Create README.md

README.md

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+---
+datasets:
+- voidful/NMSQA
+language:
+- en
+metrics:
+- wer
+pipeline_tag: automatic-speech-recognition
+---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This model was pretrained using facebook/hubert-base-ls960 model on NMSQA dataset. The task is Automatic Speech Recognition (ASR) in which the questions and context sentences are used.  
+This is a checkpoint with WER 14.36 on dev set. 
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+The input of the models are from NMSQA dataset. The task of the dataset is Spoken QA, but in this model I used the sentences for ASR.
+The input audios are both from context and questions. This ASR model was trained on using training and dev set of NMSQA. 
+
+- **Developed by:** Merve Menevse
+- **Model type:** Supervised ML
+- **Language(s) (NLP):** English
+- **Finetuned from model [optional]:** facebook/wav2vec2-base-960h
+
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+The model should be used as fine-tuned model for wav2vec2. 
+
+## How to Get Started with the Model
+
+     from transformers import AutoModel
+
+     model = AutoModel.from_pretrained("menevsem/hubert-base-ls960-nmsqa-asr")
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+ The model was trained using voidful/NMSQA train and dev set. 
+
+## Evaluation
+
+For evalaution WER metric is used on dev set.