End of training

README.md

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+---
+base_model: gokuls/model_v1_complete_training_wt_init_48_small_freeze_new
+tags:
+- generated_from_trainer
+datasets:
+- massive
+metrics:
+- accuracy
+model-index:
+- name: hbertv1-massive-logit_KD-small
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: massive
+      type: massive
+      config: en-US
+      split: validation
+      args: en-US
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8735858337432366
+---
+
+<!-- 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-massive-logit_KD-small
+
+This model is a fine-tuned version of [gokuls/model_v1_complete_training_wt_init_48_small_freeze_new](https://huggingface.co/gokuls/model_v1_complete_training_wt_init_48_small_freeze_new) on the massive dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4139
+- Accuracy: 0.8736
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 2.2301        | 1.0   | 180  | 0.8611          | 0.7565   |
+| 0.8039        | 2.0   | 360  | 0.5989          | 0.8151   |
+| 0.5542        | 3.0   | 540  | 0.5036          | 0.8396   |
+| 0.4134        | 4.0   | 720  | 0.4535          | 0.8569   |
+| 0.3187        | 5.0   | 900  | 0.4432          | 0.8569   |
+| 0.251         | 6.0   | 1080 | 0.4280          | 0.8637   |
+| 0.2201        | 7.0   | 1260 | 0.4311          | 0.8598   |
+| 0.1879        | 8.0   | 1440 | 0.4443          | 0.8608   |
+| 0.168         | 9.0   | 1620 | 0.4136          | 0.8677   |
+| 0.153         | 10.0  | 1800 | 0.4286          | 0.8598   |
+| 0.137         | 11.0  | 1980 | 0.4148          | 0.8701   |
+| 0.1276        | 12.0  | 2160 | 0.4158          | 0.8711   |
+| 0.1196        | 13.0  | 2340 | 0.3975          | 0.8721   |
+| 0.1137        | 14.0  | 2520 | 0.4221          | 0.8662   |
+| 0.1066        | 15.0  | 2700 | 0.4085          | 0.8677   |
+| 0.1024        | 16.0  | 2880 | 0.4048          | 0.8687   |
+| 0.0995        | 17.0  | 3060 | 0.4139          | 0.8736   |
+| 0.0949        | 18.0  | 3240 | 0.3953          | 0.8706   |
+| 0.0908        | 19.0  | 3420 | 0.3984          | 0.8716   |
+| 0.0882        | 20.0  | 3600 | 0.4006          | 0.8701   |
+| 0.0864        | 21.0  | 3780 | 0.3943          | 0.8731   |
+| 0.0837        | 22.0  | 3960 | 0.3912          | 0.8692   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 1.14.0a0+410ce96
+- Datasets 2.15.0
+- Tokenizers 0.15.0

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