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w2v-bert-2.0-tamil-gpu-custom_v5

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 ---
+license: mit
+base_model: facebook/w2v-bert-2.0
+tags:
+- generated_from_trainer
+metrics:
+- wer
+model-index:
+- name: w2v-bert-2.0-tamil-gpu-custom.v1
+  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-2.0-tamil-gpu-custom.v1
+This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: nan
+- Wer: 1.0
+## 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: 4.43567e-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: 500
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch | Step  | Validation Loss | Wer    |
+|:-------------:|:-----:|:-----:|:---------------:|:------:|
+| 2.3141        | 0.25  | 300   | inf             | 0.3486 |
+| 0.2064        | 0.5   | 600   | inf             | 0.3516 |
+| 0.1763        | 0.75  | 900   | inf             | 0.2858 |
+| 0.1673        | 1.0   | 1200  | inf             | 0.2929 |
+| 0.5517        | 1.25  | 1500  | inf             | 0.5617 |
+| 0.7415        | 1.49  | 1800  | inf             | 0.4608 |
+| 0.7446        | 1.74  | 2100  | inf             | 0.4608 |
+| 0.7467        | 1.99  | 2400  | inf             | 0.4608 |
+| 0.7447        | 2.24  | 2700  | inf             | 0.4608 |
+| 0.7505        | 2.49  | 3000  | inf             | 0.4608 |
+| 0.7469        | 2.74  | 3300  | inf             | 0.4608 |
+| 0.7449        | 2.99  | 3600  | inf             | 0.4608 |
+| 0.7487        | 3.24  | 3900  | inf             | 0.4608 |
+| 0.7472        | 3.49  | 4200  | inf             | 0.4608 |
+| 0.747         | 3.74  | 4500  | inf             | 0.4608 |
+| 0.7462        | 3.99  | 4800  | inf             | 0.4608 |
+| 0.7486        | 4.23  | 5100  | inf             | 0.4608 |
+| 0.7503        | 4.48  | 5400  | inf             | 0.4608 |
+| 0.7424        | 4.73  | 5700  | inf             | 0.4608 |
+| 0.746         | 4.98  | 6000  | inf             | 0.4608 |
+| 0.7518        | 5.23  | 6300  | inf             | 0.4608 |
+| 0.7442        | 5.48  | 6600  | inf             | 0.4608 |
+| 0.7466        | 5.73  | 6900  | inf             | 0.4608 |
+| 0.7468        | 5.98  | 7200  | inf             | 0.4608 |
+| 0.7542        | 6.23  | 7500  | inf             | 0.4608 |
+| 0.748         | 6.48  | 7800  | inf             | 0.4608 |
+| 0.7453        | 6.72  | 8100  | inf             | 0.4608 |
+| 0.74          | 6.97  | 8400  | inf             | 0.4608 |
+| 1.2386        | 7.22  | 8700  | nan             | 1.0    |
+| 0.0           | 7.47  | 9000  | nan             | 1.0    |
+| 0.0           | 7.72  | 9300  | nan             | 1.0    |
+| 0.0           | 7.97  | 9600  | nan             | 1.0    |
+| 0.0           | 8.22  | 9900  | nan             | 1.0    |
+| 0.0           | 8.47  | 10200 | nan             | 1.0    |
+| 0.0           | 8.72  | 10500 | nan             | 1.0    |
+| 0.0           | 8.97  | 10800 | nan             | 1.0    |
+| 0.0           | 9.22  | 11100 | nan             | 1.0    |
+| 0.0           | 9.46  | 11400 | nan             | 1.0    |
+| 0.0           | 9.71  | 11700 | nan             | 1.0    |
+| 0.0           | 9.96  | 12000 | nan             | 1.0    |
+### Framework versions
+- Transformers 4.39.3
+- Pytorch 2.1.2+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2