End of training

README.md

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+---
+base_model: gokuls/bert_12_layer_model_v1_complete_training_new_48
+tags:
+- generated_from_trainer
+datasets:
+- emotion
+metrics:
+- accuracy
+model-index:
+- name: hbertv1-emotion-logit_KD_new
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: emotion
+      type: emotion
+      config: split
+      split: validation
+      args: split
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.884
+---
+
+<!-- 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. -->
+
+# hbertv1-emotion-logit_KD_new
+
+This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_48](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_48) on the emotion dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6758
+- Accuracy: 0.884
+
+## 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: 5e-05
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 33
+- distributed_type: multi-GPU
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 50
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 2.3717        | 1.0   | 250  | 1.7665          | 0.6635   |
+| 1.3315        | 2.0   | 500  | 1.1797          | 0.7825   |
+| 0.9074        | 3.0   | 750  | 0.7837          | 0.864    |
+| 0.636         | 4.0   | 1000 | 0.7591          | 0.861    |
+| 0.5276        | 5.0   | 1250 | 0.7692          | 0.85     |
+| 0.4694        | 6.0   | 1500 | 0.7129          | 0.871    |
+| 0.4317        | 7.0   | 1750 | 0.6381          | 0.8765   |
+| 0.3791        | 8.0   | 2000 | 0.6646          | 0.879    |
+| 0.3551        | 9.0   | 2250 | 0.6414          | 0.882    |
+| 0.3236        | 10.0  | 2500 | 0.6436          | 0.8795   |
+| 0.301         | 11.0  | 2750 | 0.6758          | 0.884    |
+| 0.2854        | 12.0  | 3000 | 0.6706          | 0.8695   |
+| 0.2698        | 13.0  | 3250 | 0.6216          | 0.8825   |
+| 0.2554        | 14.0  | 3500 | 0.6592          | 0.879    |
+| 0.2311        | 15.0  | 3750 | 0.7509          | 0.877    |
+| 0.2275        | 16.0  | 4000 | 0.7177          | 0.8775   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 1.14.0a0+410ce96
+- Datasets 2.15.0
+- Tokenizers 0.15.0

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