End of training

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- sms_spam
+metrics:
+- accuracy
+model-index:
+- name: MiniLMv2-L12-H384-distilled-finetuned-spam-detection
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: sms_spam
+      type: sms_spam
+      args: plain_text
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.978494623655914
+---
+
+<!-- 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. -->
+
+# MiniLMv2-L12-H384-distilled-finetuned-spam-detection
+
+This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large) on the sms_spam dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4473
+- Accuracy: 0.9785
+
+## 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: 0.0001
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 33
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 4.1186        | 1.0   | 18   | 3.4012          | 0.8351   |
+| 2.9893        | 2.0   | 36   | 2.9206          | 0.8351   |
+| 2.6718        | 3.0   | 54   | 2.8932          | 0.8351   |
+| 2.5495        | 4.0   | 72   | 2.8916          | 0.8351   |
+| 1.7213        | 5.0   | 90   | 0.6804          | 0.9821   |
+| 0.7464        | 6.0   | 108  | 0.6017          | 0.9713   |
+| 1.2052        | 7.0   | 126  | 0.3425          | 0.9857   |
+| 0.438         | 8.0   | 144  | 0.2136          | 0.9857   |
+| 0.2282        | 9.0   | 162  | 0.4539          | 0.9785   |
+| 0.438         | 10.0  | 180  | 0.4473          | 0.9785   |
+
+
+### Framework versions
+
+- Transformers 4.17.0
+- Pytorch 1.10.2+cu113
+- Datasets 1.18.4
+- Tokenizers 0.12.1

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