End of training

README.md

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+---
+license: apache-2.0
+base_model: ntu-spml/distilhubert
+tags:
+- generated_from_trainer
+datasets:
+- audiofolder
+metrics:
+- accuracy
+model-index:
+- name: DH_ONM_WORDS
+  results:
+  - task:
+      name: Audio Classification
+      type: audio-classification
+    dataset:
+      name: audiofolder
+      type: audiofolder
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.999762962962963
+---
+
+<!-- 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. -->
+
+# DH_ONM_WORDS
+
+This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0021
+- Accuracy: 0.9998
+
+## 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: 3e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- distributed_type: tpu
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.0251        | 1.0   | 263  | 0.0150          | 0.9993   |
+| 0.0085        | 2.0   | 527  | 0.0045          | 0.9997   |
+| 0.0026        | 3.0   | 791  | 0.0029          | 0.9998   |
+| 0.0018        | 4.0   | 1055 | 0.0023          | 0.9998   |
+| 0.0019        | 4.99  | 1315 | 0.0021          | 0.9998   |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.0.0+cu118
+- Datasets 2.16.1
+- Tokenizers 0.15.1

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