈ)λ₯Ό κΈ°λ°μΌλ‘ ν λ°λͺ
μ μ 체μ μΈ κ°λ
μ μ§§κ² μ€λͺ
λΆνλ립λλ€.'</li><li>'1μ₯μ λ°μ΄ν° μμ§ κΈ°μ μ λν΄ μμ½ν΄μ£ΌμΈμ'</li
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| μ μ¬λ¬Έμ | <ul><li>'5G ν΅μ λͺ¨λ μ΅μ νμ κ΄λ ¨λ νλ‘μ νΈλ₯Ό νκ³ μλλ°, λΉμ·ν λ΄μ©μ νλ‘μ νΈλ λ
Όλ¬Έμ΄ μλμ§ μ°κ²°ν΄μ λ§ν΄μ€λ?'</li><li>'
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| μ€λ³΅μ± κ²ν | <ul><li>'5G
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| νΉν μ§μμ 보 μ 곡 | <ul><li>'3D κΈμ λ°°μ κΈ°μ (HBM, TSV)μ λμ
μΌλ‘ μΈν μ λ ₯ μλΉ κ°μ λ°©μμλ μ΄λ€ κ²μ΄ μλκ°μ?'</li><li>'AI μν¬λ‘λλ₯Ό μ²λ¦¬νκΈ° μν λ°λ체 μν€ν
μ² μ€κ³μμλ μ΄λ€ μ λ΅λ€μ΄ μ¬μ©λλμ?'</li><li>'
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## Evaluation
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### Metrics
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| Label | Accuracy |
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|:--------|:---------|
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| **all** | 0.
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## Uses
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@@ -92,7 +93,7 @@ from setfit import SetFitModel
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# Download from the π€ Hub
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model = SetFitModel.from_pretrained("NTIS/kepri-embedding")
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# Run inference
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-
preds = model("
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```
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<!--
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@@ -124,15 +125,15 @@ preds = model("κΈ°μ
μ΄ νμ° μ μ²μ ν λ μ±λ¬΄μμ μ£Όλ μ±
μ λ²
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### Training Set Metrics
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| Training set | Min | Median | Max |
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|:-------------|:----|:--------|:----|
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| Word count | 6 | 12.
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| Label | Training Sample Count |
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|:-----------|:----------------------|
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| μμ½ | 105 |
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| μ€λ³΅μ± κ²ν |
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| νΉν μ§μμ 보 μ 곡 |
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| μ μ¬λ¬Έμ | 115 |
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| μ€νμ νμ§ |
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### Training Hyperparameters
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- batch_size: (64, 64)
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μ΄ νμ° μ μ²μ ν λ μ±λ¬΄μμ μ£Όλ μ±
μ λ²
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### Training Results
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| Epoch | Step | Training Loss | Validation Loss |
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|:-------:|:--------:|:-------------:|:---------------:|
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| 9.7919 | 32000 | 0.0 | - |
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| 9.8072 | 32050 | 0.0 | - |
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| 9.8225 | 32100 | 0.0 | - |
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-
| 9.8378 | 32150 | 0.0 | - |
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| 9.8684 | 32250 | 0.0 | - |
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| 9.8837 | 32300 | 0.0 | - |
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| 9.8990 | 32350 | 0.0 | - |
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| 9.9143 | 32400 | 0.0 | - |
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| 9.9296 | 32450 | 0.0 | - |
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| 9.9449 | 32500 | 0.0 | - |
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-
| 9.9602 | 32550 | 0.0 | - |
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-
| 9.9755 | 32600 | 0.0 | - |
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-
| 9.9908 | 32650 | 0.0 | - |
|
| 820 |
-
| 10.0 | 32680 | - | 0.0787 |
|
| 821 |
|
| 822 |
* The bold row denotes the saved checkpoint.
|
| 823 |
### Framework Versions
|
|
|
|
| 9 |
- text-classification
|
| 10 |
- generated_from_setfit_trainer
|
| 11 |
widget:
|
| 12 |
+
- text: μΈκ³΅μ§λ₯ μ±λ΄ κΈ°μ ν₯μμ λν μμ΄λμ΄κ° μλλ°, κ΄λ ¨λ μμ μμΈν μ λ³΄κ° λ΄κΈ΄ λ
Όλ¬Έμ΄λ λ³΄κ³ μλ₯Ό μ°Ύμμ€λμ?
|
| 13 |
+
- text: μ°κ΅¬ μλ£μ μλ‘ λΆλΆμ ν μ€λ‘ μμ½ν΄ μ€ μ μλμ?
|
| 14 |
+
- text: μ°λ¦¬ νμ¬μ HR μ μ±
κ°μ λ°©μμ λν κ³Όμ λ₯Ό μ§ν μ€μ΄μΌ. κ°μ μ£Όμ μ μ΄μ κ³Όμ μ μ΄λ€ λΆλΆμμ μ μ€λ³΅λμλμ§ κΆκΈν΄
|
| 15 |
+
- text: μ΄μ λ체μ μκ³ μ¨λμ κ΄ν μ°κ΅¬ μλ£λ₯Ό λͺ¨μΌκ³ μμ΄μ. μ¬κΈ°μ κ΄λ ¨λ μ μ¬ν μ°κ΅¬λ λ³΄κ³ μλ₯Ό μΆμ²λ°κ³ μΆμ΄μ
|
| 16 |
+
- text: 곡μ₯μμ λ°μνλ κ°μ€ λμΆ λ¬Έμ ν΄κ²°μ μν μμ€ν
μ κ°λ°νλ €κ³ νλλ°, μ΄μ κ°μ μΈ‘λ©΄μμ μ§νλ κΈ°μ‘΄ μ°κ΅¬λ λΉμ·ν νλ‘μ νΈκ°
|
| 17 |
+
μλμ§ μλ €μ£ΌμΈμ
|
| 18 |
inference: true
|
| 19 |
model-index:
|
| 20 |
- name: SetFit
|
|
|
|
| 28 |
split: test
|
| 29 |
metrics:
|
| 30 |
- type: accuracy
|
| 31 |
+
value: 0.9891304347826086
|
| 32 |
name: Accuracy
|
| 33 |
---
|
| 34 |
|
|
|
|
| 60 |
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
|
| 61 |
|
| 62 |
### Model Labels
|
| 63 |
+
| Label | Examples |
|
| 64 |
+
|:-----------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
| 65 |
+
| μ€νμ νμ§ | <ul><li>'κ±΄μΆ νλ‘μ νΈ μ€λͺ
λ¬Έμ₯μμ μ€νλ μλͺ»λ λ§μΆ€λ²μ μ°Ύμμ€.'</li><li>'κ²½μ λ³΄κ³ μ λ΄μ©μ λν μ€νμλ₯Ό κ²ν νκ³ μμ ν΄ λ릴 μ μμκΉμ?'</li><li>'κ²½μμ¬ λΆμ νλͺ© λ΄ λ¬Έμ₯ ꡬμ±μ μ€λ₯λ₯Ό μ§μ ν΄μ£Όκ² μ΅λκΉ?'</li></ul> |
|
| 66 |
+
| μμ½ | <ul><li>'(νΉμ λ
Όλ¬Έ μ λͺ©)μ κ²°λ‘ λ° ν₯ν μ°κ΅¬ λ°©ν₯μ λν΄ μμ μ μ λ¦¬ν΄ μ£ΌμΈμ.'</li><li>'(νΉμ νΉνλ²νΈ)λ₯Ό κΈ°λ°μΌλ‘ ν λ°λͺ
μ μ 체μ μΈ κ°λ
μ μ§§κ² μ€λͺ
λΆνλ립λλ€.'</li><li>'1μ₯μ λ°μ΄ν° μμ§ κΈ°μ μ λν΄ μμ½ν΄μ£ΌμΈμ'</li></ul> |
|
| 67 |
+
| μ μ¬λ¬Έμ | <ul><li>'5G ν΅μ λͺ¨λ μ΅μ νμ κ΄λ ¨λ νλ‘μ νΈλ₯Ό νκ³ μλλ°, λΉμ·ν λ΄μ©μ νλ‘μ νΈλ λ
Όλ¬Έμ΄ μλμ§ μ°κ²°ν΄μ λ§ν΄μ€λ?'</li><li>'AI κΈ°λ° ν¬μ€μΌμ΄ μ루μ
κ°λ°μ κ΄ν λ¬Έν μ‘°μ¬λ₯Ό νκ³ μμ΅λλ€. μ κ°μ μ£Όμ λ₯Ό λ€λ£¬ λ¬Έμλ₯Ό μ°Ύμμ€ μ μμκΉμ?'</li><li>'AI μ°μ° μλλ₯Ό μ΅μ ννκΈ° μν λ°λ체 μ€κ³ λ°©μμ μ°κ΅¬νκ³ μμ΄. κ΄λ ¨λ μ μ¬ν λ
Όλ¬Έμ΄λ λ³΄κ³ μλ₯Ό μ°Ύκ³ μΆμ΄'</li></ul> |
|
| 68 |
+
| μ€λ³΅μ± κ²ν | <ul><li>'5G ν΅μ λ§μ κΈ°λ°μΌλ‘ μ€λ§νΈ μν° κ΅¬μΆμ κ΄ν μ°κ΅¬λ₯Ό μμνμ΄. μ΄μ λμΌνκ±°λ κ²ΉμΉλ μ°κ΅¬ κ³Όμ λ νλ‘μ νΈκ° μλμ§ μμλ΄μ£Όκ³ , μ΄μ λ λͺ
ννκ² λ°νμ€'</li><li>'건물μ λ΄μ§ μ€κ³ κ°ν λ°©μμ μ‘°μ¬νκ³ μλλ° μ΄μ μ°κ΄λ κΈ°μ‘΄ νλ‘μ νΈκ° 무μμ΄ μλμ§ κ·Έλ¦¬κ³ μ κ²ΉμΉλμ§ λ§ν΄μ€λ?'</li><li>'κ³ μ±λ₯ λ©λͺ¨λ¦¬ μμμ λ΄κ΅¬μ±μ ν₯μμν€λ κΈ°μ μ κ°λ°νκ³ μμ΄. μ΄μ λΉμ·ν κ³Όμ κ° μ΄μ μ μμλμ§, κ·Έλ¦¬κ³ μ΄λ»κ² μ μ¬νκ±°λ μ€λ³΅λλμ§ λ§ν΄μ€'</li></ul> |
|
| 69 |
+
| νΉν μ§μμ 보 μ 곡 | <ul><li>'3D κΈμ λ°°μ κΈ°μ (HBM, TSV)μ λμ
μΌλ‘ μΈν μ λ ₯ μλΉ κ°μ λ°©μμλ μ΄λ€ κ²μ΄ μλκ°μ?'</li><li>'AI μν¬λ‘λλ₯Ό μ²λ¦¬νκΈ° μν λ°λ체 μν€ν
μ² μ€κ³μμλ μ΄λ€ μ λ΅λ€μ΄ μ¬μ©λλμ?'</li><li>'LEED μΈμ¦μ κΈ°μ€κ³Ό νλ κ³Όμ μ λν΄ μκ³ μΆμ΅λλ€.'</li></ul> |
|
| 70 |
|
| 71 |
## Evaluation
|
| 72 |
|
| 73 |
### Metrics
|
| 74 |
| Label | Accuracy |
|
| 75 |
|:--------|:---------|
|
| 76 |
+
| **all** | 0.9891 |
|
| 77 |
|
| 78 |
## Uses
|
| 79 |
|
|
|
|
| 93 |
# Download from the π€ Hub
|
| 94 |
model = SetFitModel.from_pretrained("NTIS/kepri-embedding")
|
| 95 |
# Run inference
|
| 96 |
+
preds = model("μ°κ΅¬ μλ£μ μλ‘ λΆλΆμ ν μ€λ‘ μμ½ν΄ μ€ μ μλμ?")
|
| 97 |
```
|
| 98 |
|
| 99 |
<!--
|
|
|
|
| 125 |
### Training Set Metrics
|
| 126 |
| Training set | Min | Median | Max |
|
| 127 |
|:-------------|:----|:--------|:----|
|
| 128 |
+
| Word count | 6 | 12.4709 | 27 |
|
| 129 |
|
| 130 |
| Label | Training Sample Count |
|
| 131 |
|:-----------|:----------------------|
|
| 132 |
| μμ½ | 105 |
|
| 133 |
+
| μ€λ³΅μ± κ²ν | 78 |
|
| 134 |
+
| νΉν μ§μμ 보 μ 곡 | 106 |
|
| 135 |
| μ μ¬λ¬Έμ | 115 |
|
| 136 |
+
| μ€νμ νμ§ | 95 |
|
| 137 |
|
| 138 |
### Training Hyperparameters
|
| 139 |
- batch_size: (64, 64)
|
|
|
|
| 155 |
### Training Results
|
| 156 |
| Epoch | Step | Training Loss | Validation Loss |
|
| 157 |
|:-------:|:--------:|:-------------:|:---------------:|
|
| 158 |
+
| 0.0003 | 1 | 0.2062 | - |
|
| 159 |
+
| 0.0161 | 50 | 0.2314 | - |
|
| 160 |
+
| 0.0322 | 100 | 0.2008 | - |
|
| 161 |
+
| 0.0484 | 150 | 0.1395 | - |
|
| 162 |
+
| 0.0645 | 200 | 0.11 | - |
|
| 163 |
+
| 0.0806 | 250 | 0.0872 | - |
|
| 164 |
+
| 0.0967 | 300 | 0.0462 | - |
|
| 165 |
+
| 0.1129 | 350 | 0.0188 | - |
|
| 166 |
+
| 0.1290 | 400 | 0.0201 | - |
|
| 167 |
+
| 0.1451 | 450 | 0.025 | - |
|
| 168 |
+
| 0.1612 | 500 | 0.004 | - |
|
| 169 |
+
| 0.1774 | 550 | 0.002 | - |
|
| 170 |
+
| 0.1935 | 600 | 0.0153 | - |
|
| 171 |
+
| 0.2096 | 650 | 0.0011 | - |
|
| 172 |
+
| 0.2257 | 700 | 0.0007 | - |
|
| 173 |
+
| 0.2419 | 750 | 0.0006 | - |
|
| 174 |
+
| 0.2580 | 800 | 0.0006 | - |
|
| 175 |
+
| 0.2741 | 850 | 0.0005 | - |
|
| 176 |
+
| 0.2902 | 900 | 0.0004 | - |
|
| 177 |
+
| 0.3064 | 950 | 0.0005 | - |
|
| 178 |
+
| 0.3225 | 1000 | 0.0002 | - |
|
| 179 |
+
| 0.3386 | 1050 | 0.0002 | - |
|
| 180 |
+
| 0.3547 | 1100 | 0.0003 | - |
|
| 181 |
+
| 0.3708 | 1150 | 0.0002 | - |
|
| 182 |
+
| 0.3870 | 1200 | 0.0002 | - |
|
| 183 |
+
| 0.4031 | 1250 | 0.0002 | - |
|
| 184 |
+
| 0.4192 | 1300 | 0.0001 | - |
|
| 185 |
+
| 0.4353 | 1350 | 0.0002 | - |
|
| 186 |
+
| 0.4515 | 1400 | 0.0001 | - |
|
| 187 |
+
| 0.4676 | 1450 | 0.0001 | - |
|
| 188 |
+
| 0.4837 | 1500 | 0.0001 | - |
|
| 189 |
+
| 0.4998 | 1550 | 0.0001 | - |
|
| 190 |
+
| 0.5160 | 1600 | 0.0001 | - |
|
| 191 |
+
| 0.5321 | 1650 | 0.0001 | - |
|
| 192 |
+
| 0.5482 | 1700 | 0.0001 | - |
|
| 193 |
+
| 0.5643 | 1750 | 0.0001 | - |
|
| 194 |
+
| 0.5805 | 1800 | 0.0001 | - |
|
| 195 |
+
| 0.5966 | 1850 | 0.0001 | - |
|
| 196 |
+
| 0.6127 | 1900 | 0.0001 | - |
|
| 197 |
+
| 0.6288 | 1950 | 0.0001 | - |
|
| 198 |
+
| 0.6450 | 2000 | 0.0001 | - |
|
| 199 |
+
| 0.6611 | 2050 | 0.0001 | - |
|
| 200 |
+
| 0.6772 | 2100 | 0.0001 | - |
|
| 201 |
+
| 0.6933 | 2150 | 0.0001 | - |
|
| 202 |
+
| 0.7094 | 2200 | 0.0001 | - |
|
| 203 |
+
| 0.7256 | 2250 | 0.0001 | - |
|
| 204 |
+
| 0.7417 | 2300 | 0.0001 | - |
|
| 205 |
+
| 0.7578 | 2350 | 0.0001 | - |
|
| 206 |
+
| 0.7739 | 2400 | 0.0001 | - |
|
| 207 |
+
| 0.7901 | 2450 | 0.0001 | - |
|
| 208 |
+
| 0.8062 | 2500 | 0.0001 | - |
|
| 209 |
+
| 0.8223 | 2550 | 0.0001 | - |
|
| 210 |
+
| 0.8384 | 2600 | 0.0 | - |
|
| 211 |
+
| 0.8546 | 2650 | 0.0 | - |
|
| 212 |
+
| 0.8707 | 2700 | 0.0 | - |
|
| 213 |
+
| 0.8868 | 2750 | 0.0001 | - |
|
| 214 |
+
| 0.9029 | 2800 | 0.0 | - |
|
| 215 |
+
| 0.9191 | 2850 | 0.0001 | - |
|
| 216 |
+
| 0.9352 | 2900 | 0.0 | - |
|
| 217 |
+
| 0.9513 | 2950 | 0.0 | - |
|
| 218 |
+
| 0.9674 | 3000 | 0.0 | - |
|
| 219 |
+
| 0.9836 | 3050 | 0.0 | - |
|
| 220 |
+
| 0.9997 | 3100 | 0.0 | - |
|
| 221 |
+
| **1.0** | **3101** | **-** | **0.0247** |
|
| 222 |
+
| 1.0158 | 3150 | 0.0 | - |
|
| 223 |
+
| 1.0319 | 3200 | 0.0 | - |
|
| 224 |
+
| 1.0480 | 3250 | 0.0 | - |
|
| 225 |
+
| 1.0642 | 3300 | 0.0001 | - |
|
| 226 |
+
| 1.0803 | 3350 | 0.0 | - |
|
| 227 |
+
| 1.0964 | 3400 | 0.0 | - |
|
| 228 |
+
| 1.1125 | 3450 | 0.0 | - |
|
| 229 |
+
| 1.1287 | 3500 | 0.0 | - |
|
| 230 |
+
| 1.1448 | 3550 | 0.0 | - |
|
| 231 |
+
| 1.1609 | 3600 | 0.0 | - |
|
| 232 |
+
| 1.1770 | 3650 | 0.0 | - |
|
| 233 |
+
| 1.1932 | 3700 | 0.0 | - |
|
| 234 |
+
| 1.2093 | 3750 | 0.0 | - |
|
| 235 |
+
| 1.2254 | 3800 | 0.0 | - |
|
| 236 |
+
| 1.2415 | 3850 | 0.0 | - |
|
| 237 |
+
| 1.2577 | 3900 | 0.0 | - |
|
| 238 |
+
| 1.2738 | 3950 | 0.0 | - |
|
| 239 |
+
| 1.2899 | 4000 | 0.0 | - |
|
| 240 |
+
| 1.3060 | 4050 | 0.0 | - |
|
| 241 |
+
| 1.3222 | 4100 | 0.0 | - |
|
| 242 |
+
| 1.3383 | 4150 | 0.0 | - |
|
| 243 |
+
| 1.3544 | 4200 | 0.0 | - |
|
| 244 |
+
| 1.3705 | 4250 | 0.0 | - |
|
| 245 |
+
| 1.3866 | 4300 | 0.0 | - |
|
| 246 |
+
| 1.4028 | 4350 | 0.0 | - |
|
| 247 |
+
| 1.4189 | 4400 | 0.0 | - |
|
| 248 |
+
| 1.4350 | 4450 | 0.0 | - |
|
| 249 |
+
| 1.4511 | 4500 | 0.0 | - |
|
| 250 |
+
| 1.4673 | 4550 | 0.0 | - |
|
| 251 |
+
| 1.4834 | 4600 | 0.0 | - |
|
| 252 |
+
| 1.4995 | 4650 | 0.0 | - |
|
| 253 |
+
| 1.5156 | 4700 | 0.0 | - |
|
| 254 |
+
| 1.5318 | 4750 | 0.0 | - |
|
| 255 |
+
| 1.5479 | 4800 | 0.0 | - |
|
| 256 |
+
| 1.5640 | 4850 | 0.0 | - |
|
| 257 |
+
| 1.5801 | 4900 | 0.0 | - |
|
| 258 |
+
| 1.5963 | 4950 | 0.0 | - |
|
| 259 |
+
| 1.6124 | 5000 | 0.0 | - |
|
| 260 |
+
| 1.6285 | 5050 | 0.0 | - |
|
| 261 |
+
| 1.6446 | 5100 | 0.0 | - |
|
| 262 |
+
| 1.6608 | 5150 | 0.0 | - |
|
| 263 |
+
| 1.6769 | 5200 | 0.0 | - |
|
| 264 |
+
| 1.6930 | 5250 | 0.0 | - |
|
| 265 |
+
| 1.7091 | 5300 | 0.0 | - |
|
| 266 |
+
| 1.7252 | 5350 | 0.0 | - |
|
| 267 |
+
| 1.7414 | 5400 | 0.0 | - |
|
| 268 |
+
| 1.7575 | 5450 | 0.0 | - |
|
| 269 |
+
| 1.7736 | 5500 | 0.0 | - |
|
| 270 |
+
| 1.7897 | 5550 | 0.0 | - |
|
| 271 |
+
| 1.8059 | 5600 | 0.0 | - |
|
| 272 |
+
| 1.8220 | 5650 | 0.0 | - |
|
| 273 |
+
| 1.8381 | 5700 | 0.0 | - |
|
| 274 |
+
| 1.8542 | 5750 | 0.0 | - |
|
| 275 |
+
| 1.8704 | 5800 | 0.0 | - |
|
| 276 |
+
| 1.8865 | 5850 | 0.0 | - |
|
| 277 |
+
| 1.9026 | 5900 | 0.0 | - |
|
| 278 |
+
| 1.9187 | 5950 | 0.0 | - |
|
| 279 |
+
| 1.9349 | 6000 | 0.0 | - |
|
| 280 |
+
| 1.9510 | 6050 | 0.0 | - |
|
| 281 |
+
| 1.9671 | 6100 | 0.0 | - |
|
| 282 |
+
| 1.9832 | 6150 | 0.0 | - |
|
| 283 |
+
| 1.9994 | 6200 | 0.0 | - |
|
| 284 |
+
| 2.0 | 6202 | - | 0.0262 |
|
| 285 |
+
| 2.0155 | 6250 | 0.0 | - |
|
| 286 |
+
| 2.0316 | 6300 | 0.0 | - |
|
| 287 |
+
| 2.0477 | 6350 | 0.0 | - |
|
| 288 |
+
| 2.0639 | 6400 | 0.0 | - |
|
| 289 |
+
| 2.0800 | 6450 | 0.0 | - |
|
| 290 |
+
| 2.0961 | 6500 | 0.0 | - |
|
| 291 |
+
| 2.1122 | 6550 | 0.0 | - |
|
| 292 |
+
| 2.1283 | 6600 | 0.0 | - |
|
| 293 |
+
| 2.1445 | 6650 | 0.0 | - |
|
| 294 |
+
| 2.1606 | 6700 | 0.0 | - |
|
| 295 |
+
| 2.1767 | 6750 | 0.0 | - |
|
| 296 |
+
| 2.1928 | 6800 | 0.0 | - |
|
| 297 |
+
| 2.2090 | 6850 | 0.0 | - |
|
| 298 |
+
| 2.2251 | 6900 | 0.0 | - |
|
| 299 |
+
| 2.2412 | 6950 | 0.0 | - |
|
| 300 |
+
| 2.2573 | 7000 | 0.0 | - |
|
| 301 |
+
| 2.2735 | 7050 | 0.0 | - |
|
| 302 |
+
| 2.2896 | 7100 | 0.0 | - |
|
| 303 |
+
| 2.3057 | 7150 | 0.0 | - |
|
| 304 |
+
| 2.3218 | 7200 | 0.0 | - |
|
| 305 |
+
| 2.3380 | 7250 | 0.0 | - |
|
| 306 |
+
| 2.3541 | 7300 | 0.0 | - |
|
| 307 |
+
| 2.3702 | 7350 | 0.0 | - |
|
| 308 |
+
| 2.3863 | 7400 | 0.0 | - |
|
| 309 |
+
| 2.4025 | 7450 | 0.0 | - |
|
| 310 |
+
| 2.4186 | 7500 | 0.0 | - |
|
| 311 |
+
| 2.4347 | 7550 | 0.0 | - |
|
| 312 |
+
| 2.4508 | 7600 | 0.0 | - |
|
| 313 |
+
| 2.4669 | 7650 | 0.0 | - |
|
| 314 |
+
| 2.4831 | 7700 | 0.0 | - |
|
| 315 |
+
| 2.4992 | 7750 | 0.0 | - |
|
| 316 |
+
| 2.5153 | 7800 | 0.0 | - |
|
| 317 |
+
| 2.5314 | 7850 | 0.0 | - |
|
| 318 |
+
| 2.5476 | 7900 | 0.0 | - |
|
| 319 |
+
| 2.5637 | 7950 | 0.0 | - |
|
| 320 |
+
| 2.5798 | 8000 | 0.0 | - |
|
| 321 |
+
| 2.5959 | 8050 | 0.0 | - |
|
| 322 |
+
| 2.6121 | 8100 | 0.0 | - |
|
| 323 |
+
| 2.6282 | 8150 | 0.0 | - |
|
| 324 |
+
| 2.6443 | 8200 | 0.0 | - |
|
| 325 |
+
| 2.6604 | 8250 | 0.0 | - |
|
| 326 |
+
| 2.6766 | 8300 | 0.0 | - |
|
| 327 |
+
| 2.6927 | 8350 | 0.0 | - |
|
| 328 |
+
| 2.7088 | 8400 | 0.0 | - |
|
| 329 |
+
| 2.7249 | 8450 | 0.0 | - |
|
| 330 |
+
| 2.7411 | 8500 | 0.0 | - |
|
| 331 |
+
| 2.7572 | 8550 | 0.0 | - |
|
| 332 |
+
| 2.7733 | 8600 | 0.0 | - |
|
| 333 |
+
| 2.7894 | 8650 | 0.0 | - |
|
| 334 |
+
| 2.8055 | 8700 | 0.0 | - |
|
| 335 |
+
| 2.8217 | 8750 | 0.0 | - |
|
| 336 |
+
| 2.8378 | 8800 | 0.0 | - |
|
| 337 |
+
| 2.8539 | 8850 | 0.0 | - |
|
| 338 |
+
| 2.8700 | 8900 | 0.0 | - |
|
| 339 |
+
| 2.8862 | 8950 | 0.0 | - |
|
| 340 |
+
| 2.9023 | 9000 | 0.0 | - |
|
| 341 |
+
| 2.9184 | 9050 | 0.0 | - |
|
| 342 |
+
| 2.9345 | 9100 | 0.0 | - |
|
| 343 |
+
| 2.9507 | 9150 | 0.0 | - |
|
| 344 |
+
| 2.9668 | 9200 | 0.0 | - |
|
| 345 |
+
| 2.9829 | 9250 | 0.0 | - |
|
| 346 |
+
| 2.9990 | 9300 | 0.0 | - |
|
| 347 |
+
| 3.0 | 9303 | - | 0.025 |
|
| 348 |
+
| 3.0152 | 9350 | 0.0 | - |
|
| 349 |
+
| 3.0313 | 9400 | 0.0 | - |
|
| 350 |
+
| 3.0474 | 9450 | 0.0 | - |
|
| 351 |
+
| 3.0635 | 9500 | 0.0 | - |
|
| 352 |
+
| 3.0797 | 9550 | 0.0 | - |
|
| 353 |
+
| 3.0958 | 9600 | 0.0 | - |
|
| 354 |
+
| 3.1119 | 9650 | 0.0 | - |
|
| 355 |
+
| 3.1280 | 9700 | 0.0 | - |
|
| 356 |
+
| 3.1441 | 9750 | 0.0 | - |
|
| 357 |
+
| 3.1603 | 9800 | 0.0 | - |
|
| 358 |
+
| 3.1764 | 9850 | 0.0 | - |
|
| 359 |
+
| 3.1925 | 9900 | 0.0 | - |
|
| 360 |
+
| 3.2086 | 9950 | 0.0 | - |
|
| 361 |
+
| 3.2248 | 10000 | 0.0 | - |
|
| 362 |
+
| 3.2409 | 10050 | 0.0 | - |
|
| 363 |
+
| 3.2570 | 10100 | 0.0 | - |
|
| 364 |
+
| 3.2731 | 10150 | 0.0 | - |
|
| 365 |
+
| 3.2893 | 10200 | 0.0 | - |
|
| 366 |
+
| 3.3054 | 10250 | 0.0 | - |
|
| 367 |
+
| 3.3215 | 10300 | 0.0 | - |
|
| 368 |
+
| 3.3376 | 10350 | 0.0 | - |
|
| 369 |
+
| 3.3538 | 10400 | 0.0 | - |
|
| 370 |
+
| 3.3699 | 10450 | 0.0 | - |
|
| 371 |
+
| 3.3860 | 10500 | 0.0 | - |
|
| 372 |
+
| 3.4021 | 10550 | 0.0 | - |
|
| 373 |
+
| 3.4183 | 10600 | 0.0 | - |
|
| 374 |
+
| 3.4344 | 10650 | 0.0 | - |
|
| 375 |
+
| 3.4505 | 10700 | 0.0 | - |
|
| 376 |
+
| 3.4666 | 10750 | 0.0083 | - |
|
| 377 |
+
| 3.4827 | 10800 | 0.0019 | - |
|
| 378 |
+
| 3.4989 | 10850 | 0.0001 | - |
|
| 379 |
+
| 3.5150 | 10900 | 0.0 | - |
|
| 380 |
+
| 3.5311 | 10950 | 0.001 | - |
|
| 381 |
+
| 3.5472 | 11000 | 0.0 | - |
|
| 382 |
+
| 3.5634 | 11050 | 0.0 | - |
|
| 383 |
+
| 3.5795 | 11100 | 0.0 | - |
|
| 384 |
+
| 3.5956 | 11150 | 0.0 | - |
|
| 385 |
+
| 3.6117 | 11200 | 0.0 | - |
|
| 386 |
+
| 3.6279 | 11250 | 0.0 | - |
|
| 387 |
+
| 3.6440 | 11300 | 0.0 | - |
|
| 388 |
+
| 3.6601 | 11350 | 0.0 | - |
|
| 389 |
+
| 3.6762 | 11400 | 0.0 | - |
|
| 390 |
+
| 3.6924 | 11450 | 0.0 | - |
|
| 391 |
+
| 3.7085 | 11500 | 0.0 | - |
|
| 392 |
+
| 3.7246 | 11550 | 0.0 | - |
|
| 393 |
+
| 3.7407 | 11600 | 0.0 | - |
|
| 394 |
+
| 3.7569 | 11650 | 0.0 | - |
|
| 395 |
+
| 3.7730 | 11700 | 0.0 | - |
|
| 396 |
+
| 3.7891 | 11750 | 0.0 | - |
|
| 397 |
+
| 3.8052 | 11800 | 0.0 | - |
|
| 398 |
+
| 3.8213 | 11850 | 0.0 | - |
|
| 399 |
+
| 3.8375 | 11900 | 0.0 | - |
|
| 400 |
+
| 3.8536 | 11950 | 0.0 | - |
|
| 401 |
+
| 3.8697 | 12000 | 0.0 | - |
|
| 402 |
+
| 3.8858 | 12050 | 0.0 | - |
|
| 403 |
+
| 3.9020 | 12100 | 0.0 | - |
|
| 404 |
+
| 3.9181 | 12150 | 0.0 | - |
|
| 405 |
+
| 3.9342 | 12200 | 0.0 | - |
|
| 406 |
+
| 3.9503 | 12250 | 0.0 | - |
|
| 407 |
+
| 3.9665 | 12300 | 0.0 | - |
|
| 408 |
+
| 3.9826 | 12350 | 0.0 | - |
|
| 409 |
+
| 3.9987 | 12400 | 0.0 | - |
|
| 410 |
+
| 4.0 | 12404 | - | 0.0253 |
|
| 411 |
+
| 4.0148 | 12450 | 0.0 | - |
|
| 412 |
+
| 4.0310 | 12500 | 0.0 | - |
|
| 413 |
+
| 4.0471 | 12550 | 0.0 | - |
|
| 414 |
+
| 4.0632 | 12600 | 0.0 | - |
|
| 415 |
+
| 4.0793 | 12650 | 0.0 | - |
|
| 416 |
+
| 4.0955 | 12700 | 0.0 | - |
|
| 417 |
+
| 4.1116 | 12750 | 0.0 | - |
|
| 418 |
+
| 4.1277 | 12800 | 0.0 | - |
|
| 419 |
+
| 4.1438 | 12850 | 0.0 | - |
|
| 420 |
+
| 4.1599 | 12900 | 0.0 | - |
|
| 421 |
+
| 4.1761 | 12950 | 0.0 | - |
|
| 422 |
+
| 4.1922 | 13000 | 0.0 | - |
|
| 423 |
+
| 4.2083 | 13050 | 0.0 | - |
|
| 424 |
+
| 4.2244 | 13100 | 0.0 | - |
|
| 425 |
+
| 4.2406 | 13150 | 0.0 | - |
|
| 426 |
+
| 4.2567 | 13200 | 0.0 | - |
|
| 427 |
+
| 4.2728 | 13250 | 0.0 | - |
|
| 428 |
+
| 4.2889 | 13300 | 0.0 | - |
|
| 429 |
+
| 4.3051 | 13350 | 0.0 | - |
|
| 430 |
+
| 4.3212 | 13400 | 0.0 | - |
|
| 431 |
+
| 4.3373 | 13450 | 0.0 | - |
|
| 432 |
+
| 4.3534 | 13500 | 0.0 | - |
|
| 433 |
+
| 4.3696 | 13550 | 0.0 | - |
|
| 434 |
+
| 4.3857 | 13600 | 0.0 | - |
|
| 435 |
+
| 4.4018 | 13650 | 0.0 | - |
|
| 436 |
+
| 4.4179 | 13700 | 0.0 | - |
|
| 437 |
+
| 4.4341 | 13750 | 0.0 | - |
|
| 438 |
+
| 4.4502 | 13800 | 0.0 | - |
|
| 439 |
+
| 4.4663 | 13850 | 0.0 | - |
|
| 440 |
+
| 4.4824 | 13900 | 0.0 | - |
|
| 441 |
+
| 4.4985 | 13950 | 0.0 | - |
|
| 442 |
+
| 4.5147 | 14000 | 0.0 | - |
|
| 443 |
+
| 4.5308 | 14050 | 0.0 | - |
|
| 444 |
+
| 4.5469 | 14100 | 0.0 | - |
|
| 445 |
+
| 4.5630 | 14150 | 0.0 | - |
|
| 446 |
+
| 4.5792 | 14200 | 0.0 | - |
|
| 447 |
+
| 4.5953 | 14250 | 0.0 | - |
|
| 448 |
+
| 4.6114 | 14300 | 0.0 | - |
|
| 449 |
+
| 4.6275 | 14350 | 0.0 | - |
|
| 450 |
+
| 4.6437 | 14400 | 0.0 | - |
|
| 451 |
+
| 4.6598 | 14450 | 0.0 | - |
|
| 452 |
+
| 4.6759 | 14500 | 0.0 | - |
|
| 453 |
+
| 4.6920 | 14550 | 0.0 | - |
|
| 454 |
+
| 4.7082 | 14600 | 0.0 | - |
|
| 455 |
+
| 4.7243 | 14650 | 0.0 | - |
|
| 456 |
+
| 4.7404 | 14700 | 0.0 | - |
|
| 457 |
+
| 4.7565 | 14750 | 0.0 | - |
|
| 458 |
+
| 4.7727 | 14800 | 0.0 | - |
|
| 459 |
+
| 4.7888 | 14850 | 0.0 | - |
|
| 460 |
+
| 4.8049 | 14900 | 0.0 | - |
|
| 461 |
+
| 4.8210 | 14950 | 0.0 | - |
|
| 462 |
+
| 4.8371 | 15000 | 0.0 | - |
|
| 463 |
+
| 4.8533 | 15050 | 0.0 | - |
|
| 464 |
+
| 4.8694 | 15100 | 0.0 | - |
|
| 465 |
+
| 4.8855 | 15150 | 0.0 | - |
|
| 466 |
+
| 4.9016 | 15200 | 0.0 | - |
|
| 467 |
+
| 4.9178 | 15250 | 0.0 | - |
|
| 468 |
+
| 4.9339 | 15300 | 0.0 | - |
|
| 469 |
+
| 4.9500 | 15350 | 0.0 | - |
|
| 470 |
+
| 4.9661 | 15400 | 0.0 | - |
|
| 471 |
+
| 4.9823 | 15450 | 0.0 | - |
|
| 472 |
+
| 4.9984 | 15500 | 0.0 | - |
|
| 473 |
+
| 5.0 | 15505 | - | 0.0259 |
|
| 474 |
+
| 5.0145 | 15550 | 0.0 | - |
|
| 475 |
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| 762 |
+
| 9.5937 | 29750 | 0.0 | - |
|
| 763 |
+
| 9.6098 | 29800 | 0.0 | - |
|
| 764 |
+
| 9.6259 | 29850 | 0.0 | - |
|
| 765 |
+
| 9.6421 | 29900 | 0.0 | - |
|
| 766 |
+
| 9.6582 | 29950 | 0.0 | - |
|
| 767 |
+
| 9.6743 | 30000 | 0.0 | - |
|
| 768 |
+
| 9.6904 | 30050 | 0.0 | - |
|
| 769 |
+
| 9.7065 | 30100 | 0.0 | - |
|
| 770 |
+
| 9.7227 | 30150 | 0.0 | - |
|
| 771 |
+
| 9.7388 | 30200 | 0.0 | - |
|
| 772 |
+
| 9.7549 | 30250 | 0.0 | - |
|
| 773 |
+
| 9.7710 | 30300 | 0.0 | - |
|
| 774 |
+
| 9.7872 | 30350 | 0.0 | - |
|
| 775 |
+
| 9.8033 | 30400 | 0.0 | - |
|
| 776 |
+
| 9.8194 | 30450 | 0.0 | - |
|
| 777 |
+
| 9.8355 | 30500 | 0.0 | - |
|
| 778 |
+
| 9.8517 | 30550 | 0.0 | - |
|
| 779 |
+
| 9.8678 | 30600 | 0.0 | - |
|
| 780 |
+
| 9.8839 | 30650 | 0.0 | - |
|
| 781 |
+
| 9.9000 | 30700 | 0.0 | - |
|
| 782 |
+
| 9.9162 | 30750 | 0.0 | - |
|
| 783 |
+
| 9.9323 | 30800 | 0.0 | - |
|
| 784 |
+
| 9.9484 | 30850 | 0.0 | - |
|
| 785 |
+
| 9.9645 | 30900 | 0.0 | - |
|
| 786 |
+
| 9.9807 | 30950 | 0.0 | - |
|
| 787 |
+
| 9.9968 | 31000 | 0.0 | - |
|
| 788 |
+
| 10.0 | 31010 | - | 0.0264 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 789 |
|
| 790 |
* The bold row denotes the saved checkpoint.
|
| 791 |
### Framework Versions
|
config.json
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
{
|
| 2 |
-
"_name_or_path": "checkpoints/
|
| 3 |
"architectures": [
|
| 4 |
"RobertaModel"
|
| 5 |
],
|
|
|
|
| 1 |
{
|
| 2 |
+
"_name_or_path": "checkpoints/step_3101/",
|
| 3 |
"architectures": [
|
| 4 |
"RobertaModel"
|
| 5 |
],
|
model_head.pkl
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 31807
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f4802da0698b467591672e8edc185b3106b923dfcd820f6940b3a716a20cdf29
|
| 3 |
size 31807
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 442537395
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bbad31734f3289fb3b4a559c9fe471b2ce2c509b62fd9f14fa9856733f431097
|
| 3 |
size 442537395
|