End of training

README.md

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 ---
-library_name: transformers
-tags: []
+license: mit
+base_model: facebook/w2v-bert-2.0
+tags:
+- generated_from_trainer
+metrics:
+- wer
+model-index:
+- name: w2v-bert-2.0-nonstudio_and_studioRecords
+  results: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
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-
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-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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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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-## Uses
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-## How to Get Started with the Model
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-## Training Details
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-### Training Data
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-### Training Procedure 
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-#### Training Hyperparameters
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-#### Summary
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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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-2.0-nonstudio_and_studioRecords
+
+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: 0.1641
+- Wer: 0.1184
+
+## 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: 500
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Wer    |
+|:-------------:|:-----:|:-----:|:---------------:|:------:|
+| 1.1077        | 0.46  | 600   | 0.4029          | 0.4897 |
+| 0.1727        | 0.92  | 1200  | 0.2339          | 0.3573 |
+| 0.1224        | 1.38  | 1800  | 0.2159          | 0.3225 |
+| 0.1103        | 1.84  | 2400  | 0.1838          | 0.2764 |
+| 0.0907        | 2.3   | 3000  | 0.1844          | 0.2603 |
+| 0.0796        | 2.76  | 3600  | 0.1829          | 0.2498 |
+| 0.0685        | 3.22  | 4200  | 0.1719          | 0.2336 |
+| 0.0588        | 3.68  | 4800  | 0.1607          | 0.2030 |
+| 0.054         | 4.14  | 5400  | 0.1611          | 0.1941 |
+| 0.0424        | 4.6   | 6000  | 0.1536          | 0.1821 |
+| 0.0402        | 5.06  | 6600  | 0.1562          | 0.1769 |
+| 0.0312        | 5.52  | 7200  | 0.1494          | 0.1655 |
+| 0.0303        | 5.98  | 7800  | 0.1471          | 0.1510 |
+| 0.0218        | 6.44  | 8400  | 0.1707          | 0.1488 |
+| 0.0218        | 6.9   | 9000  | 0.1458          | 0.1296 |
+| 0.0151        | 7.36  | 9600  | 0.1424          | 0.1326 |
+| 0.014         | 7.82  | 10200 | 0.1406          | 0.1266 |
+| 0.0107        | 8.28  | 10800 | 0.1476          | 0.1291 |
+| 0.0078        | 8.74  | 11400 | 0.1563          | 0.1254 |
+| 0.007         | 9.2   | 12000 | 0.1528          | 0.1197 |
+| 0.0041        | 9.66  | 12600 | 0.1641          | 0.1184 |
+
+
+### Framework versions
+
+- Transformers 4.39.3
+- Pytorch 2.1.1+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.1

model.safetensors

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runs/May10_09-51-58_kudsit-dgxserver/events.out.tfevents.1715315777.kudsit-dgxserver.2880687.0

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