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--- |
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license: mit |
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base_model: facebook/w2v-bert-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: CiceroASR |
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results: [] |
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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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# CiceroASR |
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This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) |
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for the transcription of Classical Latin! |
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Example from the Aeneid: |
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<video controls src="https://cdn-uploads.huggingface.co/production/uploads/5fc7944e8a82cc0bcf7cc51d/hYNFr2od1EKDlRRdzJmzR.webm"></video> |
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Transcription: |
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**arma virumque cano** (Of arms and men I sing) |
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Example from Genesis: |
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<video controls src="https://cdn-uploads.huggingface.co/production/uploads/5fc7944e8a82cc0bcf7cc51d/9Q6DfG2h8FkABnl55DLBH.webm"></video> |
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Transcription (little error there): |
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**creavit deus chaelum et terram** (In the beggining God created the heaven and the earth) |
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It achieves the following results on the evaluation set of my dataset [Latin Youtube](https://huggingface.co/datasets/thiagolira/LatinYoutube): |
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- Loss: 0.5395 |
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- Wer: 0.2220 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 16 |
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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: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 300 |
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- num_epochs: 15 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 3.6548 | 0.94 | 50 | 2.8634 | 0.9990 | |
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| 2.2055 | 1.89 | 100 | 1.0921 | 0.9727 | |
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| 1.667 | 2.83 | 150 | 0.7201 | 0.4615 | |
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| 1.3148 | 3.77 | 200 | 0.6431 | 0.3866 | |
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| 0.9899 | 4.72 | 250 | 0.5561 | 0.3116 | |
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| 0.9629 | 5.66 | 300 | 0.6027 | 0.3817 | |
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| 0.7557 | 6.6 | 350 | 0.7145 | 0.3145 | |
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| 0.9143 | 7.55 | 400 | 0.4926 | 0.2610 | |
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| 0.5837 | 8.49 | 450 | 0.5396 | 0.2619 | |
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| 0.7037 | 9.43 | 500 | 0.5076 | 0.2746 | |
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| 0.5986 | 10.38 | 550 | 0.5224 | 0.2415 | |
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| 0.5288 | 11.32 | 600 | 0.5332 | 0.2259 | |
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| 0.5034 | 12.26 | 650 | 0.5436 | 0.2249 | |
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| 0.4897 | 13.21 | 700 | 0.5171 | 0.2162 | |
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| 0.4738 | 14.15 | 750 | 0.5395 | 0.2220 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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