End of training

README.md

@@ -1,199 +1,126 @@
 ---
 library_name: transformers
-tags: []
+license: cc-by-nc-4.0
+base_model: facebook/mms-1b-all
+tags:
+- generated_from_trainer
+metrics:
+- wer
+model-index:
+- name: ssc-kcn-mms-model-mix-adapt-max3-devtrain
+  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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-<!-- 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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-## How to Get Started with the Model
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-#### Training Hyperparameters
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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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+<!-- 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. -->
+
+# ssc-kcn-mms-model-mix-adapt-max3-devtrain
+
+This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 3.0275
+- Cer: 0.9948
+- 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: 0.0005
+- train_batch_size: 8
+- eval_batch_size: 6
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 100
+- num_epochs: 20
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch   | Step  | Validation Loss | Cer    | Wer    |
+|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
+| 1.8683        | 0.3255  | 200   | 1.3551          | 0.3006 | 0.7960 |
+| 1.7632        | 0.6509  | 400   | 1.1500          | 0.2843 | 0.7674 |
+| 1.5399        | 0.9764  | 600   | 1.1117          | 0.2728 | 0.7389 |
+| 1.5162        | 1.3011  | 800   | 1.0280          | 0.2635 | 0.6834 |
+| 1.3652        | 1.6265  | 1000  | 1.0271          | 0.2644 | 0.6979 |
+| 1.4639        | 1.9520  | 1200  | 0.9877          | 0.2561 | 0.6707 |
+| 1.3649        | 2.2766  | 1400  | 0.9695          | 0.2905 | 0.7334 |
+| 1.3099        | 2.6021  | 1600  | 0.9362          | 0.2580 | 0.6528 |
+| 1.4657        | 2.9276  | 1800  | 0.9327          | 0.2444 | 0.6286 |
+| 1.3293        | 3.2522  | 2000  | 0.9036          | 0.2435 | 0.6340 |
+| 1.3104        | 3.5777  | 2200  | 0.8912          | 0.2271 | 0.5869 |
+| 1.3312        | 3.9032  | 2400  | 0.8735          | 0.2316 | 0.5929 |
+| 1.2938        | 4.2278  | 2600  | 0.8586          | 0.2477 | 0.6311 |
+| 1.1996        | 4.5533  | 2800  | 0.8563          | 0.2239 | 0.5740 |
+| 1.2274        | 4.8788  | 3000  | 0.8228          | 0.2201 | 0.5660 |
+| 1.239         | 5.2034  | 3200  | 0.7998          | 0.2166 | 0.5610 |
+| 1.2571        | 5.5289  | 3400  | 0.8093          | 0.2182 | 0.5644 |
+| 1.1657        | 5.8544  | 3600  | 0.8015          | 0.2154 | 0.5709 |
+| 1.2191        | 6.1790  | 3800  | 0.8172          | 0.2596 | 0.6538 |
+| 1.1454        | 6.5045  | 4000  | 0.7585          | 0.2040 | 0.5385 |
+| 1.1947        | 6.8299  | 4200  | 0.7499          | 0.2098 | 0.5623 |
+| 1.1713        | 7.1546  | 4400  | 0.7742          | 0.2032 | 0.5332 |
+| 1.174         | 7.4801  | 4600  | 0.7535          | 0.2051 | 0.5401 |
+| 1.0679        | 7.8055  | 4800  | 0.7980          | 0.1998 | 0.5126 |
+| 1.0496        | 8.1302  | 5000  | 0.7340          | 0.2110 | 0.5332 |
+| 1.0556        | 8.4557  | 5200  | 0.7798          | 0.1967 | 0.5160 |
+| 1.1176        | 8.7811  | 5400  | 0.7507          | 0.1955 | 0.5105 |
+| 1.0328        | 9.1058  | 5600  | 0.7879          | 0.2314 | 0.5839 |
+| 1.2111        | 9.4312  | 5800  | 0.7895          | 0.2144 | 0.5552 |
+| 1.1246        | 9.7567  | 6000  | 0.7783          | 0.2365 | 0.5866 |
+| 1.1467        | 10.0814 | 6200  | 0.9091          | 0.3878 | 0.7700 |
+| 1.1934        | 10.4068 | 6400  | 0.8550          | 0.2718 | 0.6812 |
+| 1.1667        | 10.7323 | 6600  | 0.8572          | 0.2272 | 0.5897 |
+| 1.0666        | 11.0570 | 6800  | 0.8905          | 0.2220 | 0.5870 |
+| 1.1896        | 11.3824 | 7000  | 0.9218          | 0.2132 | 0.5424 |
+| 1.2196        | 11.7079 | 7200  | 0.8750          | 0.2562 | 0.6450 |
+| 1.2411        | 12.0325 | 7400  | 0.9874          | 0.3248 | 0.7407 |
+| 1.2445        | 12.3580 | 7600  | 1.0789          | 0.4789 | 0.7972 |
+| 1.267         | 12.6835 | 7800  | 1.0402          | 0.4264 | 0.8475 |
+| 1.2832        | 13.0081 | 8000  | 1.1640          | 0.4774 | 0.8471 |
+| 1.3661        | 13.3336 | 8200  | 1.1290          | 0.4364 | 0.8530 |
+| 1.4301        | 13.6591 | 8400  | 1.0429          | 0.2741 | 0.6720 |
+| 1.4179        | 13.9845 | 8600  | 1.3128          | 0.4368 | 0.8406 |
+| 1.5952        | 14.3092 | 8800  | 2.6448          | 0.9623 | 0.9972 |
+| 2.0972        | 14.6347 | 9000  | 2.4127          | 0.9534 | 0.9972 |
+| 2.6133        | 14.9601 | 9200  | 3.1122          | 0.9361 | 0.9999 |
+| 3.1285        | 15.2848 | 9400  | 3.0395          | 0.9937 | 0.9999 |
+| 2.7735        | 15.6103 | 9600  | 2.6072          | 0.9847 | 0.9998 |
+| 2.6965        | 15.9357 | 9800  | 2.9206          | 0.7560 | 0.9992 |
+| 2.7295        | 16.2604 | 10000 | 2.5736          | 0.9724 | 1.0    |
+| 2.9313        | 16.5858 | 10200 | 2.6595          | 0.9875 | 1.0    |
+| 2.9284        | 16.9113 | 10400 | 2.8261          | 0.9899 | 1.0    |
+| 3.0932        | 17.2360 | 10600 | 3.0139          | 0.9904 | 1.0    |
+| 3.009         | 17.5614 | 10800 | 2.9606          | 0.9945 | 1.0    |
+| 3.0038        | 17.8869 | 11000 | 3.0613          | 0.9943 | 1.0    |
+| 3.0147        | 18.2116 | 11200 | 2.9256          | 0.9948 | 1.0    |
+| 3.0213        | 18.5370 | 11400 | 2.9503          | 0.9946 | 1.0    |
+| 3.1034        | 18.8625 | 11600 | 2.9490          | 0.9947 | 1.0    |
+| 3.0172        | 19.1871 | 11800 | 2.9878          | 0.9948 | 1.0    |
+| 3.066         | 19.5126 | 12000 | 3.0423          | 0.9947 | 1.0    |
+| 3.0562        | 19.8381 | 12200 | 3.0275          | 0.9948 | 1.0    |
+
+
+### Framework versions
+
+- Transformers 4.52.1
+- Pytorch 2.9.1+cu128
+- Datasets 3.6.0
+- Tokenizers 0.21.4

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vocab.json

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