End of training

README.md

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+---
+license: mit
+base_model: facebook/w2v-bert-2.0
+tags:
+- generated_from_trainer
+metrics:
+- wer
+model-index:
+- name: malayalam_combined_Read
+  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. -->
+
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/krishnan-aravind/huggingface/runs/if5rp2lo)
+# malayalam_combined_Read
+
+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: 0.1389
+- Wer: 0.1305
+
+## 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: 16
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 50
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
+|:-------------:|:------:|:-----:|:---------------:|:------:|
+| 0.3294        | 0.4378 | 500   | 0.2872          | 0.3888 |
+| 0.2522        | 0.8757 | 1000  | 0.2404          | 0.3214 |
+| 0.2017        | 1.3135 | 1500  | 0.2100          | 0.3247 |
+| 0.19          | 1.7513 | 2000  | 0.2004          | 0.2744 |
+| 0.1443        | 2.1891 | 2500  | 0.1791          | 0.2370 |
+| 0.1484        | 2.6270 | 3000  | 0.1684          | 0.2309 |
+| 0.1232        | 3.0648 | 3500  | 0.1672          | 0.2114 |
+| 0.1135        | 3.5026 | 4000  | 0.1584          | 0.2031 |
+| 0.1152        | 3.9405 | 4500  | 0.1457          | 0.1841 |
+| 0.0904        | 4.3783 | 5000  | 0.1528          | 0.1870 |
+| 0.0887        | 4.8161 | 5500  | 0.1455          | 0.1726 |
+| 0.0768        | 5.2539 | 6000  | 0.1471          | 0.1636 |
+| 0.069         | 5.6918 | 6500  | 0.1463          | 0.1578 |
+| 0.0587        | 6.1296 | 7000  | 0.1401          | 0.1582 |
+| 0.0537        | 6.5674 | 7500  | 0.1377          | 0.1485 |
+| 0.0534        | 7.0053 | 8000  | 0.1278          | 0.1459 |
+| 0.0397        | 7.4431 | 8500  | 0.1284          | 0.1418 |
+| 0.0433        | 7.8809 | 9000  | 0.1274          | 0.1414 |
+| 0.0323        | 8.3187 | 9500  | 0.1299          | 0.1357 |
+| 0.0364        | 8.7566 | 10000 | 0.1340          | 0.1348 |
+| 0.0219        | 9.1944 | 10500 | 0.1350          | 0.1335 |
+| 0.0245        | 9.6322 | 11000 | 0.1389          | 0.1305 |
+
+
+### Framework versions
+
+- Transformers 4.43.0.dev0
+- Pytorch 1.14.0a0+44dac51
+- Datasets 2.16.1
+- Tokenizers 0.19.1

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