End of training

README.md

@@ -1,199 +1,95 @@
 ---
-library_name: transformers
-tags: []
+license: mit
+base_model: facebook/w2v-bert-2.0
+tags:
+- generated_from_trainer
+metrics:
+- wer
+model-index:
+- name: w2v-bert-tamil_new
+  results: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
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-## Model Details
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-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+<!-- 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-tamil_new
+
+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: 0.0960
+- Wer: 0.1781
+
+## 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: 4e-05
+- train_batch_size: 2
+- eval_batch_size: 1
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 2000
+- num_epochs: 5
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
+|:-------------:|:------:|:-----:|:---------------:|:------:|
+| 0.3099        | 0.1547 | 2000  | 0.2685          | 0.4726 |
+| 0.2319        | 0.3094 | 4000  | 0.2052          | 0.3246 |
+| 0.21          | 0.4640 | 6000  | 0.1702          | 0.2968 |
+| 0.1907        | 0.6187 | 8000  | 0.1591          | 0.2809 |
+| 0.1789        | 0.7734 | 10000 | 0.1468          | 0.2703 |
+| 0.1626        | 0.9281 | 12000 | 0.1482          | 0.2540 |
+| 0.1469        | 1.0828 | 14000 | 0.1390          | 0.2536 |
+| 0.144         | 1.2375 | 16000 | 0.1298          | 0.2433 |
+| 0.1418        | 1.3921 | 18000 | 0.1287          | 0.2399 |
+| 0.1349        | 1.5468 | 20000 | 0.1219          | 0.2343 |
+| 0.1266        | 1.7015 | 22000 | 0.1229          | 0.2349 |
+| 0.1257        | 1.8562 | 24000 | 0.1202          | 0.2241 |
+| 0.1209        | 2.0109 | 26000 | 0.1193          | 0.2176 |
+| 0.1113        | 2.1655 | 28000 | 0.1146          | 0.2150 |
+| 0.1052        | 2.3202 | 30000 | 0.1165          | 0.2234 |
+| 0.103         | 2.4749 | 32000 | 0.1130          | 0.2112 |
+| 0.0988        | 2.6296 | 34000 | 0.1092          | 0.2029 |
+| 0.098         | 2.7843 | 36000 | 0.1061          | 0.2022 |
+| 0.1007        | 2.9390 | 38000 | 0.1054          | 0.2036 |
+| 0.0823        | 3.0936 | 40000 | 0.1042          | 0.1997 |
+| 0.0866        | 3.2483 | 42000 | 0.1020          | 0.1945 |
+| 0.0874        | 3.4030 | 44000 | 0.0993          | 0.1972 |
+| 0.0825        | 3.5577 | 46000 | 0.1012          | 0.1941 |
+| 0.083         | 3.7124 | 48000 | 0.1017          | 0.1911 |
+| 0.0724        | 3.8671 | 50000 | 0.0992          | 0.1904 |
+| 0.0761        | 4.0217 | 52000 | 0.0983          | 0.1856 |
+| 0.0641        | 4.1764 | 54000 | 0.1011          | 0.1857 |
+| 0.0611        | 4.3311 | 56000 | 0.0980          | 0.1821 |
+| 0.0646        | 4.4858 | 58000 | 0.0982          | 0.1816 |
+| 0.062         | 4.6405 | 60000 | 0.0962          | 0.1786 |
+| 0.0616        | 4.7951 | 62000 | 0.0951          | 0.1787 |
+| 0.0607        | 4.9498 | 64000 | 0.0960          | 0.1781 |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- Pytorch 2.1.2+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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