cdactvm
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w2v-bert-malayalam

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+license: mit
+base_model: facebook/w2v-bert-2.0
+tags:
+- generated_from_trainer
+metrics:
+- wer
+model-index:
+- name: w2v-bert-malayalam
+  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. -->
+# w2v-bert-malayalam
+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.
+It achieves the following results on the evaluation set:
+- Loss: 0.1149
+- Wer: 0.0646
+## 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: 4
+- total_train_batch_size: 8
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
+|:-------------:|:------:|:-----:|:---------------:|:------:|
+| 0.3705        | 0.2758 | 2000  | 0.3227          | 0.3629 |
+| 0.291         | 0.5516 | 4000  | 0.2434          | 0.2891 |
+| 0.2695        | 0.8274 | 6000  | 0.2445          | 0.2775 |
+| 0.2118        | 1.1032 | 8000  | 0.1979          | 0.2567 |
+| 0.1923        | 1.3790 | 10000 | 0.1852          | 0.2213 |
+| 0.1788        | 1.6548 | 12000 | 0.1691          | 0.2033 |
+| 0.167         | 1.9306 | 14000 | 0.1870          | 0.1955 |
+| 0.1612        | 2.2063 | 16000 | 0.1571          | 0.1731 |
+| 0.1516        | 2.4821 | 18000 | 0.1406          | 0.1685 |
+| 0.1597        | 2.7579 | 20000 | 0.1358          | 0.1496 |
+| 0.1299        | 3.0336 | 22000 | 0.1332          | 0.1397 |
+| 0.1096        | 3.3095 | 24000 | 0.1397          | 0.1384 |
+| 0.1291        | 3.5853 | 26000 | 0.1298          | 0.1354 |
+| 0.0975        | 3.8611 | 28000 | 0.1220          | 0.1134 |
+| 0.0919        | 4.1368 | 30000 | 0.1261          | 0.1081 |
+| 0.0806        | 4.4126 | 32000 | 0.1189          | 0.1120 |
+| 0.0778        | 4.6884 | 34000 | 0.1159          | 0.1027 |
+| 0.0922        | 4.9642 | 36000 | 0.1218          | 0.1027 |
+| 0.0907        | 5.2400 | 38000 | 0.1099          | 0.0977 |
+| 0.0708        | 5.5158 | 40000 | 0.1043          | 0.0920 |
+| 0.0715        | 5.7916 | 42000 | 0.1048          | 0.0928 |
+| 0.0646        | 6.0673 | 44000 | 0.1047          | 0.0893 |
+| 0.0567        | 6.3431 | 46000 | 0.1294          | 0.0891 |
+| 0.0729        | 6.6189 | 48000 | 0.1236          | 0.0873 |
+| 0.0607        | 6.8947 | 50000 | 0.1182          | 0.0830 |
+| 0.0555        | 7.1705 | 52000 | 0.1222          | 0.0809 |
+| 0.0516        | 7.4463 | 54000 | 0.1145          | 0.0798 |
+| 0.0429        | 7.7221 | 56000 | 0.0915          | 0.0763 |
+| 0.0399        | 7.9979 | 58000 | 0.0987          | 0.0731 |
+| 0.0373        | 8.2736 | 60000 | 0.1167          | 0.0714 |
+| 0.0371        | 8.5494 | 62000 | 0.1130          | 0.0710 |
+| 0.0412        | 8.8252 | 64000 | 0.1194          | 0.0707 |
+| 0.0282        | 9.1009 | 66000 | 0.1217          | 0.0683 |
+| 0.0284        | 9.3768 | 68000 | 0.1177          | 0.0671 |
+| 0.0275        | 9.6526 | 70000 | 0.1117          | 0.0661 |
+| 0.0216        | 9.9284 | 72000 | 0.1149          | 0.0646 |
+### Framework versions
+- Transformers 4.48.0
+- Pytorch 2.5.1+cu124
+- Datasets 3.2.0
+- Tokenizers 0.21.0

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