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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: KasuleTrevor/wav2vec2-large-xls-r-300m-lg-cv-130hr-v1
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - precision
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+ - recall
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+ - f1
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+ model-index:
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+ - name: multilingual_speech_to_intent_wav2vec_xlsr
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # multilingual_speech_to_intent_wav2vec_xlsr
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+
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+ This model is a fine-tuned version of [KasuleTrevor/wav2vec2-large-xls-r-300m-lg-cv-130hr-v1](https://huggingface.co/KasuleTrevor/wav2vec2-large-xls-r-300m-lg-cv-130hr-v1) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1493
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+ - Accuracy: 0.9804
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+ - Precision: 0.9813
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+ - Recall: 0.9804
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+ - F1: 0.9805
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0003
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+ - train_batch_size: 32
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 64
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 100
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 0.65 | 1.0 | 219 | 0.1235 | 0.9795 | 0.9799 | 0.9795 | 0.9795 |
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+ | 0.2315 | 2.0 | 438 | 0.1033 | 0.9851 | 0.9854 | 0.9851 | 0.9852 |
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+ | 0.2355 | 3.0 | 657 | 0.1331 | 0.9724 | 0.9740 | 0.9724 | 0.9724 |
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+ | 0.1943 | 4.0 | 876 | 0.2951 | 0.9250 | 0.9304 | 0.9250 | 0.9245 |
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+ | 0.1854 | 5.0 | 1095 | 0.5676 | 0.8931 | 0.9056 | 0.8931 | 0.8925 |
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+ | 0.1499 | 6.0 | 1314 | 0.3552 | 0.9243 | 0.9344 | 0.9243 | 0.9240 |
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+ | 0.1461 | 7.0 | 1533 | 0.2503 | 0.9441 | 0.9492 | 0.9441 | 0.9442 |
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+ | 0.1407 | 8.0 | 1752 | 0.2951 | 0.9214 | 0.9269 | 0.9214 | 0.9212 |
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+ | 0.116 | 9.0 | 1971 | 0.3022 | 0.9391 | 0.9425 | 0.9391 | 0.9390 |
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+ | 0.1142 | 10.0 | 2190 | 0.2169 | 0.9483 | 0.9526 | 0.9483 | 0.9483 |
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+ | 0.1064 | 11.0 | 2409 | 0.5370 | 0.9115 | 0.9171 | 0.9115 | 0.9111 |
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+ | 0.1067 | 12.0 | 2628 | 1.1525 | 0.8259 | 0.8471 | 0.8259 | 0.8266 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.43.3
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+ - Pytorch 2.1.0+cu118
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+ - Datasets 2.20.0
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+ - Tokenizers 0.19.1
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