update model card README.md

README.md

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+---
+language:
+- pl
+tags:
+- whisper-event
+- generated_from_trainer
+datasets:
+- mozilla-foundation/common_voice_11_0
+metrics:
+- wer
+model-index:
+- name: Whisper Large v2 PL
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice 11.0
+      type: mozilla-foundation/common_voice_11_0
+      config: pl
+      split: test
+      args: pl
+    metrics:
+    - name: Wer
+      type: wer
+      value: 7.280175959972464
+---
+
+<!-- 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. -->
+
+# Whisper Large v2 PL
+
+This model is a fine-tuned version of [bardsai/whisper-large-v2-pl-v2](https://huggingface.co/bardsai/whisper-large-v2-pl-v2) on the Common Voice 11.0 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3684
+- Wer: 7.2802
+
+## 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: 1e-05
+- train_batch_size: 8
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- training_steps: 2100
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Wer    |
+|:-------------:|:-----:|:----:|:---------------:|:------:|
+| 0.0047        | 1.35  | 700  | 0.3428          | 8.5562 |
+| 0.0011        | 2.7   | 1400 | 0.3605          | 7.5505 |
+| 0.0003        | 4.05  | 2100 | 0.3684          | 7.2802 |
+
+
+### Framework versions
+
+- Transformers 4.26.0.dev0
+- Pytorch 1.13.0+cu117
+- Datasets 2.7.1.dev0
+- Tokenizers 0.13.2