End of training

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-zeroshot-v5.0-nli-downsample-and-non-nli
+  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-zeroshot-v5.0-nli-downsample-and-non-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: 0.4531
+- F1 Macro: 0.8330
+- F1 Micro: 0.8337
+- Accuracy Balanced: 0.8331
+- Accuracy: 0.8337
+- Precision Macro: 0.8330
+- Recall Macro: 0.8331
+- Precision Micro: 0.8337
+- Recall Micro: 0.8337
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
+| 0.3748        | 0.85  | 200  | 0.4218          | 0.7971   | 0.7999   | 0.7970            | 0.7999   | 0.7973          | 0.7970       | 0.7999          | 0.7999       |
+| 0.2693        | 1.69  | 400  | 0.4523          | 0.8061   | 0.8078   | 0.8077            | 0.8078   | 0.8053          | 0.8077       | 0.8078          | 0.8078       |
+| 0.1905        | 2.54  | 600  | 0.4720          | 0.8226   | 0.8242   | 0.8241            | 0.8242   | 0.8217          | 0.8241       | 0.8242          | 0.8242       |
+
+
+### Framework versions
+
+- Transformers 4.33.3
+- Pytorch 2.5.1+cu121
+- Datasets 2.14.7
+- Tokenizers 0.13.3

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