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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: w2v-bert-2-malayalam-combo-v1 |
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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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# w2v-bert-2-malayalam-combo-v1 |
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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) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: inf |
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- Wer: 0.1007 |
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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: 5e-05 |
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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: 500 |
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- num_epochs: 10 |
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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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| 1.9859 | 0.2432 | 300 | inf | 0.4513 | |
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| 0.2903 | 0.4864 | 600 | inf | 0.4107 | |
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| 0.2294 | 0.7296 | 900 | inf | 0.3331 | |
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| 0.2075 | 0.9728 | 1200 | inf | 0.2968 | |
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| 0.1737 | 1.2161 | 1500 | inf | 0.2862 | |
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| 0.1561 | 1.4593 | 1800 | inf | 0.2603 | |
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| 0.1435 | 1.7025 | 2100 | inf | 0.2496 | |
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| 0.1388 | 1.9457 | 2400 | inf | 0.2329 | |
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| 0.1213 | 2.1889 | 2700 | inf | 0.2271 | |
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| 0.1168 | 2.4321 | 3000 | inf | 0.2202 | |
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| 0.1086 | 2.6753 | 3300 | inf | 0.2273 | |
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| 0.1131 | 2.9185 | 3600 | inf | 0.2132 | |
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| 0.0951 | 3.1617 | 3900 | inf | 0.2068 | |
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| 0.0851 | 3.4049 | 4200 | inf | 0.2075 | |
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| 0.0905 | 3.6482 | 4500 | inf | 0.1969 | |
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| 0.0811 | 3.8914 | 4800 | inf | 0.1941 | |
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| 0.0754 | 4.1346 | 5100 | inf | 0.1717 | |
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| 0.0653 | 4.3778 | 5400 | inf | 0.1704 | |
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| 0.0663 | 4.6210 | 5700 | inf | 0.1737 | |
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| 0.0635 | 4.8642 | 6000 | inf | 0.1551 | |
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| 0.0607 | 5.1074 | 6300 | inf | 0.1479 | |
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| 0.05 | 5.3506 | 6600 | inf | 0.1478 | |
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| 0.0519 | 5.5938 | 6900 | inf | 0.1441 | |
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| 0.048 | 5.8370 | 7200 | inf | 0.1410 | |
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| 0.0428 | 6.0803 | 7500 | inf | 0.1362 | |
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| 0.0344 | 6.3235 | 7800 | inf | 0.1325 | |
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| 0.0344 | 6.5667 | 8100 | inf | 0.1242 | |
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| 0.0361 | 6.8099 | 8400 | inf | 0.1247 | |
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| 0.031 | 7.0531 | 8700 | inf | 0.1227 | |
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| 0.0256 | 7.2963 | 9000 | inf | 0.1175 | |
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| 0.023 | 7.5395 | 9300 | inf | 0.1172 | |
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| 0.0223 | 7.7827 | 9600 | inf | 0.1161 | |
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| 0.0203 | 8.0259 | 9900 | inf | 0.1099 | |
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| 0.014 | 8.2692 | 10200 | inf | 0.1094 | |
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| 0.0158 | 8.5124 | 10500 | inf | 0.1081 | |
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| 0.0147 | 8.7556 | 10800 | inf | 0.1078 | |
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| 0.0132 | 8.9988 | 11100 | inf | 0.1049 | |
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| 0.008 | 9.2420 | 11400 | inf | 0.1048 | |
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| 0.0081 | 9.4852 | 11700 | inf | 0.1010 | |
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| 0.0081 | 9.7284 | 12000 | inf | 0.1010 | |
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| 0.0094 | 9.9716 | 12300 | inf | 0.1007 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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