Update README.md

README.md

@@ -1,7 +1,71 @@
 ---
-license: cc
+language:
+- sv-SE
+license: apache-2.0
+tags:
+- automatic-speech-recognition
+- mozilla-foundation/common_voice_7_0
+- generated_from_trainer
+- no
+- robust-speech-event
+- model_for_talk
+datasets:
+- mozilla-foundation/common_voice_7_0
+model-index:
+- name: XLS-R-300M-LM - Norwegian
+  results:
+  - task: 
+      name: Automatic Speech Recognition 
+      type: automatic-speech-recognition
+    dataset:
+      name: NPSC
+      type: NbAiLab/NPSC
+      args: sv-SE
+    metrics:
+       - name: Eval WER
+         type: wer
+         value: 21.10
+       - name: Eval CER
+         type: cer
+         value: 0.06
+
 ---
-This is based on checkpoint 8500 of NbAiLab/wav2vec2-xlsr-300M-NPSC-OH
 
+# XLS-R-300M-LM - Norwegian
+
+This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - SV-SE dataset.
+### Scores without Language Model
+Without using a language model, it achieves the following scores on the NPSC Eval set
+It achieves the following results on the evaluation set without a language model:
+- Loss: 0.1992
+- WER: 0.2110
+- CER: 0.0622
+
+### Scores with Language Model
+A 5-gram KenLM was added to boost the models performance. After 
+
+## Model description
+This current version is based on checkpoint 8500 of [NbAiLab/wav2vec2-xlsr-300M-NPSC-OH](https://huggingface.co/NbAiLab/wav2vec2-xlsr-300M-NPSC-OH)
+
+## Intended uses & limitations
+Demo version only. The model will be updated later this week.
+
+## Training and evaluation data
+The model is trained and evaluated on [NPSC](https://huggingface.co/datasets/NbAiLab/NPSC). Unfortunately there is no Norwegian test data in Common Voice, and currently the model is only evaluated on the validation set of NPSC..
+
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 7.5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 2000
+- num_epochs: 30.0 (But interrupted after 8500 steps, approx 6 epochs)
+- mixed_precision_training: Native AMP