meiiny00
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w2v-bert-2.0-15red

@@ -1,199 +1,98 @@
 ---
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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-2.0-15red
+  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-2.0-15red
+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: 0.2430
+- Wer: 0.1037
+## 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: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- 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
+- training_steps: 17000
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch   | Step  | Validation Loss | Wer    |
+|:-------------:|:-------:|:-----:|:---------------:|:------:|
+| 0.6691        | 0.4261  | 500   | 0.6233          | 0.5383 |
+| 0.45          | 0.8522  | 1000  | 0.4130          | 0.3675 |
+| 0.3758        | 1.2782  | 1500  | 0.3631          | 0.3105 |
+| 0.2521        | 1.7043  | 2000  | 0.3245          | 0.2777 |
+| 0.2897        | 2.1304  | 2500  | 0.3024          | 0.2466 |
+| 0.23          | 2.5565  | 3000  | 0.2813          | 0.2389 |
+| 0.2937        | 2.9825  | 3500  | 0.2713          | 0.2236 |
+| 0.1833        | 3.4086  | 4000  | 0.2600          | 0.2055 |
+| 0.1375        | 3.8347  | 4500  | 0.2424          | 0.1910 |
+| 0.2097        | 4.2608  | 5000  | 0.2376          | 0.1856 |
+| 0.1676        | 4.6868  | 5500  | 0.2304          | 0.1839 |
+| 0.1268        | 5.1129  | 6000  | 0.2328          | 0.1687 |
+| 0.1229        | 5.5390  | 6500  | 0.2274          | 0.1646 |
+| 0.1116        | 5.9651  | 7000  | 0.2103          | 0.1562 |
+| 0.2322        | 6.3911  | 7500  | 0.2080          | 0.1540 |
+| 0.1592        | 6.8172  | 8000  | 0.2151          | 0.1496 |
+| 0.0796        | 7.2433  | 8500  | 0.2065          | 0.1401 |
+| 0.0774        | 7.6694  | 9000  | 0.2036          | 0.1373 |
+| 0.0979        | 8.0954  | 9500  | 0.2109          | 0.1361 |
+| 0.0916        | 8.5215  | 10000 | 0.2082          | 0.1320 |
+| 0.1057        | 8.9476  | 10500 | 0.2080          | 0.1294 |
+| 0.0642        | 9.3737  | 11000 | 0.2032          | 0.1245 |
+| 0.0585        | 9.7997  | 11500 | 0.1974          | 0.1232 |
+| 0.0531        | 10.2258 | 12000 | 0.2108          | 0.1203 |
+| 0.049         | 10.6519 | 12500 | 0.2027          | 0.1155 |
+| 0.0431        | 11.0780 | 13000 | 0.2065          | 0.1152 |
+| 0.0454        | 11.5040 | 13500 | 0.2167          | 0.1122 |
+| 0.0236        | 11.9301 | 14000 | 0.2195          | 0.1113 |
+| 0.0313        | 12.3562 | 14500 | 0.2314          | 0.1080 |
+| 0.0452        | 12.7823 | 15000 | 0.2231          | 0.1063 |
+| 0.0159        | 13.2084 | 15500 | 0.2259          | 0.1057 |
+| 0.0166        | 13.6344 | 16000 | 0.2355          | 0.1043 |
+| 0.0175        | 14.0605 | 16500 | 0.2340          | 0.1040 |
+| 0.0169        | 14.4866 | 17000 | 0.2430          | 0.1037 |
+### Framework versions
+- Transformers 4.44.2
+- Pytorch 2.4.1+cu124
+- Datasets 2.20.0
+- Tokenizers 0.19.1