Model save

README.md

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+---
+license: apache-2.0
+base_model: jonatasgrosman/wav2vec2-large-xlsr-53-english
+tags:
+- generated_from_trainer
+datasets:
+- ravdess
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: wav2vec2-large-xlsr-53-english-finetuned-ravdess
+  results:
+  - task:
+      name: Audio Classification
+      type: audio-classification
+    dataset:
+      name: ravdess
+      type: ravdess
+      config: all
+      split: train
+      args: all
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.7152777777777778
+    - name: Precision
+      type: precision
+      value: 0.7360657858765911
+    - name: Recall
+      type: recall
+      value: 0.7152777777777778
+    - name: F1
+      type: f1
+      value: 0.6891900402765098
+---
+
+<!-- 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. -->
+
+# wav2vec2-large-xlsr-53-english-finetuned-ravdess
+
+This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the ravdess dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.0013
+- Accuracy: 0.7153
+- Precision: 0.7361
+- Recall: 0.7153
+- F1: 0.6892
+
+## 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: 2
+- eval_batch_size: 2
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 4
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 1.9323        | 1.0   | 288  | 1.9023          | 0.2917   | 0.4800    | 0.2917 | 0.2042 |
+| 1.4114        | 2.0   | 576  | 1.2845          | 0.6111   | 0.7423    | 0.6111 | 0.5283 |
+| 0.938         | 3.0   | 864  | 1.0013          | 0.7153   | 0.7361    | 0.7153 | 0.6892 |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.1