adelinamart
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minilm_finetuned_emotions

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 ---
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-{}
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 ---
+license: mit
+base_model: microsoft/MiniLM-L12-H384-uncased
+tags:
+- generated_from_trainer
+datasets:
+- emotion
+metrics:
+- f1
+model-index:
+- name: minilm_finetuned_emotions
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: emotion
+      type: emotion
+      config: split
+      split: validation
+      args: split
+    metrics:
+    - name: F1
+      type: f1
+      value: 0.9103389601463553
 ---
+<!-- 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. -->
+# minilm_finetuned_emotions
+This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the emotion dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4118
+- F1: 0.9103
+## 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: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch | Step | Validation Loss | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:------:|
+| 1.3758        | 1.0   | 250  | 1.0211          | 0.5910 |
+| 0.885         | 2.0   | 500  | 0.7133          | 0.7977 |
+| 0.6248        | 3.0   | 750  | 0.5321          | 0.8840 |
+| 0.4874        | 4.0   | 1000 | 0.4416          | 0.9013 |
+| 0.4193        | 5.0   | 1250 | 0.4118          | 0.9103 |
+### Framework versions
+- Transformers 4.38.1
+- Pytorch 2.1.0+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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