End of training

README.md

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+---
+base_model: gokuls/model_v1_complete_training_wt_init_48_tiny_freeze_new_ffn_2
+tags:
+- generated_from_trainer
+datasets:
+- emotion
+metrics:
+- accuracy
+model-index:
+- name: hbertv1-emotion-logit_KD-tiny_ffn_2
+  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: 0.9005
+---
+
+<!-- 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-tiny_ffn_2
+
+This model is a fine-tuned version of [gokuls/model_v1_complete_training_wt_init_48_tiny_freeze_new_ffn_2](https://huggingface.co/gokuls/model_v1_complete_training_wt_init_48_tiny_freeze_new_ffn_2) on the emotion dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4740
+- Accuracy: 0.9005
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 3.1189        | 1.0   | 250  | 2.6103          | 0.514    |
+| 2.0804        | 2.0   | 500  | 1.4939          | 0.7695   |
+| 1.2677        | 3.0   | 750  | 0.8999          | 0.8445   |
+| 0.8885        | 4.0   | 1000 | 0.6887          | 0.874    |
+| 0.7023        | 5.0   | 1250 | 0.5821          | 0.889    |
+| 0.5796        | 6.0   | 1500 | 0.5364          | 0.8875   |
+| 0.5106        | 7.0   | 1750 | 0.5043          | 0.89     |
+| 0.4603        | 8.0   | 2000 | 0.5055          | 0.889    |
+| 0.405         | 9.0   | 2250 | 0.4903          | 0.89     |
+| 0.3782        | 10.0  | 2500 | 0.4793          | 0.8965   |
+| 0.3488        | 11.0  | 2750 | 0.4832          | 0.8945   |
+| 0.3301        | 12.0  | 3000 | 0.4740          | 0.9005   |
+| 0.3163        | 13.0  | 3250 | 0.4768          | 0.89     |
+| 0.2983        | 14.0  | 3500 | 0.4925          | 0.887    |
+| 0.2835        | 15.0  | 3750 | 0.4764          | 0.898    |
+| 0.2702        | 16.0  | 4000 | 0.4856          | 0.8905   |
+| 0.2522        | 17.0  | 4250 | 0.4829          | 0.897    |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 1.14.0a0+410ce96
+- Datasets 2.15.0
+- Tokenizers 0.15.0

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