End of training

README.md

@@ -1,199 +1,136 @@
 ---
 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-mmc-mms-model-mix-adapt-max3-devtrain
+  results: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-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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-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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-### Model Architecture and Objective
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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-mmc-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: 0.8857
+- Cer: 0.2393
+- Wer: 0.5815
+
+## 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    |
+|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
+| 2.037         | 0.2803  | 200   | 1.4866          | 0.3482 | 0.7704 |
+| 1.6013        | 0.5606  | 400   | 1.2655          | 0.3096 | 0.7048 |
+| 1.4971        | 0.8409  | 600   | 1.1954          | 0.3003 | 0.6941 |
+| 1.4418        | 1.1205  | 800   | 1.1713          | 0.2920 | 0.6872 |
+| 1.4512        | 1.4008  | 1000  | 1.1212          | 0.2852 | 0.6568 |
+| 1.4548        | 1.6811  | 1200  | 1.1313          | 0.2909 | 0.6727 |
+| 1.4091        | 1.9615  | 1400  | 1.0973          | 0.2803 | 0.6512 |
+| 1.3783        | 2.2411  | 1600  | 1.0624          | 0.2735 | 0.6373 |
+| 1.3352        | 2.5214  | 1800  | 1.0520          | 0.2734 | 0.6506 |
+| 1.3385        | 2.8017  | 2000  | 1.0440          | 0.2696 | 0.6354 |
+| 1.3509        | 3.0813  | 2200  | 1.0346          | 0.2699 | 0.6357 |
+| 1.3288        | 3.3616  | 2400  | 1.0316          | 0.2679 | 0.6272 |
+| 1.3297        | 3.6419  | 2600  | 1.0292          | 0.2717 | 0.6406 |
+| 1.3295        | 3.9222  | 2800  | 1.0210          | 0.2661 | 0.6227 |
+| 1.2545        | 4.2018  | 3000  | 1.0265          | 0.2675 | 0.6220 |
+| 1.2892        | 4.4821  | 3200  | 1.0432          | 0.2682 | 0.6307 |
+| 1.2743        | 4.7624  | 3400  | 1.0247          | 0.2696 | 0.6328 |
+| 1.2602        | 5.0420  | 3600  | 1.0064          | 0.2612 | 0.6320 |
+| 1.2304        | 5.3224  | 3800  | 0.9983          | 0.2605 | 0.6137 |
+| 1.2485        | 5.6027  | 4000  | 0.9865          | 0.2606 | 0.6153 |
+| 1.2809        | 5.8830  | 4200  | 1.0008          | 0.2621 | 0.6244 |
+| 1.2645        | 6.1626  | 4400  | 0.9878          | 0.2617 | 0.6184 |
+| 1.2498        | 6.4429  | 4600  | 0.9697          | 0.2592 | 0.6088 |
+| 1.2493        | 6.7232  | 4800  | 0.9764          | 0.2579 | 0.6072 |
+| 1.2766        | 7.0028  | 5000  | 0.9755          | 0.2598 | 0.6127 |
+| 1.2008        | 7.2831  | 5200  | 0.9856          | 0.2600 | 0.6158 |
+| 1.2236        | 7.5634  | 5400  | 0.9799          | 0.2589 | 0.6113 |
+| 1.2314        | 7.8437  | 5600  | 0.9697          | 0.2608 | 0.6214 |
+| 1.1737        | 8.1233  | 5800  | 0.9668          | 0.2552 | 0.6048 |
+| 1.226         | 8.4036  | 6000  | 0.9939          | 0.2596 | 0.6263 |
+| 1.1786        | 8.6840  | 6200  | 0.9626          | 0.2548 | 0.6069 |
+| 1.1727        | 8.9643  | 6400  | 0.9721          | 0.2546 | 0.6029 |
+| 1.1681        | 9.2439  | 6600  | 0.9593          | 0.2541 | 0.6023 |
+| 1.1573        | 9.5242  | 6800  | 0.9428          | 0.2518 | 0.6100 |
+| 1.1613        | 9.8045  | 7000  | 0.9520          | 0.2544 | 0.5994 |
+| 1.1747        | 10.0841 | 7200  | 0.9409          | 0.2514 | 0.5986 |
+| 1.1799        | 10.3644 | 7400  | 0.9372          | 0.2483 | 0.5955 |
+| 1.1322        | 10.6447 | 7600  | 0.9432          | 0.2505 | 0.5958 |
+| 1.1394        | 10.9250 | 7800  | 0.9521          | 0.2520 | 0.6004 |
+| 1.1284        | 11.2046 | 8000  | 0.9400          | 0.2508 | 0.5919 |
+| 1.1209        | 11.4849 | 8200  | 0.9439          | 0.2518 | 0.6017 |
+| 1.1309        | 11.7652 | 8400  | 0.9448          | 0.2488 | 0.5949 |
+| 1.0763        | 12.0448 | 8600  | 0.9377          | 0.2459 | 0.5935 |
+| 1.1177        | 12.3252 | 8800  | 0.9414          | 0.2480 | 0.5933 |
+| 1.0991        | 12.6055 | 9000  | 0.9406          | 0.2481 | 0.5961 |
+| 1.1344        | 12.8858 | 9200  | 0.9333          | 0.2466 | 0.5977 |
+| 1.0788        | 13.1654 | 9400  | 0.9256          | 0.2469 | 0.5916 |
+| 1.0727        | 13.4457 | 9600  | 0.9087          | 0.2447 | 0.5916 |
+| 1.0711        | 13.7260 | 9800  | 0.9107          | 0.2434 | 0.5881 |
+| 1.1605        | 14.0056 | 10000 | 0.9138          | 0.2448 | 0.5886 |
+| 1.1109        | 14.2859 | 10200 | 0.9238          | 0.2444 | 0.5929 |
+| 1.0427        | 14.5662 | 10400 | 0.9053          | 0.2423 | 0.5841 |
+| 1.0677        | 14.8465 | 10600 | 0.9126          | 0.2425 | 0.5873 |
+| 1.0662        | 15.1261 | 10800 | 0.9079          | 0.2429 | 0.5899 |
+| 1.0476        | 15.4064 | 11000 | 0.9074          | 0.2424 | 0.5866 |
+| 1.0644        | 15.6868 | 11200 | 0.9087          | 0.2431 | 0.5857 |
+| 1.0739        | 15.9671 | 11400 | 0.9019          | 0.2427 | 0.5902 |
+| 1.0621        | 16.2467 | 11600 | 0.8995          | 0.2414 | 0.5868 |
+| 1.0654        | 16.5270 | 11800 | 0.9013          | 0.2407 | 0.5847 |
+| 1.0352        | 16.8073 | 12000 | 0.8967          | 0.2410 | 0.5850 |
+| 1.0703        | 17.0869 | 12200 | 0.8984          | 0.2405 | 0.5845 |
+| 1.0605        | 17.3672 | 12400 | 0.8973          | 0.2414 | 0.5858 |
+| 1.0434        | 17.6475 | 12600 | 0.8939          | 0.2405 | 0.5818 |
+| 1.0416        | 17.9278 | 12800 | 0.8897          | 0.2401 | 0.5829 |
+| 1.038         | 18.2074 | 13000 | 0.8937          | 0.2389 | 0.5818 |
+| 1.0558        | 18.4877 | 13200 | 0.8930          | 0.2389 | 0.5822 |
+| 1.026         | 18.7680 | 13400 | 0.8899          | 0.2394 | 0.5813 |
+| 0.9742        | 19.0477 | 13600 | 0.8825          | 0.2380 | 0.5795 |
+| 1.0177        | 19.3280 | 13800 | 0.8843          | 0.2386 | 0.5786 |
+| 0.9995        | 19.6083 | 14000 | 0.8864          | 0.2392 | 0.5816 |
+| 1.002         | 19.8886 | 14200 | 0.8857          | 0.2393 | 0.5815 |
+
+
+### Framework versions
+
+- Transformers 4.52.1
+- Pytorch 2.9.1+cu128
+- Datasets 3.6.0
+- Tokenizers 0.21.4

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+  "xvector_output_dim": 512
+}

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preprocessor_config.json

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vocab.json

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