Create README.md

README.md

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+---
+language: 
+- bn
+license: apache-2.0
+tags:
+- automatic-speech-recognition
+- openslr_SLR53
+- robust-speech-event
+datasets:
+- openslr
+- SLR53
+metrics:
+- wer
+- cer
+model-index:
+- name: Tahsin-Mayeesha/wav2vec2-bn-300m
+  results:
+  - task: 
+      type: automatic-speech-recognition
+      name: Speech Recognition
+    dataset:
+      type: openslr
+      name: Open SLR
+      args: SLR66
+    metrics:
+      - type: wer    # Required. Example: wer
+        value: 0.31104373941386626  # Required. Example: 20.90
+        name: Test WER    # Optional. Example: Test WER
+      - type: cer
+        value: 0.07263099973420006
+        name: Test CER
+---
+
+This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the OPENSLR_SLR53 - bengali dataset.
+It achieves the following results on the evaluation set:
+- Wer: 0.3467
+- Cer : 0.072
+
+Note : 1% of a total 218703 samples have been used for evaluation. Evaluation set has 21871 examples. Training was stopped after 30k steps. Output predictions are available under files section.
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 7.5e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- gradient_accumulation_steps: 4
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 2000
+- mixed_precision_training: Native AMP
+
+### Framework versions
+
+- Transformers 4.16.0.dev0
+- Pytorch 1.10.1+cu102
+- Datasets 1.17.1.dev0
+- Tokenizers 0.11.0
+