End of training

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- xglue
+metrics:
+- accuracy
+model-index:
+- name: xlm-v-base-finetuned-xglue-xnli
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: xglue
+      type: xglue
+      config: xnli
+      split: validation.en+validation.ar+validation.bg+validation.de+validation.el+validation.es+validation.fr+validation.hi+validation.ru+validation.sw+validation.th+validation.tr+validation.ur+validation.vi+validation.zh
+      args: xnli
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.7402677376171352
+---
+
+<!-- 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. -->
+
+# xlm-v-base-finetuned-xglue-xnli
+
+This model is a fine-tuned version of [stefan-it/xlm-v-base](https://huggingface.co/stefan-it/xlm-v-base) on the xglue dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6511
+- Accuracy: 0.7403
+
+## 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: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 2
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 1.0994        | 0.08  | 1000  | 1.0966          | 0.3697   |
+| 1.0221        | 0.16  | 2000  | 1.0765          | 0.4560   |
+| 0.8437        | 0.24  | 3000  | 0.8472          | 0.6179   |
+| 0.6997        | 0.33  | 4000  | 0.7650          | 0.6804   |
+| 0.6304        | 0.41  | 5000  | 0.7227          | 0.7007   |
+| 0.5972        | 0.49  | 6000  | 0.7430          | 0.6977   |
+| 0.5886        | 0.57  | 7000  | 0.7365          | 0.7066   |
+| 0.5585        | 0.65  | 8000  | 0.6819          | 0.7223   |
+| 0.5464        | 0.73  | 9000  | 0.7222          | 0.7046   |
+| 0.5289        | 0.81  | 10000 | 0.7290          | 0.7054   |
+| 0.5298        | 0.9   | 11000 | 0.6824          | 0.7221   |
+| 0.5241        | 0.98  | 12000 | 0.6650          | 0.7268   |
+| 0.4806        | 1.06  | 13000 | 0.6861          | 0.7308   |
+| 0.4715        | 1.14  | 14000 | 0.6619          | 0.7304   |
+| 0.4645        | 1.22  | 15000 | 0.6656          | 0.7284   |
+| 0.4443        | 1.3   | 16000 | 0.7026          | 0.7270   |
+| 0.4582        | 1.39  | 17000 | 0.7055          | 0.7225   |
+| 0.4456        | 1.47  | 18000 | 0.6592          | 0.7361   |
+| 0.44          | 1.55  | 19000 | 0.6816          | 0.7329   |
+| 0.4419        | 1.63  | 20000 | 0.6772          | 0.7357   |
+| 0.4403        | 1.71  | 21000 | 0.6745          | 0.7319   |
+| 0.4348        | 1.79  | 22000 | 0.6678          | 0.7338   |
+| 0.4355        | 1.87  | 23000 | 0.6614          | 0.7365   |
+| 0.4295        | 1.96  | 24000 | 0.6511          | 0.7403   |
+
+
+### Framework versions
+
+- Transformers 4.26.0
+- Pytorch 1.13.1+cu116
+- Datasets 2.9.0
+- Tokenizers 0.13.2

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