End of training

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- glue
+metrics:
+- accuracy
+model-index:
+- name: tiny-bert-mnli-distilled
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: glue
+      type: glue
+      args: mnli
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.5818644931227712
+---
+
+<!-- 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. -->
+
+# tiny-bert-mnli-distilled
+
+This model is a fine-tuned version of [M-FAC/bert-mini-finetuned-mnli](https://huggingface.co/M-FAC/bert-mini-finetuned-mnli) on the glue dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.5018
+- Accuracy: 0.5819
+- F1 score: 0.5782
+- Precision score: 0.6036
+- Metric recall: 0.5819
+
+## 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: 0.0005
+- train_batch_size: 64
+- eval_batch_size: 32
+- seed: 33
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 4
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 score | Precision score | Metric recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:-------------:|
+| 1.4475        | 1.0   | 614  | 1.4296          | 0.4521   | 0.4070   | 0.5621          | 0.4521        |
+| 1.3354        | 2.0   | 1228 | 1.4320          | 0.4805   | 0.4579   | 0.5276          | 0.4805        |
+| 1.2244        | 3.0   | 1842 | 1.4786          | 0.5699   | 0.5602   | 0.5865          | 0.5699        |
+| 1.1416        | 4.0   | 2456 | 1.5018          | 0.5819   | 0.5782   | 0.6036          | 0.5819        |
+
+
+### Framework versions
+
+- Transformers 4.18.0
+- Pytorch 1.9.1
+- Datasets 2.1.0
+- Tokenizers 0.11.6

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