End of training

README.md

@@ -1,199 +1,131 @@
 ---
 library_name: transformers
-tags: []
+license: cc-by-nc-4.0
+base_model: facebook/mms-1b-all
+tags:
+- generated_from_trainer
+datasets:
+- fleurs
+metrics:
+- wer
+model-index:
+- name: wav2vec2-large-mms-1b-igbo
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: fleurs
+      type: fleurs
+      config: ig_ng
+      split: test
+      args: ig_ng
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.444900640499261
 ---
 
-# 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.
-
-- **Developed by:** [More Information Needed]
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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 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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+<!-- 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. -->
+
+# wav2vec2-large-mms-1b-igbo
+
+This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the fleurs dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4649
+- Wer: 0.4449
+
+## 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: 0.001
+- train_batch_size: 4
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use 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: 4
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
+|:-------------:|:------:|:-----:|:---------------:|:------:|
+| 0.464         | 0.0731 | 1000  | 0.7265          | 0.5768 |
+| 0.4324        | 0.1463 | 2000  | 0.7455          | 0.6102 |
+| 0.4307        | 0.2194 | 3000  | 1.1129          | 0.6445 |
+| 0.3982        | 0.2925 | 4000  | 0.7999          | 0.5870 |
+| 0.3915        | 0.3657 | 5000  | 0.7252          | 0.5210 |
+| 0.3834        | 0.4388 | 6000  | 0.7565          | 0.5677 |
+| 0.376         | 0.5120 | 7000  | 0.7596          | 0.6294 |
+| 0.388         | 0.5851 | 8000  | 0.6784          | 0.5679 |
+| 0.3687        | 0.6582 | 9000  | 0.7597          | 0.5916 |
+| 0.374         | 0.7314 | 10000 | 0.6482          | 0.5023 |
+| 0.3576        | 0.8045 | 11000 | 0.6486          | 0.5572 |
+| 0.3621        | 0.8776 | 12000 | 0.5482          | 0.4869 |
+| 0.363         | 0.9508 | 13000 | 0.6543          | 0.5082 |
+| 0.3549        | 1.0239 | 14000 | 0.5477          | 0.4849 |
+| 0.342         | 1.0971 | 15000 | 0.5505          | 0.5079 |
+| 0.3296        | 1.1702 | 16000 | 0.5701          | 0.5211 |
+| 0.3363        | 1.2433 | 17000 | 0.5565          | 0.5281 |
+| 0.3265        | 1.3165 | 18000 | 0.6660          | 0.5794 |
+| 0.327         | 1.3896 | 19000 | 0.5414          | 0.4854 |
+| 0.3319        | 1.4627 | 20000 | 0.5677          | 0.5181 |
+| 0.3273        | 1.5359 | 21000 | 0.5482          | 0.4901 |
+| 0.3209        | 1.6090 | 22000 | 0.5475          | 0.5019 |
+| 0.3153        | 1.6821 | 23000 | 0.5278          | 0.4723 |
+| 0.3214        | 1.7553 | 24000 | 0.5232          | 0.4809 |
+| 0.3227        | 1.8284 | 25000 | 0.5419          | 0.4950 |
+| 0.306         | 1.9016 | 26000 | 0.5120          | 0.4653 |
+| 0.2956        | 1.9747 | 27000 | 0.5043          | 0.4790 |
+| 0.2875        | 2.0478 | 28000 | 0.5111          | 0.4592 |
+| 0.3158        | 2.1210 | 29000 | 0.4959          | 0.4582 |
+| 0.2906        | 2.1941 | 30000 | 0.4857          | 0.4577 |
+| 0.2985        | 2.2672 | 31000 | 0.4897          | 0.4625 |
+| 0.2877        | 2.3404 | 32000 | 0.4869          | 0.4667 |
+| 0.2832        | 2.4135 | 33000 | 0.4877          | 0.4541 |
+| 0.2815        | 2.4867 | 34000 | 0.4869          | 0.4598 |
+| 0.28          | 2.5598 | 35000 | 0.4935          | 0.4624 |
+| 0.2904        | 2.6329 | 36000 | 0.4859          | 0.4540 |
+| 0.2767        | 2.7061 | 37000 | 0.4879          | 0.4550 |
+| 0.2801        | 2.7792 | 38000 | 0.4855          | 0.4536 |
+| 0.2711        | 2.8523 | 39000 | 0.5059          | 0.4674 |
+| 0.2652        | 2.9255 | 40000 | 0.4715          | 0.4512 |
+| 0.276         | 2.9986 | 41000 | 0.4804          | 0.4568 |
+| 0.2556        | 3.0717 | 42000 | 0.4869          | 0.4572 |
+| 0.275         | 3.1449 | 43000 | 0.4761          | 0.4536 |
+| 0.2615        | 3.2180 | 44000 | 0.4848          | 0.4679 |
+| 0.264         | 3.2912 | 45000 | 0.4722          | 0.4518 |
+| 0.2554        | 3.3643 | 46000 | 0.4747          | 0.4551 |
+| 0.2632        | 3.4374 | 47000 | 0.4695          | 0.4507 |
+| 0.2565        | 3.5106 | 48000 | 0.4761          | 0.4506 |
+| 0.2555        | 3.5837 | 49000 | 0.4802          | 0.4619 |
+| 0.2397        | 3.6568 | 50000 | 0.4687          | 0.4497 |
+| 0.2599        | 3.7300 | 51000 | 0.4684          | 0.4506 |
+| 0.2451        | 3.8031 | 52000 | 0.4678          | 0.4504 |
+| 0.2623        | 3.8763 | 53000 | 0.4642          | 0.4461 |
+| 0.2475        | 3.9494 | 54000 | 0.4649          | 0.4449 |
+
+
+### Framework versions
+
+- Transformers 4.54.0.dev0
+- Pytorch 2.5.1+cu121
+- Datasets 3.2.0
+- Tokenizers 0.21.0

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