Update README.md

README.md

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----
-license: mit
-base_model: MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7
-tags:
-- generated_from_trainer
-metrics:
-- accuracy
-model-index:
-- name: mDeBERTa-v3-base-xnli-multilingual-nli-2mil7
-  results: []
----
-
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
-# mDeBERTa-v3-base-xnli-multilingual-nli-2mil7
-
-This model is a fine-tuned version of [MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.4486
-- F1 Macro: 0.8264
-- F1 Micro: 0.8274
-- Accuracy Balanced: 0.8270
-- Accuracy: 0.8274
-- Precision Macro: 0.8260
-- Recall Macro: 0.8270
-- Precision Micro: 0.8274
-- Recall Micro: 0.8274
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 16
-- eval_batch_size: 128
-- seed: 20241201
-- gradient_accumulation_steps: 2
-- total_train_batch_size: 32
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_ratio: 0.06
-- num_epochs: 3
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | Accuracy Balanced | Accuracy | Precision Macro | Recall Macro | Precision Micro | Recall Micro |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
-| 0.3242        | 1.69  | 200  | 0.4044          | 0.8308   | 0.8312   | 0.8322            | 0.8312   | 0.8306          | 0.8322       | 0.8312          | 0.8312       |
-
-
-### Framework versions
-
-- Transformers 4.33.3
-- Pytorch 2.5.1+cu121
-- Datasets 2.14.7
-- Tokenizers 0.13.3
+---
+base_model: MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: mDeBERTa-v3-base-xnli-multilingual-nli-2mil7
+  results: []
+datasets:
+- asadfgglie/nli-zh-tw-all
+language:
+- zh
+pipeline_tag: zero-shot-classification
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# mDeBERTa-v3-base-xnli-multilingual-zeroshot-v4.0-only-nli-downsample
+
+This model use same dataset with [asadfgglie/mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.0](https://huggingface.co/asadfgglie/mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.0), but training set was downsampled as 80% size of non-nli dataset [asadfgglie/BanBan_2024-10-17-facial_expressions-nli](https://huggingface.co/datasets/asadfgglie/BanBan_2024-10-17-facial_expressions-nli).
+
+This model is a fine-tuned version of [MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4486
+- F1 Macro: 0.8264
+- F1 Micro: 0.8274
+- Accuracy Balanced: 0.8270
+- Accuracy: 0.8274
+- Precision Macro: 0.8260
+- Recall Macro: 0.8270
+- Precision Micro: 0.8274
+- Recall Micro: 0.8274
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 128
+- seed: 20241201
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.06
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | Accuracy Balanced | Accuracy | Precision Macro | Recall Macro | Precision Micro | Recall Micro |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
+| 0.3242        | 1.69  | 200  | 0.4044          | 0.8308   | 0.8312   | 0.8322            | 0.8312   | 0.8306          | 0.8322       | 0.8312          | 0.8312       |
+
+### Eval results
+|Datasets|asadfgglie/nli-zh-tw-all/test|asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test|eval_dataset|test_dataset|
+| :---: | :---: | :---: | :---: | :---: |
+|eval_loss|0.445|1.142|0.429|0.449|
+|eval_f1_macro|0.827|0.505|0.83|0.826|
+|eval_f1_micro|0.828|0.55|0.831|0.827|
+|eval_accuracy_balanced|0.828|0.548|0.831|0.827|
+|eval_accuracy|0.828|0.55|0.831|0.827|
+|eval_precision_macro|0.827|0.575|0.83|0.826|
+|eval_recall_macro|0.828|0.548|0.831|0.827|
+|eval_precision_micro|0.828|0.55|0.831|0.827|
+|eval_recall_micro|0.828|0.55|0.831|0.827|
+|eval_runtime|275.581|4.734|54.573|209.065|
+|eval_samples_per_second|30.844|199.853|31.151|32.526|
+|eval_steps_per_second|0.243|1.69|0.257|0.258|
+|epoch|2.99|2.99|2.99|2.99|
+|Size of dataset|8500|946|1700|6800|
+
+### Framework versions
+
+- Transformers 4.33.3
+- Pytorch 2.5.1+cu121
+- Datasets 2.14.7
+- Tokenizers 0.13.3