End of training

README.md

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+---
+library_name: transformers
+base_model: KasuleTrevor/wav2vec2-xls-r-300m-nyn_filtered-yogera-v3
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: Luganda_speech_to_intent_nyn_xlsr
+  results: []
+---
+
+<!-- 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. -->
+
+# Luganda_speech_to_intent_nyn_xlsr
+
+This model is a fine-tuned version of [KasuleTrevor/wav2vec2-xls-r-300m-nyn_filtered-yogera-v3](https://huggingface.co/KasuleTrevor/wav2vec2-xls-r-300m-nyn_filtered-yogera-v3) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1401
+- Accuracy: 0.9757
+- Precision: 0.9761
+- Recall: 0.9757
+- F1: 0.9755
+
+## 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: 0.0001
+- train_batch_size: 32
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 64
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 30
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 2.9405        | 1.0   | 131  | 2.3617          | 0.5163   | 0.4655    | 0.5163 | 0.4450 |
+| 1.9336        | 2.0   | 262  | 0.1540          | 0.9859   | 0.9865    | 0.9859 | 0.9858 |
+| 0.3581        | 3.0   | 393  | 0.0748          | 0.9902   | 0.9904    | 0.9902 | 0.9902 |
+| 0.1253        | 4.0   | 524  | 0.0730          | 0.9881   | 0.9884    | 0.9881 | 0.9881 |
+| 0.1166        | 5.0   | 655  | 0.0609          | 0.9913   | 0.9915    | 0.9913 | 0.9913 |
+| 0.1071        | 6.0   | 786  | 0.0667          | 0.9913   | 0.9915    | 0.9913 | 0.9913 |
+| 0.0836        | 7.0   | 917  | 0.0601          | 0.9902   | 0.9904    | 0.9902 | 0.9902 |
+| 0.0736        | 8.0   | 1048 | 0.0611          | 0.9913   | 0.9915    | 0.9913 | 0.9913 |
+| 0.0612        | 9.0   | 1179 | 0.0633          | 0.9902   | 0.9904    | 0.9902 | 0.9902 |
+| 0.0553        | 10.0  | 1310 | 0.0657          | 0.9902   | 0.9904    | 0.9902 | 0.9902 |
+| 0.0478        | 11.0  | 1441 | 0.0650          | 0.9913   | 0.9915    | 0.9913 | 0.9913 |
+| 0.0392        | 12.0  | 1572 | 0.0681          | 0.9902   | 0.9904    | 0.9902 | 0.9902 |
+
+
+### Framework versions
+
+- Transformers 4.51.3
+- Pytorch 2.1.0+cu118
+- Datasets 3.6.0
+- Tokenizers 0.21.2

model.safetensors

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 size 1267137848