Push model using huggingface_hub.
Browse files- README.md +241 -240
- config_setfit.json +2 -2
- model.safetensors +1 -1
- model_head.pkl +1 -1
README.md
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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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inference: true
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model-index:
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- name: SetFit with klue/roberta-base
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split: test
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metrics:
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- type: metric
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value: 0.
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name: Metric
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---
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@@ -61,37 +62,37 @@ The model has been trained using an efficient few-shot learning technique that i
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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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| 9.0 | <ul><li>'
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## Evaluation
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### Metrics
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| Label | Metric |
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|:--------|:-------|
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| **all** | 0.
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## Uses
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# Download from the ๐ค Hub
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model = SetFitModel.from_pretrained("mini1013/master_item_fd")
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# Run inference
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preds = model("(
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```
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<!--
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์ด๋ฌต ๋ฐ์ฐฌ
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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 | 3 | 9.
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| Label | Training Sample Count |
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|:------|:----------------------|
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์ด๋ฌต ๋ฐ์ฐฌ
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### Training Results
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| Epoch | Step | Training Loss | Validation Loss |
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|:-------:|:-----:|:-------------:|:---------------:|
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| 0.0006 | 1 | 0.
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| 0.0316 | 50 | 0.
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| 0.2841 | 450 | 0.
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| 0.3472 | 550 | 0.
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| 0.5366 | 850 | 0.
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| 0.5682 | 900 | 0.
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| 0.8207 | 1300 | 0.
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| 0.8838 | 1400 | 0.
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| 0.9470 | 1500 | 0.
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| 1.0101 | 1600 | 0.
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| 1.0732 | 1700 | 0.
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| 1.1048 | 1750 | 0.
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| 1.1995 | 1900 | 0.
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| 2.4306 | 3850 | 0.0004 | - |
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์ด๋ฌต ๋ฐ์ฐฌ
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์ด๋ฌต ๋ฐ์ฐฌ
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| 472 |
| 8.7753 | 13900 | 0.0 | - |
|
| 473 |
| 8.8068 | 13950 | 0.0 | - |
|
| 474 |
| 8.8384 | 14000 | 0.0 | - |
|
|
@@ -482,7 +483,7 @@ preds = model("(๋๋)(Vegan)๋๋น์๋250g ์ฌ๋ผ์ด์ค ์ค๋
์ด๋ฌต ๋ฐ์ฐฌ
|
|
| 482 |
| 9.0909 | 14400 | 0.0 | - |
|
| 483 |
| 9.1225 | 14450 | 0.0 | - |
|
| 484 |
| 9.1540 | 14500 | 0.0 | - |
|
| 485 |
-
| 9.1856 | 14550 | 0.
|
| 486 |
| 9.2172 | 14600 | 0.0 | - |
|
| 487 |
| 9.2487 | 14650 | 0.0 | - |
|
| 488 |
| 9.2803 | 14700 | 0.0 | - |
|
|
@@ -499,18 +500,18 @@ preds = model("(๋๋)(Vegan)๋๋น์๋250g ์ฌ๋ผ์ด์ค ์ค๋
์ด๋ฌต ๋ฐ์ฐฌ
|
|
| 499 |
| 9.6275 | 15250 | 0.0 | - |
|
| 500 |
| 9.6591 | 15300 | 0.0 | - |
|
| 501 |
| 9.6907 | 15350 | 0.0 | - |
|
| 502 |
-
| 9.7222 | 15400 | 0.
|
| 503 |
| 9.7538 | 15450 | 0.0 | - |
|
| 504 |
-
| 9.7854 | 15500 | 0.
|
| 505 |
-
| 9.8169 | 15550 | 0.
|
| 506 |
| 9.8485 | 15600 | 0.0 | - |
|
| 507 |
| 9.8801 | 15650 | 0.0 | - |
|
| 508 |
-
| 9.9116 | 15700 | 0.
|
| 509 |
| 9.9432 | 15750 | 0.0 | - |
|
| 510 |
-
| 9.9747 | 15800 | 0.
|
| 511 |
| 10.0063 | 15850 | 0.0 | - |
|
| 512 |
| 10.0379 | 15900 | 0.0 | - |
|
| 513 |
-
| 10.0694 | 15950 | 0.
|
| 514 |
| 10.1010 | 16000 | 0.0 | - |
|
| 515 |
| 10.1326 | 16050 | 0.0 | - |
|
| 516 |
| 10.1641 | 16100 | 0.0 | - |
|
|
@@ -519,7 +520,7 @@ preds = model("(๋๋)(Vegan)๋๋น์๋250g ์ฌ๋ผ์ด์ค ์ค๋
์ด๋ฌต ๋ฐ์ฐฌ
|
|
| 519 |
| 10.2588 | 16250 | 0.0 | - |
|
| 520 |
| 10.2904 | 16300 | 0.0 | - |
|
| 521 |
| 10.3220 | 16350 | 0.0 | - |
|
| 522 |
-
| 10.3535 | 16400 | 0.
|
| 523 |
| 10.3851 | 16450 | 0.0 | - |
|
| 524 |
| 10.4167 | 16500 | 0.0 | - |
|
| 525 |
| 10.4482 | 16550 | 0.0 | - |
|
|
@@ -529,7 +530,7 @@ preds = model("(๋๋)(Vegan)๋๋น์๋250g ์ฌ๋ผ์ด์ค ์ค๋
์ด๋ฌต ๋ฐ์ฐฌ
|
|
| 529 |
| 10.5745 | 16750 | 0.0 | - |
|
| 530 |
| 10.6061 | 16800 | 0.0 | - |
|
| 531 |
| 10.6376 | 16850 | 0.0 | - |
|
| 532 |
-
| 10.6692 | 16900 | 0.
|
| 533 |
| 10.7008 | 16950 | 0.0 | - |
|
| 534 |
| 10.7323 | 17000 | 0.0 | - |
|
| 535 |
| 10.7639 | 17050 | 0.0 | - |
|
|
@@ -540,10 +541,10 @@ preds = model("(๋๋)(Vegan)๋๋น์๋250g ์ฌ๋ผ์ด์ค ์ค๋
์ด๋ฌต ๋ฐ์ฐฌ
|
|
| 540 |
| 10.9217 | 17300 | 0.0 | - |
|
| 541 |
| 10.9533 | 17350 | 0.0 | - |
|
| 542 |
| 10.9848 | 17400 | 0.0 | - |
|
| 543 |
-
| 11.0164 | 17450 | 0.
|
| 544 |
| 11.0480 | 17500 | 0.0 | - |
|
| 545 |
-
| 11.0795 | 17550 | 0.
|
| 546 |
-
| 11.1111 | 17600 | 0.
|
| 547 |
| 11.1427 | 17650 | 0.0 | - |
|
| 548 |
| 11.1742 | 17700 | 0.0 | - |
|
| 549 |
| 11.2058 | 17750 | 0.0 | - |
|
|
@@ -555,9 +556,9 @@ preds = model("(๋๋)(Vegan)๋๋น์๋250g ์ฌ๋ผ์ด์ค ์ค๋
์ด๋ฌต ๋ฐ์ฐฌ
|
|
| 555 |
| 11.3952 | 18050 | 0.0 | - |
|
| 556 |
| 11.4268 | 18100 | 0.0 | - |
|
| 557 |
| 11.4583 | 18150 | 0.0 | - |
|
| 558 |
-
| 11.4899 | 18200 | 0.
|
| 559 |
| 11.5215 | 18250 | 0.0 | - |
|
| 560 |
-
| 11.5530 | 18300 | 0.
|
| 561 |
| 11.5846 | 18350 | 0.0 | - |
|
| 562 |
| 11.6162 | 18400 | 0.0 | - |
|
| 563 |
| 11.6477 | 18450 | 0.0 | - |
|
|
@@ -604,7 +605,7 @@ preds = model("(๋๋)(Vegan)๋๋น์๋250g ์ฌ๋ผ์ด์ค ์ค๋
์ด๋ฌต ๋ฐ์ฐฌ
|
|
| 604 |
| 12.9419 | 20500 | 0.0 | - |
|
| 605 |
| 12.9735 | 20550 | 0.0 | - |
|
| 606 |
| 13.0051 | 20600 | 0.0 | - |
|
| 607 |
-
| 13.0366 | 20650 | 0.
|
| 608 |
| 13.0682 | 20700 | 0.0 | - |
|
| 609 |
| 13.0997 | 20750 | 0.0 | - |
|
| 610 |
| 13.1313 | 20800 | 0.0 | - |
|
|
@@ -612,7 +613,7 @@ preds = model("(๋๋)(Vegan)๋๋น์๋250g ์ฌ๋ผ์ด์ค ์ค๋
์ด๋ฌต ๋ฐ์ฐฌ
|
|
| 612 |
| 13.1944 | 20900 | 0.0 | - |
|
| 613 |
| 13.2260 | 20950 | 0.0 | - |
|
| 614 |
| 13.2576 | 21000 | 0.0 | - |
|
| 615 |
-
| 13.2891 | 21050 | 0.
|
| 616 |
| 13.3207 | 21100 | 0.0 | - |
|
| 617 |
| 13.3523 | 21150 | 0.0 | - |
|
| 618 |
| 13.3838 | 21200 | 0.0 | - |
|
|
@@ -624,7 +625,7 @@ preds = model("(๋๋)(Vegan)๋๋น์๋250g ์ฌ๋ผ์ด์ค ์ค๋
์ด๋ฌต ๋ฐ์ฐฌ
|
|
| 624 |
| 13.5732 | 21500 | 0.0 | - |
|
| 625 |
| 13.6048 | 21550 | 0.0 | - |
|
| 626 |
| 13.6364 | 21600 | 0.0 | - |
|
| 627 |
-
| 13.6679 | 21650 | 0.
|
| 628 |
| 13.6995 | 21700 | 0.0 | - |
|
| 629 |
| 13.7311 | 21750 | 0.0 | - |
|
| 630 |
| 13.7626 | 21800 | 0.0 | - |
|
|
@@ -636,14 +637,14 @@ preds = model("(๋๋)(Vegan)๋๋น์๋250g ์ฌ๋ผ์ด์ค ์ค๋
์ด๋ฌต ๋ฐ์ฐฌ
|
|
| 636 |
| 13.9520 | 22100 | 0.0 | - |
|
| 637 |
| 13.9836 | 22150 | 0.0 | - |
|
| 638 |
| 14.0152 | 22200 | 0.0 | - |
|
| 639 |
-
| 14.0467 | 22250 | 0.
|
| 640 |
| 14.0783 | 22300 | 0.0 | - |
|
| 641 |
| 14.1098 | 22350 | 0.0 | - |
|
| 642 |
| 14.1414 | 22400 | 0.0 | - |
|
| 643 |
| 14.1730 | 22450 | 0.0 | - |
|
| 644 |
-
| 14.2045 | 22500 | 0.
|
| 645 |
| 14.2361 | 22550 | 0.0 | - |
|
| 646 |
-
| 14.2677 | 22600 | 0.
|
| 647 |
| 14.2992 | 22650 | 0.0 | - |
|
| 648 |
| 14.3308 | 22700 | 0.0 | - |
|
| 649 |
| 14.3624 | 22750 | 0.0 | - |
|
|
@@ -687,7 +688,7 @@ preds = model("(๋๋)(Vegan)๋๋น์๋250g ์ฌ๋ผ์ด์ค ์ค๋
์ด๋ฌต ๋ฐ์ฐฌ
|
|
| 687 |
| 15.5619 | 24650 | 0.0 | - |
|
| 688 |
| 15.5934 | 24700 | 0.0 | - |
|
| 689 |
| 15.625 | 24750 | 0.0 | - |
|
| 690 |
-
| 15.6566 | 24800 | 0.
|
| 691 |
| 15.6881 | 24850 | 0.0 | - |
|
| 692 |
| 15.7197 | 24900 | 0.0 | - |
|
| 693 |
| 15.7513 | 24950 | 0.0 | - |
|
|
|
|
| 10 |
- text-classification
|
| 11 |
- generated_from_setfit_trainer
|
| 12 |
widget:
|
| 13 |
+
- text: ๐ฏ๊ตญ์ฐ์ ์กฐ์ฒญ๐ฏ ์์ ์ค๋๋ค ์ ๋ฌผ์ธํธ ๋ต๋กํ 18P 9์ 3์ผ ์ถ๊ณ (4์ผ~5์ผ ๋์ฐฉ์์ )_๊ฒฌ๊ณผ์คํ์
(์ค๋ฆฌ์ง๋8p+์คํ์
10p)_๊ฐ์ฌ์
|
| 14 |
+
๋ง์์ ์ ํฉ๋๋ค ์ํ๋ฃป(st.fruit)
|
| 15 |
+
- text: ํฌ์นด๋ฆฌ์ค์จํธ 245ml 1๊ฐ ์คํจํธ_๋ฝ๋ก๋ก(๋ฐํฌ๋ง) 235ML X 24๋ณ ์ฃผ์ํ์ฌ ์ก๋ฏผ
|
| 16 |
+
- text: ํฌ์นด๋ฆฌ์ค์จํธ 245ml 1๊ฐ ๋ฏธ๋์บ_ํฐ์คํผ ์ค์ํธ ์๋ฉ๋ฆฌ์นด๋
ธ 200ml 30๊ฐ ๋์์์ฌ
|
| 17 |
+
- text: ์์ดํฌ๋ฒ ์ด๋น ๋ค์ด์ดํธ ๋จ๋ฐฑ์ง ์์ดํฌ ๋ง์๋ ์์ฌ๋์ฉ ์๋จ ์์ ๋ธ๊ธฐ๋ง 750g 3+ํด๋ฉ๋ฐ์ค ๊ตฌ์ฑ_์ด์ฝ+์ค์์ฝ+๋ง์๋ฉ๋ก์ด์ฝ_ํด๋ฉ๋ฐ์ค+ํํฌ๋ณดํ
|
| 18 |
+
1๊ฐ+ํ์ดํธ๋ณดํ 1๊ฐ ์ฃผ์ํ์ฌ ์ฌ๋ก์ฐ๋ก์ผ
|
| 19 |
+
- text: ํฌ๋ด ์ด์๊ณ ์ํฐ์ง XV ์ค๋ฉ๊ฐ3 ๋ฏธ๋ ์นด์ ๋กํ
ํฌ๋ด ์ด์๊ณ ์๋ฌผ์ฑ ์ํฐ์ง ์ค๋ฉ๊ฐ3โณ (์ฃผ)ํฌํฐ๋ธ๋ดํธ๋ฆฌ์
|
| 20 |
inference: true
|
| 21 |
model-index:
|
| 22 |
- name: SetFit with klue/roberta-base
|
|
|
|
| 30 |
split: test
|
| 31 |
metrics:
|
| 32 |
- type: metric
|
| 33 |
+
value: 0.9180474602529828
|
| 34 |
name: Metric
|
| 35 |
---
|
| 36 |
|
|
|
|
| 62 |
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
|
| 63 |
|
| 64 |
### Model Labels
|
| 65 |
+
| Label | Examples |
|
| 66 |
+
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
| 67 |
+
| 9.0 | <ul><li>'๋๋ณด์ํ ๊น๋ง๋ 4kg ์๊ณ ๋ ค'</li><li>'[์ฐ๋คํด๋ฐ์ฐฌ]์ฝฉ์๋ฐ 200g ์ฃผ์ํ์ฌ ์ฐ๋คํด'</li><li>'์๊ฒ ๋ฌด์นจ 4kg ๋์ฉ๋ ์
์์ฉ ์๋น ๋ฐ์ฐฌ ๋ฐฉ๊ฒ ์กฐ๋ฆผ (์ ) ํ๋๋ง์ฌ๋์ํ'</li></ul> |
|
| 68 |
+
| 0.0 | <ul><li>'๋๋ ๋ฐ ์ฟ๊ธฐ๋ฆ๊ฐ๋ฃจ 1kg (๋ณตํฉ) ์ฃผ์ํ์ฌ ์ผ๋ถ'</li><li>'ํ๋ฆฌ๋ฏธ์ ์๋ชฌ๋๊ฐ๋ฃจ 1kg 95% ์๋ชฌ๋๋ถ๋ง ์๋ชฌ๋ํ์ฐ๋ ํ๋ฆฌ๋ฏธ์ ์๋ชฌ๋๋ถ๋ง(95%) 1kg ๋นํ๋ฏผํ๋ฌ์ค'</li><li>'์ค๋๊ธฐ ํ๊น๊ฐ๋ฃจ 2kg ๋ฆฌ์ผ์ ํต์ปดํผ๋'</li></ul> |
|
| 69 |
+
| 2.0 | <ul><li>'์๋ก์ด์ฝ์ด๋นํ ์ด์์ผ ์ผ๋ณธ๊ณผ์ ์ฟ ํฌ๋ค์ค ํ์์ฐ์ธ 9๊ฐ์
๋ํค๋ณด๋ฆฌ์ ์ฐ์ธ 12๋งค์
INCAPE CO.,LTD'</li><li>'์์ผ๋ฆฌํจ ์ค๋ฆฌ์ง๋ ๋ฆฌํ 115gx3๋ด ์ธ 5์ข
02.์์ผ๋ฆฌํจ ์ํ ๋ฆฌํ 102g_01.์์ผ๋ฆฌํจ ์ค๋ฆฌ์ง๋ ๋ฆฌํ 115g_06.์กธ์๋ฒ์ฉ๊ป ํกํก!87g (์ฃผ)ํธ๋์กฐ์ด'</li><li>'70g ์ถ์ต์ ๋๋์ค 1๊ฐ ์์ค์ ์ด(SJ)์กฐ์์ผํ'</li></ul> |
|
| 70 |
+
| 12.0 | <ul><li>'๋ฐ์ ์์คํธ๋ผ๋ฒ์ง ์ฌ๋ฆฌ๋ธ์ค์ผ 2L ์ 3์์ ๋ฐฐ์ก๊ด๋ จ ๊ฐ์ธ์ ๋ณด ์ด์ฉ์ ๋ํด ๋์ํจ ๋ฒ๋๋ฒ์ฆ'</li><li>'CJ์ ์ผ์ ๋น ๋ฐฑ์ค ์ฝฉ๊ธฐ๋ฆ 1.5L ์ฌ์กฐ ํดํ ์์ฉ์ 1.8L ์ผ์์ ํต'</li><li>'CJ [๋ง๋ฅ]๋ฐฑ์ค ๊ฑด๊ฐ์ ์๊ฐํ ์๋ฆฌ์ 900ml ๊ฐ์์์ฌ๋ฃ ์๋ง ๋ง์ง ๋ฏฟ๊ณ ๋จน๋ ์ฐ๋ฆฌ์ง ๊ฑด๊ฐํ ์์ฌ๋ฃ ๋ด๋ ์คํ ์ด'</li></ul> |
|
| 71 |
+
| 6.0 | <ul><li>'ํ๊ตญ์ ํ ํ๋ฒ๋ผ์ดํ ํ๋ฒํฐ ํ๋ฒ๋ฒ ๋ฒ๋ฆฌ์ง ๋ง๊ตฌ ์์ด'</li><li>'์คํค๋๋ฉ ๊ฐ๋ฒผ์์ง๋ ์์์ค ๋ค์ด์ดํธ 14ํฌ cissus ๋ณด์กฐ์ ์จ์์ค 15ํฌ ์ฌ๋ฐ๋ฅธ์์ ์ฃผ์ํ์ฌ'</li><li>'ํ๋กฌ๋ฐ์ด์ค ์ํ๋ฆฌ์นด๋ง๊ณ ์ํฐ๋ฏน์ค ๋ ๋ชฌ๋ง 2์ฃผ 14ํฌx1๋ฐ์ค ์ํ๋ฆฌ์นด ๋ง๊ณ ์ํฐ๋ฏน์ค ๋ ๋ชฌ 2์ฃผ ์ฃผ์ํ์ฌ ํ๋กฌ๋ฐ์ด์ค'</li></ul> |
|
| 72 |
+
| 18.0 | <ul><li>'์ธ์ฐ์ฃฝ์ผ 9ํ ์์ฃฝ์ผ ๊ณ ์ฒด 60g ๋นํ์ฝ์ด'</li><li>'[์ฒญ์ ์] ์ํ์ถ 50g (๊ฒฝ์ฐ์ ) ์ฃผ์ํ์ฌ ์์ค์์ค์ง๋ท์ปด'</li><li>'๋ธ๋๊ทธ ์ ๊ธฐ๋ ์ด๋ชจ ์ฌ๊ณผ์์ด 946ml ์ฒ์ฐ๋ฐํจ ์์ดํธ๋ฆญ ๋ฐ๋๊ทธ๋ฆฌ์ค ์ ํ์ฌ์ด๋ค๋น๋๊ฑฐ ๋๋๊ทธ๋ฆฌ์ค ์ฌ๊ณผ์์ด 1000ml ์ธ์์ด๋ค'</li></ul> |
|
| 73 |
+
| 4.0 | <ul><li>'๋์ ์๋ฐ ์์๋ญ์ฃฝ 285g ํ์ฑ์ ํต'</li><li>'ํธ๋๋ฒ๋ ์ซ์์นํจ๋๊ฒ500g ๋ฏธ๋๊น์ค 2์ข
480g 1+1 ๋ฏธ๋๋๊น์ค 1๊ฐ+๋ฏธ๋์นํจ๊น์ค 1๊ฐ ๋ค์ํ๋์'</li><li>'[์ผ์ง์ด๋ฌต] 100์ฌ๊ฐ 1๋ด 320g (10๊ฐ์
) (์ฃผ)๋๋ฐฉ์ ๋'</li></ul> |
|
| 74 |
+
| 21.0 | <ul><li>'[๋ฆฌ์ค์ฐํ ] ํ ๋ผํ๋จ ์ฌ๋ผ์ด์ค 540g (๊ฒฝ์ฐ์ ) ์ฃผ์ํ์ฌ ์์ค์์ค์ง๋ท์ปด'</li><li>'์ฌ์กฐ ์์ฌ ๋ญ๊ฐ์ด์ด 90g ์ฃผ์ํ์ฌ ํด๋ฒ๋ฆฌ๋ง์คํ'</li><li>'์ํ ๊ฝ์น 400g ์ง์์ฐ์
'</li></ul> |
|
| 75 |
+
| 17.0 | <ul><li>'๊ณฐํ ํต๋ฐ ํธ๋ก๋ฏน์ค 450g x 4 ์ฝ์คํธ์ปท๋ชฐ'</li><li>'CJ ํซ์ผ์ต๋ฏน์ค 1kg ํํํฐ ์๋ง์๋ง ์์ผํํฐ ํ๋ฉ์ด๋ ๋ฐ์ญํ ๋ฐํคํธ ๋ง์ด์ปดํผ๋'</li><li>'๊ณฐํ ํต๋ฐ ํธ๋ก๋ฏน์ค 450g x 4๊ฐ ์ฝ์คํธ์ฝ ํ๋ฒ ์ดํน ํธ๋ก ์ฌ๋ฃ ์ด์ง์ฝ์ค'</li></ul> |
|
| 76 |
+
| 20.0 | <ul><li>'๋ผ์ง ๋จธ๋ฆฟ๊ณ ๊ธฐ ์ฌ๋ผ์ด์ค ์
์์ฉ ๋จธ๋ฆฌ๊ณ ๊ธฐ 1kg ๋ผ์ง๊ตญ๋ฐฅ์ฉ ์ ๋จ ์๋๊ตญ ์ฐฝ์
์ฌ๋ฃ -์ ์ง์๋ ๋งค์ฝค ๋ฐฑํ ์ข
์๋ 500g X 2ํฉ ํธ๋์จ'</li><li>'ํ์ผ์ ์บ ํ๊ณ ๊ธฐ ๊ตฌ์ด 300g ๊ตญ๋ด์ฐ ์์ปท์ ๋ฌธ ๋ณด์์ ์ผ์๊ณ ๊ธฐ ์์ก ์ ๊ณจ ํ ์๋ฆฌ ์์ โ์์ก์ฉ ๋ท๋ค๋ฆฌ์ด 500g (๊ป๋ฐ๊ธฐ+๊ณ ๊ธฐ) ํ์ผ์์ฐ๊ตฌ์'</li><li>'ํํฐํ ๊ท์กฑ ํต๋ผ์ง๋ฐ๋ฒ ํ (5-10์ธ๋ถ) ๋งํ๊ณ ๊ธฐ ์บ ํ์์ ์ง๋ค์ด ์ถ์ฅ ๊ฒฝ์ฃผ์์ธ๋ฒ์คํฐ๋ฏธ๋_1/6ํ์ฒด ์ฃผ์ํ์ฌ ํํฐํ'</li></ul> |
|
| 77 |
+
| 15.0 | <ul><li>'์นด์นด์ค๋์ค 500g ์ค๊ตฌ์ค๊ตฌ(5959)'</li><li>'์ ์ฃผ์ฝฉ์๋ซ๋ ํนํ๊ธฐ์ ๋ก ๋ง๋ ๋์์๊ณ ๋ง์๋ ๋ซ๋ 53 g x 7 ๊ฐ ์ ์ฃผ์ฝฉ ์๋ซ๋ 7 ๊ฐ (์ฃผ)์ผ๋ธ์์์์ค'</li><li>'[2+2] ํด์ฐฌ๋ค ์ฌ๊ณ์ ์์ฅ 500G ๋ฉ๊ฐ๊ธ๋ก๋ฒ001'</li></ul> |
|
| 78 |
+
| 14.0 | <ul><li>'๋ชฌ์คํฐ ์๋์ง ์ธํธ๋ผ ์ํธ๋ผ 355ml 1๊ฐ ์ฌ๋ฆผ์บ_๊ฒํ ๋ ์ด ๋ ๋ชฌ 240ml 30๊ฐ ์ฃผ์ํ์ฌ ์ก๋ฏผ'</li><li>'๋ฏธ๋ผ ํซ์ด์ฝ ์ค๋ฆฌ์ง๋ ๋ฏธ๋์คํฑ 40T+๋ณผํ ๋ฏธ๋ผ ๋ง์ผ๋10T x2๊ฐ+๋ณผํ์ฆ์ ์ ๋์ฝ๋งํธ'</li><li>'์ผํ ์ด์ ํ์ฐ์ 1.5L 6๊ฐ ์คํจํธ_์๋
์์ํฐ ์ค๋ ์ง์๋ชฝ๋ธ๋ํฐ 500ml 12๊ฐ ์ฃผ์ํ์ฌ ์ก๋ฏผ'</li></ul> |
|
| 79 |
+
| 10.0 | <ul><li>'ํ์ธ์ฆ ๋
ธ์๊ฐ ์ผ์ฐน (ํ์ธ์ฆ ๋ฆฌ๋์ค๋์๊ฐ ์ผ์ฐน) 369g (์ฃผ)์์ด๋ฏธ์ํ์์ค'</li><li>'๋์ ์ฒญ์ ์ ์ฐ๋ฆฌ์์ด ์ผ์ฐน 620g / 806kcal ์๋ฆฌZIP'</li><li>'์คํ
์ดํฌ์์ค ACE 260g ์คํ
์ดํฌ์์ค ์์์ฌ ๋งํธ ๋ค๋ผ์กฐ๋ช
'</li></ul> |
|
| 80 |
+
| 16.0 | <ul><li>'๋กํค๋ฆฟ์ง ๋ฉ์ดํ์๋ฝ 340g ์ธ์ปจ๋ ๋ฒ ์ด์ค'</li><li>'๋ณต์์๋ฆฌ ๋ธ๊ธฐ์ผ 500g 3๊ฐ ๋ธ๊ธฐ์ผ(3๊ฐ) ๋ธ๊ธฐ์ผ+์ ๋ฌผ์ฉ ์ข
์ด๊ฐ๋ฐฉ ๋์จ๋๋์ด'</li><li>'Chocolate Hazelnut Spread ์ ํ์์ '</li></ul> |
|
| 81 |
+
| 1.0 | <ul><li>'[๊ณต์] ๋ฅํฐ๊ฒ์ ํค์ฆ์ด๋ฎจ 20g x 14ํฌ (4+4ํํ) 8๋ฐ์ค [47%ํ ์ธ+๋ฌด๋ฃ๋ฐฐ์ก] ์ฃผ์ํ์ฌ ๊ทธ๋ฆฟ์ธํผ์ค'</li><li>'๋ฌ์์ ์ฐจ๊ฐ๋ฒ์ฏ ์ฐจ ์๋ฌผ 300g ์ฐจ๊ฐ๋ฒ์ฏ 300g ๋์
ํ์ฌ๋ฒ์ธ ์ฃผ์ํ์ฌ ๋์์ ์ฝ์ด'</li><li>'์ปคํด๋๋์๊ทธ๋์ถฐ ๋ง๋์นด ํ๋ 2.27kg ์์ผ๋ํ๋ผ์ ์ฐ๋์ค ํ๋๋'</li></ul> |
|
| 82 |
+
| 13.0 | <ul><li>'์์ธ์ฐ์ ๋๋ฌผ์ฑ ์ํฌ๋ฆผ 500ml ์ปคํผ์ ์ ๋นต ์ํฌ๋ฆผ 1๊ฐ[ํฌ์ฅ ๋ฏธ์ ํ์ ๋ฐฐ์ก์ง์ฐ] ๋๋๋ฆผ'</li><li>'์์ธ์ฐ์ ๋ฐ๋ฆฌ์คํ์ฆ ํํํฌ๋ฆผ 500g ์คํ๋ ์ดํ ๋ฐ๋ฆฌ์คํ ํํํฌ๋ฆผ 500g_์์ด์ค๋ฐ์ค ์ฌ๊ตฟ์ ํต'</li><li>'[์์ด์ค๋ฐ์ค๋ฌด๋ฃ] ์ ์ธ DB ํํํฌ๋ฆผ 1L ๋ฌด๊ฐ๋น ํผํฉ ์ํฌ๋ฆผ ์ฌ์ดF&B'</li></ul> |
|
| 83 |
+
| 3.0 | <ul><li>'ํ์ฐฝ์ ๊ฐ์๋ ํ์ฐฝ ์ ์๋ฐฐ์ถ 20kg ๊ณ ๋ญ์ง ํต๋ฐฐ์ถ ์ ์๋ฐฐ์ถ10kg_12-22๊ธ์์ผ ๋ฐฐ์ก์ถ๋ฐ์ผ ์ฃผ์ํ์ฌ ์ฌ๋ง๋ฃจ(Allmaru)'</li><li>'๊น๊ถํ ์ ๋ผ๋ ํฌ๊ธฐ ๋ฐฐ์ถ ๊น์น ๊น์ฅ๊น์น 2kg ์ฅ๊ณผ ๋ง์๋ [2-3] ์ ์จ์์ฑ ๋ฌต์์ง 5kg ์ฃผ์ํ์ฌ ์ฐ์ํ๋ผ'</li><li>'๋ณด๋ฆฌ๊น์น 3kg 5kg ์ ์ฃผ์ฐ๋ณด๋ฆฌ [100%๊ตญ๋ด์ฐ] ํ๋ฐฑ๊น์น ์ก๋์ '</li></ul> |
|
| 84 |
+
| 8.0 | <ul><li>'[์ ์ธ๊ณ ๊ท๊ฒฉ](์ ์ธ๊ณ ๋ณธ์ )๋ ๊ตด๋ฆฌ์ค์ฌ๋ฆฌ๋ธ์ค์ผ์ดํผ์์ค ์ฃผ์ํ์ฌ ์์ค์์ค์ง๋ท์ปด'</li><li>'ํ๋ ์๋ฐ ๋ฒ ์ด์ปจํฌ๋ฆผํ์คํ 630g 1ํฉ 630g ร 2ํฉ ์ ํฌ์ปค๋จธ์ค'</li><li>'[์ฟ ์บฃ][์ฟ ์บฃ๋ฉ์ด๋] ๋ ์ง์ฟ ์บฃ ๋ง๋ผ๋ก์ ์ฐ๋ญ 230g X 3ํฉ ๋ํด๋'</li></ul> |
|
| 85 |
+
| 11.0 | <ul><li>'์ ๋ฏธ์ ์ญ๊พธ๋ฏธ ๋ณถ์ 450g 2์ธ ์ง๋ค์ด ์์ ์บ ํ ์๋ฆฌ ์ฃผ์ํ์ฌ ๋ฏน์ค์ค๋งฅ์ค(MIXNMAX CO.,LTD.)'</li><li>'์๋ฒฝ์ฅ์ด ๊ตญ์ฐ ์ํฌ๋์นด ํ์ฒ ๋ฏผ๋ฌผ์ฅ์ด 1kg ์์ง ํ 750g์ด๋ด ์ด๋ฒ์ฅ์ด 1kg(์ํฌ๋์นด ์์ด 500g๋ด์ธ) ์๋ฒฝ์ปดํผ๋ ์ฃผ์ํ์ฌ'</li><li>'์๋ง์ ๋ฐ๋ค ์์ฐ์์ธ ์ง๋ฆฌ๋ฉธ์น 1.5kg ํ์ด์ผ์ผ์ผ์ค'</li></ul> |
|
| 86 |
+
| 7.0 | <ul><li>'์ผ์ ํฐ์ปต๊น๋ฅด๋ณด๋ถ๋ญ๋ณถ์๋ฉด 105g x 4๊ฐ ๊น๋ฅด๋ณด๋ถ๋ญ๋ณถ์๋ฉด 130g x 4 ํ๋ํธ๋ํจ์ฒ'</li><li>'[1+1 ๋๋ฉด ๊ณจ๋ผ๋ด๊ธฐ] CJ ๋์น๋ฏธ ๋ฌผ๋๋ฉด ๋น๋น๋๋ฉด ์ธ 20์ข
[5+5]ํจํฅ๋น๋น๋๋ฉด์์ค85g ์จ์ ์ด์ ์ผ์ ๋น (์ฃผ)'</li><li>'์ฒญ์ ํฌ์ฅ๊ตญ์ 3.75KG / 37์ธ๋ถ ์์น ์๋ ๋ฉธ์น ๋น๋น๊ตญ์ ์๋ฉด (์ฃผ)์ ์ด๋น์์ค'</li></ul> |
|
| 87 |
+
| 5.0 | <ul><li>'์ฒญ๋ ์์ด์คํ์ ํํผ 3kg 40๊ณผ๋ด์ธ 05_๋๋ด ํํผ 3kg 15-20๊ณผ ๊ฐ๋ฏธ์ธ์๋์กฐํฉ๋ฒ์ธ'</li><li>'๊ตญ์ฐ ์ฅ๋์ด์ฝฉ ์ฝฉ๋๋ฌผ์ฝฉ 1kg ์ฝ์ฝฉ ์๋ชฉํ ๊ฒ์์ฝฉ 8. ์ช์๋ณถ์ ์๋ฆฌํ๊ฐ๋ฃจ 500g ์ฃผ์ํ์ฌ ํ๊ทน์ธ ๋์
ํ์ฌ๋ฒ์ธ'</li><li>'๋ฒ ํธ๋จ์ ๋ฌผ ๋ฐ๊ฑด์กฐ ๋ง๊ณ ๋ฒ ํธ๋จ๊ฑด๋ง๊ณ ๋ง๋ญ์ด 100G X 10๊ฐ์
์ ์์๋ฐ๋ค'</li></ul> |
|
| 88 |
+
| 19.0 | <ul><li>'๋ฐ์ฌ์๋ช
์ธ ์๋์์ฃผ ์๋ฐํ 800ml ๋ช
์ธ์๋์์ฃผ'</li><li>'ํด์ฐฝ์ฃผ์กฐ ํด์ฐฝ๋ง๊ฑธ๋ฆฌ 9๋ ํ๋ฆฌ๋ฏธ์ ๋ง๊ฑธ๋ฆฌ ์บ ํ ์์คํค ์ฐจ๋ฐ ์์ธ ํ์ํ
๊ธ๋จํ ๊ฐ์ฑ์ฌ์ง ๋์
ํ์ฌ๋ฒ์ธ ์ ๋ดํ ์ฃผ์ํ์ฌ ์ง๋งค์ฅ์ง์ '</li><li>'๋ค๋๋ฐ์ด์ค ๊ฐ๋ฌด์น์์ฃผ 43๋ 375ml ํญ์๋ฆฌ์์ฑ ๋์
๋ฒ์ธ ์ฐ๋ฆฌ๋๊ฐ (์ฃผ) ์์ธ์ง์ '</li></ul> |
|
| 89 |
|
| 90 |
## Evaluation
|
| 91 |
|
| 92 |
### Metrics
|
| 93 |
| Label | Metric |
|
| 94 |
|:--------|:-------|
|
| 95 |
+
| **all** | 0.9180 |
|
| 96 |
|
| 97 |
## Uses
|
| 98 |
|
|
|
|
| 112 |
# Download from the ๐ค Hub
|
| 113 |
model = SetFitModel.from_pretrained("mini1013/master_item_fd")
|
| 114 |
# Run inference
|
| 115 |
+
preds = model("ํฌ์นด๋ฆฌ์ค์จํธ 245ml 1๊ฐ ์คํจํธ_๋ฝ๋ก๋ก(๋ฐํฌ๋ง) 235ML X 24๋ณ ์ฃผ์ํ์ฌ ์ก๋ฏผ")
|
| 116 |
```
|
| 117 |
|
| 118 |
<!--
|
|
|
|
| 144 |
### Training Set Metrics
|
| 145 |
| Training set | Min | Median | Max |
|
| 146 |
|:-------------|:----|:-------|:----|
|
| 147 |
+
| Word count | 3 | 9.1979 | 30 |
|
| 148 |
|
| 149 |
| Label | Training Sample Count |
|
| 150 |
|:------|:----------------------|
|
|
|
|
| 192 |
### Training Results
|
| 193 |
| Epoch | Step | Training Loss | Validation Loss |
|
| 194 |
|:-------:|:-----:|:-------------:|:---------------:|
|
| 195 |
+
| 0.0006 | 1 | 0.4086 | - |
|
| 196 |
+
| 0.0316 | 50 | 0.3967 | - |
|
| 197 |
+
| 0.0631 | 100 | 0.3705 | - |
|
| 198 |
+
| 0.0947 | 150 | 0.3541 | - |
|
| 199 |
+
| 0.1263 | 200 | 0.2971 | - |
|
| 200 |
+
| 0.1578 | 250 | 0.2651 | - |
|
| 201 |
+
| 0.1894 | 300 | 0.2404 | - |
|
| 202 |
+
| 0.2210 | 350 | 0.1946 | - |
|
| 203 |
+
| 0.2525 | 400 | 0.1848 | - |
|
| 204 |
+
| 0.2841 | 450 | 0.1706 | - |
|
| 205 |
+
| 0.3157 | 500 | 0.1394 | - |
|
| 206 |
+
| 0.3472 | 550 | 0.1364 | - |
|
| 207 |
+
| 0.3788 | 600 | 0.1178 | - |
|
| 208 |
+
| 0.4104 | 650 | 0.0926 | - |
|
| 209 |
+
| 0.4419 | 700 | 0.0949 | - |
|
| 210 |
+
| 0.4735 | 750 | 0.0732 | - |
|
| 211 |
+
| 0.5051 | 800 | 0.0806 | - |
|
| 212 |
+
| 0.5366 | 850 | 0.0648 | - |
|
| 213 |
+
| 0.5682 | 900 | 0.0707 | - |
|
| 214 |
+
| 0.5997 | 950 | 0.0523 | - |
|
| 215 |
+
| 0.6313 | 1000 | 0.0529 | - |
|
| 216 |
+
| 0.6629 | 1050 | 0.0491 | - |
|
| 217 |
+
| 0.6944 | 1100 | 0.0486 | - |
|
| 218 |
+
| 0.7260 | 1150 | 0.0369 | - |
|
| 219 |
+
| 0.7576 | 1200 | 0.0296 | - |
|
| 220 |
+
| 0.7891 | 1250 | 0.0303 | - |
|
| 221 |
+
| 0.8207 | 1300 | 0.0232 | - |
|
| 222 |
+
| 0.8523 | 1350 | 0.0281 | - |
|
| 223 |
+
| 0.8838 | 1400 | 0.0178 | - |
|
| 224 |
+
| 0.9154 | 1450 | 0.0346 | - |
|
| 225 |
+
| 0.9470 | 1500 | 0.025 | - |
|
| 226 |
+
| 0.9785 | 1550 | 0.0218 | - |
|
| 227 |
+
| 1.0101 | 1600 | 0.0335 | - |
|
| 228 |
+
| 1.0417 | 1650 | 0.0206 | - |
|
| 229 |
+
| 1.0732 | 1700 | 0.0168 | - |
|
| 230 |
+
| 1.1048 | 1750 | 0.0294 | - |
|
| 231 |
+
| 1.1364 | 1800 | 0.0219 | - |
|
| 232 |
+
| 1.1679 | 1850 | 0.0176 | - |
|
| 233 |
+
| 1.1995 | 1900 | 0.0196 | - |
|
| 234 |
+
| 1.2311 | 1950 | 0.0141 | - |
|
| 235 |
+
| 1.2626 | 2000 | 0.0031 | - |
|
| 236 |
+
| 1.2942 | 2050 | 0.0131 | - |
|
| 237 |
+
| 1.3258 | 2100 | 0.0158 | - |
|
| 238 |
+
| 1.3573 | 2150 | 0.0121 | - |
|
| 239 |
+
| 1.3889 | 2200 | 0.0088 | - |
|
| 240 |
+
| 1.4205 | 2250 | 0.0047 | - |
|
| 241 |
+
| 1.4520 | 2300 | 0.0138 | - |
|
| 242 |
+
| 1.4836 | 2350 | 0.0029 | - |
|
| 243 |
+
| 1.5152 | 2400 | 0.0063 | - |
|
| 244 |
+
| 1.5467 | 2450 | 0.0042 | - |
|
| 245 |
+
| 1.5783 | 2500 | 0.0015 | - |
|
| 246 |
+
| 1.6098 | 2550 | 0.0078 | - |
|
| 247 |
+
| 1.6414 | 2600 | 0.0014 | - |
|
| 248 |
+
| 1.6730 | 2650 | 0.0055 | - |
|
| 249 |
+
| 1.7045 | 2700 | 0.0011 | - |
|
| 250 |
+
| 1.7361 | 2750 | 0.0052 | - |
|
| 251 |
+
| 1.7677 | 2800 | 0.0018 | - |
|
| 252 |
+
| 1.7992 | 2850 | 0.003 | - |
|
| 253 |
+
| 1.8308 | 2900 | 0.004 | - |
|
| 254 |
+
| 1.8624 | 2950 | 0.0006 | - |
|
| 255 |
+
| 1.8939 | 3000 | 0.0058 | - |
|
| 256 |
+
| 1.9255 | 3050 | 0.0004 | - |
|
| 257 |
+
| 1.9571 | 3100 | 0.0028 | - |
|
| 258 |
+
| 1.9886 | 3150 | 0.0005 | - |
|
| 259 |
+
| 2.0202 | 3200 | 0.0006 | - |
|
| 260 |
+
| 2.0518 | 3250 | 0.0016 | - |
|
| 261 |
+
| 2.0833 | 3300 | 0.0036 | - |
|
| 262 |
+
| 2.1149 | 3350 | 0.0009 | - |
|
| 263 |
+
| 2.1465 | 3400 | 0.001 | - |
|
| 264 |
+
| 2.1780 | 3450 | 0.0007 | - |
|
| 265 |
+
| 2.2096 | 3500 | 0.0003 | - |
|
| 266 |
+
| 2.2412 | 3550 | 0.0003 | - |
|
| 267 |
+
| 2.2727 | 3600 | 0.0004 | - |
|
| 268 |
+
| 2.3043 | 3650 | 0.0002 | - |
|
| 269 |
+
| 2.3359 | 3700 | 0.0002 | - |
|
| 270 |
+
| 2.3674 | 3750 | 0.0002 | - |
|
| 271 |
+
| 2.3990 | 3800 | 0.003 | - |
|
| 272 |
| 2.4306 | 3850 | 0.0004 | - |
|
| 273 |
+
| 2.4621 | 3900 | 0.0013 | - |
|
| 274 |
+
| 2.4937 | 3950 | 0.0003 | - |
|
| 275 |
+
| 2.5253 | 4000 | 0.0002 | - |
|
| 276 |
+
| 2.5568 | 4050 | 0.0001 | - |
|
| 277 |
+
| 2.5884 | 4100 | 0.0002 | - |
|
| 278 |
+
| 2.6199 | 4150 | 0.0001 | - |
|
| 279 |
+
| 2.6515 | 4200 | 0.0003 | - |
|
| 280 |
+
| 2.6831 | 4250 | 0.0003 | - |
|
| 281 |
| 2.7146 | 4300 | 0.0002 | - |
|
| 282 |
+
| 2.7462 | 4350 | 0.0001 | - |
|
| 283 |
+
| 2.7778 | 4400 | 0.0018 | - |
|
| 284 |
+
| 2.8093 | 4450 | 0.0005 | - |
|
| 285 |
+
| 2.8409 | 4500 | 0.0001 | - |
|
| 286 |
+
| 2.8725 | 4550 | 0.0003 | - |
|
| 287 |
| 2.9040 | 4600 | 0.0001 | - |
|
| 288 |
+
| 2.9356 | 4650 | 0.0002 | - |
|
| 289 |
+
| 2.9672 | 4700 | 0.0002 | - |
|
| 290 |
+
| 2.9987 | 4750 | 0.0002 | - |
|
| 291 |
+
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config_setfit.json
CHANGED
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@@ -1,4 +1,4 @@
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{
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model.safetensors
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