Push model using huggingface_hub.
Browse files- README.md +241 -470
- config.json +1 -1
- config_setfit.json +2 -2
- model.safetensors +1 -1
- model_head.pkl +1 -1
README.md
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---
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-
base_model: desarrolloasesoreslocales/bert-leg-al-corpus
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library_name: setfit
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metrics:
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- accuracy
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correspondiente.
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inference: true
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model-index:
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- name: SetFit
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results:
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- task:
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type: text-classification
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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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# SetFit
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification.
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The model has been trained using an efficient few-shot learning technique that involves:
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### Model Description
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- **Model Type:** SetFit
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- **Sentence Transformer
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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- **Maximum Sequence Length:** 512 tokens
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- **Number of Classes:** 20 classes
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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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### Training Hyperparameters
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- batch_size: (16, 16)
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- num_epochs: (
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- max_steps: -1
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- sampling_strategy: oversampling
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- num_iterations: 200
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- body_learning_rate: (1e-06, 1e-06)
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- head_learning_rate:
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- loss: CosineSimilarityLoss
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- distance_metric: cosine_distance
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- margin: 0.25
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- end_to_end: True
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- use_amp: True
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- warmup_proportion: 0.1
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- seed: 42
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- eval_max_steps: 100
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- load_best_model_at_end: True
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### Training Results
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| 0.35 | 1400 | 0.063 | 0.0678 |
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| 0.375 | 1500 | 0.0562 | 0.071 |
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| 0.4 | 1600 | 0.0008 | 0.0684 |
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| 0.425 | 1700 | 0.0002 | 0.073 |
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| 0.45 | 1800 | 0.0004 | 0.0719 |
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| 0.475 | 1900 | 0.0003 | 0.0747 |
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| 0.5 | 2000 | 0.0002 | 0.0712 |
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| 0.525 | 2100 | 0.0001 | 0.0742 |
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| 0.55 | 2200 | 0.0001 | 0.0716 |
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| 0.575 | 2300 | 0.0019 | 0.0734 |
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| 0.6 | 2400 | 0.0003 | 0.0748 |
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| 0.625 | 2500 | 0.0543 | 0.0757 |
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| 0.675 | 2700 | 0.0001 | 0.0709 |
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| 0.7 | 2800 | 0.0002 | 0.0722 |
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| 0.725 | 2900 | 0.0005 | 0.0727 |
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| 0.75 | 3000 | 0.0002 | 0.0755 |
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| 0.775 | 3100 | 0.0002 | 0.0687 |
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| 0.8 | 3200 | 0.0022 | 0.0734 |
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| 0.825 | 3300 | 0.0002 | 0.07 |
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| 0.85 | 3400 | 0.0001 | 0.0737 |
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| 0.875 | 3500 | 0.0001 | 0.0694 |
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| 0.9 | 3600 | 0.0002 | 0.0732 |
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| 0.925 | 3700 | 0.0002 | 0.0701 |
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| 0.95 | 3800 | 0.0002 | 0.0714 |
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| 0.975 | 3900 | 0.0005 | 0.0676 |
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| 1.8 | 7200 | 0.0042 | 0.0758 |
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| 550 |
-
| 2.325 | 9300 | 0.0001 | 0.0765 |
|
| 551 |
-
| 2.35 | 9400 | 0.0 | 0.074 |
|
| 552 |
-
| 2.375 | 9500 | 0.0024 | 0.0753 |
|
| 553 |
-
| 2.4 | 9600 | 0.0 | 0.0715 |
|
| 554 |
-
| 2.425 | 9700 | 0.0 | 0.0771 |
|
| 555 |
-
| 2.45 | 9800 | 0.0001 | 0.0746 |
|
| 556 |
-
| 2.475 | 9900 | 0.0001 | 0.0734 |
|
| 557 |
-
| 2.5 | 10000 | 0.0001 | 0.0721 |
|
| 558 |
-
| 2.525 | 10100 | 0.0001 | 0.0768 |
|
| 559 |
-
| 2.55 | 10200 | 0.0001 | 0.0774 |
|
| 560 |
-
| 2.575 | 10300 | 0.0 | 0.0736 |
|
| 561 |
-
| 2.6 | 10400 | 0.0029 | 0.0746 |
|
| 562 |
-
| 2.625 | 10500 | 0.0001 | 0.0769 |
|
| 563 |
-
| 2.65 | 10600 | 0.0001 | 0.0787 |
|
| 564 |
-
| 2.675 | 10700 | 0.0001 | 0.0718 |
|
| 565 |
-
| 2.7 | 10800 | 0.0001 | 0.0758 |
|
| 566 |
-
| 2.725 | 10900 | 0.0001 | 0.0749 |
|
| 567 |
-
| 2.75 | 11000 | 0.0 | 0.0763 |
|
| 568 |
-
| 2.775 | 11100 | 0.0001 | 0.0722 |
|
| 569 |
-
| 2.8 | 11200 | 0.0 | 0.0773 |
|
| 570 |
-
| 2.825 | 11300 | 0.0024 | 0.0746 |
|
| 571 |
-
| 2.85 | 11400 | 0.0 | 0.0756 |
|
| 572 |
-
| 2.875 | 11500 | 0.0 | 0.0718 |
|
| 573 |
-
| 2.9 | 11600 | 0.0001 | 0.0773 |
|
| 574 |
-
| 2.925 | 11700 | 0.0001 | 0.0761 |
|
| 575 |
-
| 2.95 | 11800 | 0.0001 | 0.0752 |
|
| 576 |
-
| 2.975 | 11900 | 0.0 | 0.074 |
|
| 577 |
-
| 3.0 | 12000 | 0.0401 | 0.0779 |
|
| 578 |
-
| 3.025 | 12100 | 0.0 | 0.0782 |
|
| 579 |
-
| 3.05 | 12200 | 0.0025 | 0.0738 |
|
| 580 |
-
| 3.075 | 12300 | 0.0001 | 0.0743 |
|
| 581 |
-
| 3.1 | 12400 | 0.0 | 0.076 |
|
| 582 |
-
| 3.125 | 12500 | 0.0001 | 0.078 |
|
| 583 |
-
| 3.15 | 12600 | 0.0048 | 0.0716 |
|
| 584 |
-
| 3.175 | 12700 | 0.0001 | 0.076 |
|
| 585 |
-
| 3.2 | 12800 | 0.0 | 0.0745 |
|
| 586 |
-
| 3.225 | 12900 | 0.0001 | 0.0758 |
|
| 587 |
-
| 3.25 | 13000 | 0.0 | 0.0715 |
|
| 588 |
-
| 3.275 | 13100 | 0.0024 | 0.0764 |
|
| 589 |
-
| 3.3 | 13200 | 0.0001 | 0.0747 |
|
| 590 |
-
| 3.325 | 13300 | 0.0 | 0.0767 |
|
| 591 |
-
| 3.35 | 13400 | 0.0001 | 0.0729 |
|
| 592 |
-
| 3.375 | 13500 | 0.0 | 0.0782 |
|
| 593 |
-
| 3.4 | 13600 | 0.0 | 0.076 |
|
| 594 |
-
| 3.425 | 13700 | 0.0 | 0.075 |
|
| 595 |
-
| 3.45 | 13800 | 0.0001 | 0.0734 |
|
| 596 |
-
| 3.475 | 13900 | 0.0 | 0.077 |
|
| 597 |
-
| 3.5 | 14000 | 0.0026 | 0.0768 |
|
| 598 |
-
| 3.525 | 14100 | 0.0047 | 0.0729 |
|
| 599 |
-
| 3.55 | 14200 | 0.0 | 0.074 |
|
| 600 |
-
| 3.575 | 14300 | 0.0001 | 0.0759 |
|
| 601 |
-
| 3.6 | 14400 | 0.0 | 0.078 |
|
| 602 |
-
| 3.625 | 14500 | 0.0001 | 0.0716 |
|
| 603 |
-
| 3.65 | 14600 | 0.0 | 0.0757 |
|
| 604 |
-
| 3.675 | 14700 | 0.0001 | 0.075 |
|
| 605 |
-
| 3.7 | 14800 | 0.0045 | 0.0769 |
|
| 606 |
-
| 3.725 | 14900 | 0.003 | 0.0728 |
|
| 607 |
-
| 3.75 | 15000 | 0.0 | 0.0779 |
|
| 608 |
-
| 3.775 | 15100 | 0.0 | 0.0751 |
|
| 609 |
-
| 3.8 | 15200 | 0.0001 | 0.0765 |
|
| 610 |
-
| 3.825 | 15300 | 0.0001 | 0.0722 |
|
| 611 |
-
| 3.85 | 15400 | 0.0 | 0.0778 |
|
| 612 |
-
| 3.875 | 15500 | 0.0001 | 0.0753 |
|
| 613 |
-
| 3.9 | 15600 | 0.0001 | 0.0746 |
|
| 614 |
-
| 3.925 | 15700 | 0.0 | 0.0734 |
|
| 615 |
-
| 3.95 | 15800 | 0.0026 | 0.0772 |
|
| 616 |
-
| 3.975 | 15900 | 0.0 | 0.077 |
|
| 617 |
-
| 4.0 | 16000 | 0.0 | 0.0732 |
|
| 618 |
-
| 4.025 | 16100 | 0.0 | 0.0739 |
|
| 619 |
-
| 4.05 | 16200 | 0.0 | 0.076 |
|
| 620 |
-
| 4.075 | 16300 | 0.0001 | 0.0787 |
|
| 621 |
-
| 4.1 | 16400 | 0.0047 | 0.0721 |
|
| 622 |
-
| 4.125 | 16500 | 0.0001 | 0.0765 |
|
| 623 |
-
| 4.15 | 16600 | 0.0 | 0.0754 |
|
| 624 |
-
| 4.175 | 16700 | 0.0031 | 0.0769 |
|
| 625 |
-
| 4.2 | 16800 | 0.0001 | 0.0725 |
|
| 626 |
-
| 4.225 | 16900 | 0.0 | 0.0776 |
|
| 627 |
-
| 4.25 | 17000 | 0.0 | 0.0748 |
|
| 628 |
-
| 4.275 | 17100 | 0.0001 | 0.0763 |
|
| 629 |
-
| 4.3 | 17200 | 0.0 | 0.0722 |
|
| 630 |
-
| 4.325 | 17300 | 0.0 | 0.0779 |
|
| 631 |
-
| 4.35 | 17400 | 0.0 | 0.0756 |
|
| 632 |
-
| 4.375 | 17500 | 0.0 | 0.0746 |
|
| 633 |
-
| 4.4 | 17600 | 0.0026 | 0.0733 |
|
| 634 |
-
| 4.425 | 17700 | 0.0001 | 0.0771 |
|
| 635 |
-
| 4.45 | 17800 | 0.0 | 0.0773 |
|
| 636 |
-
| 4.475 | 17900 | 0.0 | 0.0732 |
|
| 637 |
-
| 4.5 | 18000 | 0.0001 | 0.0742 |
|
| 638 |
-
| 4.525 | 18100 | 0.0 | 0.0763 |
|
| 639 |
-
| 4.55 | 18200 | 0.0001 | 0.0786 |
|
| 640 |
-
| 4.575 | 18300 | 0.0001 | 0.0719 |
|
| 641 |
-
| 4.6 | 18400 | 0.0 | 0.0763 |
|
| 642 |
-
| 4.625 | 18500 | 0.0029 | 0.0751 |
|
| 643 |
-
| 4.65 | 18600 | 0.0 | 0.0766 |
|
| 644 |
-
| 4.675 | 18700 | 0.0 | 0.0723 |
|
| 645 |
-
| 4.7 | 18800 | 0.0 | 0.0774 |
|
| 646 |
-
| 4.725 | 18900 | 0.0001 | 0.0746 |
|
| 647 |
-
| 4.75 | 19000 | 0.0 | 0.076 |
|
| 648 |
-
| 4.775 | 19100 | 0.0 | 0.0719 |
|
| 649 |
-
| 4.8 | 19200 | 0.0001 | 0.0775 |
|
| 650 |
-
| 4.825 | 19300 | 0.0001 | 0.0753 |
|
| 651 |
-
| 4.85 | 19400 | 0.0026 | 0.0747 |
|
| 652 |
-
| 4.875 | 19500 | 0.0 | 0.0734 |
|
| 653 |
-
| 4.9 | 19600 | 0.0421 | 0.0772 |
|
| 654 |
-
| 4.925 | 19700 | 0.0001 | 0.0772 |
|
| 655 |
-
| 4.95 | 19800 | 0.0 | 0.0731 |
|
| 656 |
-
| 4.975 | 19900 | 0.0001 | 0.0741 |
|
| 657 |
-
| 5.0 | 20000 | 0.0 | 0.0761 |
|
| 658 |
|
| 659 |
* The bold row denotes the saved checkpoint.
|
| 660 |
### Framework Versions
|
|
|
|
| 1 |
---
|
|
|
|
| 2 |
library_name: setfit
|
| 3 |
metrics:
|
| 4 |
- accuracy
|
|
|
|
| 27 |
correspondiente.
|
| 28 |
inference: true
|
| 29 |
model-index:
|
| 30 |
+
- name: SetFit
|
| 31 |
results:
|
| 32 |
- task:
|
| 33 |
type: text-classification
|
|
|
|
| 38 |
split: test
|
| 39 |
metrics:
|
| 40 |
- type: accuracy
|
| 41 |
+
value: 0.8
|
| 42 |
name: Accuracy
|
| 43 |
---
|
| 44 |
|
| 45 |
+
# SetFit
|
| 46 |
|
| 47 |
+
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
|
| 48 |
|
| 49 |
The model has been trained using an efficient few-shot learning technique that involves:
|
| 50 |
|
|
|
|
| 55 |
|
| 56 |
### Model Description
|
| 57 |
- **Model Type:** SetFit
|
| 58 |
+
<!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
|
| 59 |
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
|
| 60 |
- **Maximum Sequence Length:** 512 tokens
|
| 61 |
- **Number of Classes:** 20 classes
|
|
|
|
| 98 |
### Metrics
|
| 99 |
| Label | Accuracy |
|
| 100 |
|:--------|:---------|
|
| 101 |
+
| **all** | 0.8 |
|
| 102 |
|
| 103 |
## Uses
|
| 104 |
|
|
|
|
| 177 |
|
| 178 |
### Training Hyperparameters
|
| 179 |
- batch_size: (16, 16)
|
| 180 |
+
- num_epochs: (10, 10)
|
| 181 |
- max_steps: -1
|
| 182 |
- sampling_strategy: oversampling
|
|
|
|
| 183 |
- body_learning_rate: (1e-06, 1e-06)
|
| 184 |
+
- head_learning_rate: 0.003
|
| 185 |
- loss: CosineSimilarityLoss
|
| 186 |
- distance_metric: cosine_distance
|
| 187 |
- margin: 0.25
|
| 188 |
- end_to_end: True
|
| 189 |
- use_amp: True
|
| 190 |
- warmup_proportion: 0.1
|
| 191 |
+
- l2_weight: 0.001
|
| 192 |
- seed: 42
|
| 193 |
- eval_max_steps: 100
|
| 194 |
- load_best_model_at_end: True
|
| 195 |
|
| 196 |
### Training Results
|
| 197 |
+
| Epoch | Step | Training Loss | Validation Loss |
|
| 198 |
+
|:----------:|:-------:|:-------------:|:---------------:|
|
| 199 |
+
| 0.0007 | 1 | 0.0031 | - |
|
| 200 |
+
| **0.0658** | **100** | **0.0003** | **0.067** |
|
| 201 |
+
| 0.1316 | 200 | 0.0001 | 0.0717 |
|
| 202 |
+
| 0.1974 | 300 | 0.0001 | 0.0711 |
|
| 203 |
+
| 0.2632 | 400 | 0.0003 | 0.0721 |
|
| 204 |
+
| 0.3289 | 500 | 0.0021 | 0.0667 |
|
| 205 |
+
| 0.3947 | 600 | 0.0001 | 0.0611 |
|
| 206 |
+
| 0.4605 | 700 | 0.0002 | 0.0672 |
|
| 207 |
+
| 0.5263 | 800 | 0.0001 | 0.0777 |
|
| 208 |
+
| 0.5921 | 900 | 0.0001 | 0.067 |
|
| 209 |
+
| 0.6579 | 1000 | 0.0001 | 0.0687 |
|
| 210 |
+
| 0.7237 | 1100 | 0.0 | 0.0661 |
|
| 211 |
+
| 0.7895 | 1200 | 0.005 | 0.0695 |
|
| 212 |
+
| 0.8553 | 1300 | 0.0004 | 0.0661 |
|
| 213 |
+
| 0.9211 | 1400 | 0.0019 | 0.0667 |
|
| 214 |
+
| 0.9868 | 1500 | 0.0001 | 0.0672 |
|
| 215 |
+
| 1.0526 | 1600 | 0.0001 | 0.0714 |
|
| 216 |
+
| 1.1184 | 1700 | 0.0001 | 0.0687 |
|
| 217 |
+
| 1.1842 | 1800 | 0.0001 | 0.0723 |
|
| 218 |
+
| 1.25 | 1900 | 0.0 | 0.0722 |
|
| 219 |
+
| 1.3158 | 2000 | 0.0001 | 0.0728 |
|
| 220 |
+
| 1.3816 | 2100 | 0.0 | 0.0713 |
|
| 221 |
+
| 1.4474 | 2200 | 0.0 | 0.0733 |
|
| 222 |
+
| 1.5132 | 2300 | 0.0025 | 0.0719 |
|
| 223 |
+
| 1.5789 | 2400 | 0.0 | 0.0708 |
|
| 224 |
+
| 1.6447 | 2500 | 0.0 | 0.0722 |
|
| 225 |
+
| 1.7105 | 2600 | 0.0 | 0.0723 |
|
| 226 |
+
| 1.7763 | 2700 | 0.0 | 0.069 |
|
| 227 |
+
| 1.8421 | 2800 | 0.0 | 0.0703 |
|
| 228 |
+
| 1.9079 | 2900 | 0.0 | 0.0722 |
|
| 229 |
+
| 1.9737 | 3000 | 0.0001 | 0.0701 |
|
| 230 |
+
| 2.0395 | 3100 | 0.0 | 0.0691 |
|
| 231 |
+
| 2.1053 | 3200 | 0.0024 | 0.0706 |
|
| 232 |
+
| 2.1711 | 3300 | 0.0001 | 0.0716 |
|
| 233 |
+
| 2.2368 | 3400 | 0.0001 | 0.0886 |
|
| 234 |
+
| 2.3026 | 3500 | 0.0011 | 0.0734 |
|
| 235 |
+
| 2.3684 | 3600 | 0.0001 | 0.0875 |
|
| 236 |
+
| 2.4342 | 3700 | 0.0001 | 0.0809 |
|
| 237 |
+
| 2.5 | 3800 | 0.0 | 0.0818 |
|
| 238 |
+
| 2.5658 | 3900 | 0.0001 | 0.0829 |
|
| 239 |
+
| 2.6316 | 4000 | 0.0 | 0.0833 |
|
| 240 |
+
| 2.6974 | 4100 | 0.0036 | 0.0841 |
|
| 241 |
+
| 2.7632 | 4200 | 0.0 | 0.0833 |
|
| 242 |
+
| 2.8289 | 4300 | 0.0 | 0.0831 |
|
| 243 |
+
| 2.8947 | 4400 | 0.0374 | 0.083 |
|
| 244 |
+
| 2.9605 | 4500 | 0.0 | 0.083 |
|
| 245 |
+
| 3.0263 | 4600 | 0.0001 | 0.0831 |
|
| 246 |
+
| 3.0921 | 4700 | 0.0 | 0.0829 |
|
| 247 |
+
| 3.1579 | 4800 | 0.0 | 0.0828 |
|
| 248 |
+
| 3.2237 | 4900 | 0.0 | 0.0828 |
|
| 249 |
+
| 3.2895 | 5000 | 0.0068 | 0.0829 |
|
| 250 |
+
| 3.3553 | 5100 | 0.0 | 0.0826 |
|
| 251 |
+
| 3.4211 | 5200 | 0.0 | 0.0827 |
|
| 252 |
+
| 3.4868 | 5300 | 0.0 | 0.0824 |
|
| 253 |
+
| 3.5526 | 5400 | 0.0 | 0.0823 |
|
| 254 |
+
| 3.6184 | 5500 | 0.0 | 0.0822 |
|
| 255 |
+
| 3.6842 | 5600 | 0.0 | 0.0821 |
|
| 256 |
+
| 3.75 | 5700 | 0.0 | 0.0822 |
|
| 257 |
+
| 3.8158 | 5800 | 0.0 | 0.082 |
|
| 258 |
+
| 3.8816 | 5900 | 0.0032 | 0.0819 |
|
| 259 |
+
| 3.9474 | 6000 | 0.0 | 0.0822 |
|
| 260 |
+
| 4.0132 | 6100 | 0.0 | 0.0824 |
|
| 261 |
+
| 4.0789 | 6200 | 0.0 | 0.0822 |
|
| 262 |
+
| 4.1447 | 6300 | 0.0 | 0.0819 |
|
| 263 |
+
| 4.2105 | 6400 | 0.0 | 0.0822 |
|
| 264 |
+
| 4.2763 | 6500 | 0.0057 | 0.0824 |
|
| 265 |
+
| 4.3421 | 6600 | 0.0 | 0.0824 |
|
| 266 |
+
| 4.4079 | 6700 | 0.0 | 0.0824 |
|
| 267 |
+
| 4.4737 | 6800 | 0.0022 | 0.0822 |
|
| 268 |
+
| 4.5395 | 6900 | 0.0 | 0.0822 |
|
| 269 |
+
| 4.6053 | 7000 | 0.0 | 0.0823 |
|
| 270 |
+
| 4.6711 | 7100 | 0.0 | 0.0822 |
|
| 271 |
+
| 4.7368 | 7200 | 0.0034 | 0.0822 |
|
| 272 |
+
| 4.8026 | 7300 | 0.0 | 0.0822 |
|
| 273 |
+
| 4.8684 | 7400 | 0.0 | 0.0822 |
|
| 274 |
+
| 4.9342 | 7500 | 0.0 | 0.0822 |
|
| 275 |
+
| 5.0 | 7600 | 0.0 | 0.0822 |
|
| 276 |
+
| 0.0007 | 1 | 0.0018 | - |
|
| 277 |
+
| **0.0658** | **100** | **0.0002** | **0.0612** |
|
| 278 |
+
| 0.1316 | 200 | 0.0002 | 0.0613 |
|
| 279 |
+
| 0.1974 | 300 | 0.0002 | 0.0615 |
|
| 280 |
+
| 0.2632 | 400 | 0.0 | 0.0619 |
|
| 281 |
+
| 0.3289 | 500 | 0.0021 | 0.0626 |
|
| 282 |
+
| 0.3947 | 600 | 0.0001 | 0.0628 |
|
| 283 |
+
| 0.4605 | 700 | 0.0001 | 0.0633 |
|
| 284 |
+
| 0.5263 | 800 | 0.0001 | 0.064 |
|
| 285 |
+
| 0.5921 | 900 | 0.0001 | 0.0635 |
|
| 286 |
+
| 0.6579 | 1000 | 0.0001 | 0.0645 |
|
| 287 |
+
| 0.7237 | 1100 | 0.0001 | 0.0659 |
|
| 288 |
+
| 0.7895 | 1200 | 0.0055 | 0.0662 |
|
| 289 |
+
| 0.8553 | 1300 | 0.0001 | 0.0667 |
|
| 290 |
+
| 0.9211 | 1400 | 0.0032 | 0.0673 |
|
| 291 |
+
| 0.9868 | 1500 | 0.0001 | 0.067 |
|
| 292 |
+
| 1.0526 | 1600 | 0.0001 | 0.0668 |
|
| 293 |
+
| 1.1184 | 1700 | 0.0001 | 0.0667 |
|
| 294 |
+
| 1.1842 | 1800 | 0.0001 | 0.0664 |
|
| 295 |
+
| 1.25 | 1900 | 0.0001 | 0.0667 |
|
| 296 |
+
| 1.3158 | 2000 | 0.0 | 0.0674 |
|
| 297 |
+
| 1.3816 | 2100 | 0.0001 | 0.0667 |
|
| 298 |
+
| 1.4474 | 2200 | 0.0 | 0.0669 |
|
| 299 |
+
| 1.5132 | 2300 | 0.0028 | 0.0669 |
|
| 300 |
+
| 1.5789 | 2400 | 0.0001 | 0.0671 |
|
| 301 |
+
| 1.6447 | 2500 | 0.0001 | 0.0676 |
|
| 302 |
+
| 1.7105 | 2600 | 0.0001 | 0.0689 |
|
| 303 |
+
| 1.7763 | 2700 | 0.0001 | 0.069 |
|
| 304 |
+
| 1.8421 | 2800 | 0.0001 | 0.0691 |
|
| 305 |
+
| 1.9079 | 2900 | 0.0001 | 0.0696 |
|
| 306 |
+
| 1.9737 | 3000 | 0.0001 | 0.0688 |
|
| 307 |
+
| 2.0395 | 3100 | 0.0 | 0.0678 |
|
| 308 |
+
| 2.1053 | 3200 | 0.0027 | 0.0677 |
|
| 309 |
+
| 2.1711 | 3300 | 0.0001 | 0.0675 |
|
| 310 |
+
| 2.2368 | 3400 | 0.0 | 0.0676 |
|
| 311 |
+
| 2.3026 | 3500 | 0.0001 | 0.068 |
|
| 312 |
+
| 2.3684 | 3600 | 0.0001 | 0.0672 |
|
| 313 |
+
| 2.4342 | 3700 | 0.0 | 0.0669 |
|
| 314 |
+
| 2.5 | 3800 | 0.0 | 0.0667 |
|
| 315 |
+
| 2.5658 | 3900 | 0.0 | 0.0673 |
|
| 316 |
+
| 2.6316 | 4000 | 0.0 | 0.0672 |
|
| 317 |
+
| 2.6974 | 4100 | 0.0032 | 0.0689 |
|
| 318 |
+
| 2.7632 | 4200 | 0.0 | 0.0691 |
|
| 319 |
+
| 2.8289 | 4300 | 0.0001 | 0.0693 |
|
| 320 |
+
| 2.8947 | 4400 | 0.0388 | 0.0692 |
|
| 321 |
+
| 2.9605 | 4500 | 0.0001 | 0.0691 |
|
| 322 |
+
| 3.0263 | 4600 | 0.0 | 0.0683 |
|
| 323 |
+
| 3.0921 | 4700 | 0.0 | 0.0685 |
|
| 324 |
+
| 3.1579 | 4800 | 0.0001 | 0.0681 |
|
| 325 |
+
| 3.2237 | 4900 | 0.0 | 0.0677 |
|
| 326 |
+
| 3.2895 | 5000 | 0.0081 | 0.0684 |
|
| 327 |
+
| 3.3553 | 5100 | 0.0 | 0.0685 |
|
| 328 |
+
| 3.4211 | 5200 | 0.0 | 0.0681 |
|
| 329 |
+
| 3.4868 | 5300 | 0.0001 | 0.0683 |
|
| 330 |
+
| 3.5526 | 5400 | 0.0001 | 0.0681 |
|
| 331 |
+
| 3.6184 | 5500 | 0.0 | 0.0675 |
|
| 332 |
+
| 3.6842 | 5600 | 0.0 | 0.0687 |
|
| 333 |
+
| 3.75 | 5700 | 0.0001 | 0.0692 |
|
| 334 |
+
| 3.8158 | 5800 | 0.0 | 0.0695 |
|
| 335 |
+
| 3.8816 | 5900 | 0.0038 | 0.069 |
|
| 336 |
+
| 3.9474 | 6000 | 0.0001 | 0.069 |
|
| 337 |
+
| 4.0132 | 6100 | 0.0 | 0.0684 |
|
| 338 |
+
| 4.0789 | 6200 | 0.0001 | 0.0688 |
|
| 339 |
+
| 4.1447 | 6300 | 0.0 | 0.0682 |
|
| 340 |
+
| 4.2105 | 6400 | 0.0 | 0.0677 |
|
| 341 |
+
| 4.2763 | 6500 | 0.0049 | 0.0678 |
|
| 342 |
+
| 4.3421 | 6600 | 0.0001 | 0.068 |
|
| 343 |
+
| 4.4079 | 6700 | 0.0 | 0.0679 |
|
| 344 |
+
| 4.4737 | 6800 | 0.0029 | 0.0679 |
|
| 345 |
+
| 4.5395 | 6900 | 0.0 | 0.0684 |
|
| 346 |
+
| 4.6053 | 7000 | 0.0 | 0.0678 |
|
| 347 |
+
| 4.6711 | 7100 | 0.0 | 0.0688 |
|
| 348 |
+
| 4.7368 | 7200 | 0.004 | 0.0695 |
|
| 349 |
+
| 4.8026 | 7300 | 0.0 | 0.0696 |
|
| 350 |
+
| 4.8684 | 7400 | 0.0 | 0.0695 |
|
| 351 |
+
| 4.9342 | 7500 | 0.0 | 0.0695 |
|
| 352 |
+
| 5.0 | 7600 | 0.0 | 0.0691 |
|
| 353 |
+
| 5.0658 | 7700 | 0.0033 | 0.0691 |
|
| 354 |
+
| 5.1316 | 7800 | 0.0 | 0.0691 |
|
| 355 |
+
| 5.1974 | 7900 | 0.0 | 0.0688 |
|
| 356 |
+
| 5.2632 | 8000 | 0.0 | 0.0689 |
|
| 357 |
+
| 5.3289 | 8100 | 0.0001 | 0.0689 |
|
| 358 |
+
| 5.3947 | 8200 | 0.0 | 0.0688 |
|
| 359 |
+
| 5.4605 | 8300 | 0.0 | 0.0685 |
|
| 360 |
+
| 5.5263 | 8400 | 0.0 | 0.0688 |
|
| 361 |
+
| 5.5921 | 8500 | 0.0 | 0.0683 |
|
| 362 |
+
| 5.6579 | 8600 | 0.003 | 0.0688 |
|
| 363 |
+
| 5.7237 | 8700 | 0.0 | 0.0698 |
|
| 364 |
+
| 5.7895 | 8800 | 0.0037 | 0.0701 |
|
| 365 |
+
| 5.8553 | 8900 | 0.0 | 0.0701 |
|
| 366 |
+
| 5.9211 | 9000 | 0.0001 | 0.0695 |
|
| 367 |
+
| 5.9868 | 9100 | 0.0001 | 0.0697 |
|
| 368 |
+
| 6.0526 | 9200 | 0.0 | 0.0694 |
|
| 369 |
+
| 6.1184 | 9300 | 0.0 | 0.0689 |
|
| 370 |
+
| 6.1842 | 9400 | 0.0 | 0.0686 |
|
| 371 |
+
| 6.25 | 9500 | 0.0025 | 0.0686 |
|
| 372 |
+
| 6.3158 | 9600 | 0.0 | 0.069 |
|
| 373 |
+
| 6.3816 | 9700 | 0.0 | 0.069 |
|
| 374 |
+
| 6.4474 | 9800 | 0.0 | 0.0687 |
|
| 375 |
+
| 6.5132 | 9900 | 0.0001 | 0.0683 |
|
| 376 |
+
| 6.5789 | 10000 | 0.0 | 0.0684 |
|
| 377 |
+
| 6.6447 | 10100 | 0.0 | 0.0684 |
|
| 378 |
+
| 6.7105 | 10200 | 0.0001 | 0.069 |
|
| 379 |
+
| 6.7763 | 10300 | 0.0 | 0.0694 |
|
| 380 |
+
| 6.8421 | 10400 | 0.0028 | 0.0696 |
|
| 381 |
+
| 6.9079 | 10500 | 0.0 | 0.0697 |
|
| 382 |
+
| 6.9737 | 10600 | 0.0 | 0.0697 |
|
| 383 |
+
| 7.0395 | 10700 | 0.0 | 0.0694 |
|
| 384 |
+
| 7.1053 | 10800 | 0.0 | 0.0692 |
|
| 385 |
+
| 7.1711 | 10900 | 0.0 | 0.069 |
|
| 386 |
+
| 7.2368 | 11000 | 0.0 | 0.0691 |
|
| 387 |
+
| 7.3026 | 11100 | 0.0 | 0.0691 |
|
| 388 |
+
| 7.3684 | 11200 | 0.0 | 0.0691 |
|
| 389 |
+
| 7.4342 | 11300 | 0.0025 | 0.069 |
|
| 390 |
+
| 7.5 | 11400 | 0.0 | 0.0687 |
|
| 391 |
+
| 7.5658 | 11500 | 0.0 | 0.0688 |
|
| 392 |
+
| 7.6316 | 11600 | 0.0 | 0.0688 |
|
| 393 |
+
| 7.6974 | 11700 | 0.0001 | 0.0691 |
|
| 394 |
+
| 7.7632 | 11800 | 0.0 | 0.0692 |
|
| 395 |
+
| 7.8289 | 11900 | 0.0001 | 0.0692 |
|
| 396 |
+
| 7.8947 | 12000 | 0.0405 | 0.0693 |
|
| 397 |
+
| 7.9605 | 12100 | 0.0 | 0.0695 |
|
| 398 |
+
| 8.0263 | 12200 | 0.0029 | 0.0694 |
|
| 399 |
+
| 8.0921 | 12300 | 0.0001 | 0.0693 |
|
| 400 |
+
| 8.1579 | 12400 | 0.0 | 0.0692 |
|
| 401 |
+
| 8.2237 | 12500 | 0.0001 | 0.0691 |
|
| 402 |
+
| 8.2895 | 12600 | 0.0045 | 0.0693 |
|
| 403 |
+
| 8.3553 | 12700 | 0.0 | 0.0693 |
|
| 404 |
+
| 8.4211 | 12800 | 0.0 | 0.0692 |
|
| 405 |
+
| 8.4868 | 12900 | 0.0 | 0.0691 |
|
| 406 |
+
| 8.5526 | 13000 | 0.0 | 0.0691 |
|
| 407 |
+
| 8.6184 | 13100 | 0.0026 | 0.069 |
|
| 408 |
+
| 8.6842 | 13200 | 0.0 | 0.0692 |
|
| 409 |
+
| 8.75 | 13300 | 0.0 | 0.0694 |
|
| 410 |
+
| 8.8158 | 13400 | 0.0 | 0.0694 |
|
| 411 |
+
| 8.8816 | 13500 | 0.0 | 0.0693 |
|
| 412 |
+
| 8.9474 | 13600 | 0.0 | 0.0694 |
|
| 413 |
+
| 9.0132 | 13700 | 0.0 | 0.0693 |
|
| 414 |
+
| 9.0789 | 13800 | 0.0 | 0.0693 |
|
| 415 |
+
| 9.1447 | 13900 | 0.0 | 0.0692 |
|
| 416 |
+
| 9.2105 | 14000 | 0.003 | 0.0692 |
|
| 417 |
+
| 9.2763 | 14100 | 0.0044 | 0.0692 |
|
| 418 |
+
| 9.3421 | 14200 | 0.0 | 0.0692 |
|
| 419 |
+
| 9.4079 | 14300 | 0.0 | 0.0692 |
|
| 420 |
+
| 9.4737 | 14400 | 0.0 | 0.0691 |
|
| 421 |
+
| 9.5395 | 14500 | 0.0 | 0.0691 |
|
| 422 |
+
| 9.6053 | 14600 | 0.0 | 0.0691 |
|
| 423 |
+
| 9.6711 | 14700 | 0.0 | 0.0691 |
|
| 424 |
+
| 9.7368 | 14800 | 0.0043 | 0.0692 |
|
| 425 |
+
| 9.8026 | 14900 | 0.0028 | 0.0692 |
|
| 426 |
+
| 9.8684 | 15000 | 0.0 | 0.0692 |
|
| 427 |
+
| 9.9342 | 15100 | 0.0 | 0.0692 |
|
| 428 |
+
| 10.0 | 15200 | 0.0 | 0.0692 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 429 |
|
| 430 |
* The bold row denotes the saved checkpoint.
|
| 431 |
### Framework Versions
|
config.json
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
{
|
| 2 |
-
"_name_or_path": "results/
|
| 3 |
"architectures": [
|
| 4 |
"RobertaModel"
|
| 5 |
],
|
|
|
|
| 1 |
{
|
| 2 |
+
"_name_or_path": "results/step_100",
|
| 3 |
"architectures": [
|
| 4 |
"RobertaModel"
|
| 5 |
],
|
config_setfit.json
CHANGED
|
@@ -1,4 +1,4 @@
|
|
| 1 |
{
|
| 2 |
-
"
|
| 3 |
-
"
|
| 4 |
}
|
|
|
|
| 1 |
{
|
| 2 |
+
"labels": null,
|
| 3 |
+
"normalize_embeddings": false
|
| 4 |
}
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 737406824
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:53c262c6065a8f80f17c0a55b60b32037fc341ee214e1db3802f5aad4d696f6e
|
| 3 |
size 737406824
|
model_head.pkl
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 124039
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1217582c47bee2a4b06d815b1f5c0cf2d5c49cec8094f1a5801de7d638548e7b
|
| 3 |
size 124039
|