Create README.md

README.md

@@ -0,0 +1,97 @@
+---
+language:
+- sl
+license: apache-2.0
+tags:
+- automatic-speech-recognition
+- mozilla-foundation/common_voice_8_0
+- generated_from_trainer
+- sl
+- robust-speech-event
+- model_for_talk
+datasets:
+- common_voice
+model-index:
+- name: wav2vec2-large-xls-r-300m-sl-with-LM-v2
+  results:
+  - task: 
+      name: Automatic Speech Recognition 
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice 8
+      type: mozilla-foundation/common_voice_8_0
+      args: sl
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 0.21695212999560826
+       - name: Test CER
+         type: cer
+         value: 0.052850080572474256
+---
+
+<!-- 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. -->
+
+# 
+
+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_8_0 - SL dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2855
+- Wer: 0.2401
+
+## 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: 7e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 1000
+- num_epochs: 100.0
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Wer    |
+|:-------------:|:-----:|:----:|:---------------:|:------:|
+| 6.9294        | 6.1   | 500  | 2.9712          | 1.0    |
+| 2.8305        | 12.2  | 1000 | 1.7073          | 0.9479 |
+| 1.4795        | 18.29 | 1500 | 0.5756          | 0.6397 |
+| 1.3433        | 24.39 | 2000 | 0.4968          | 0.5424 |
+| 1.1766        | 30.49 | 2500 | 0.4185          | 0.4743 |
+| 1.0017        | 36.59 | 3000 | 0.3303          | 0.3578 |
+| 0.9358        | 42.68 | 3500 | 0.3003          | 0.3051 |
+| 0.8358        | 48.78 | 4000 | 0.3045          | 0.2884 |
+| 0.7647        | 54.88 | 4500 | 0.2866          | 0.2677 |
+| 0.7482        | 60.98 | 5000 | 0.2829          | 0.2585 |
+| 0.6943        | 67.07 | 5500 | 0.2782          | 0.2478 |
+| 0.6586        | 73.17 | 6000 | 0.2911          | 0.2537 |
+| 0.6425        | 79.27 | 6500 | 0.2817          | 0.2462 |
+| 0.6067        | 85.37 | 7000 | 0.2910          | 0.2436 |
+| 0.5974        | 91.46 | 7500 | 0.2875          | 0.2430 |
+| 0.5812        | 97.56 | 8000 | 0.2852          | 0.2396 |
+
+
+### Framework versions
+
+- Transformers 4.17.0.dev0
+- Pytorch 1.10.2+cu102
+- Datasets 1.18.2.dev0
+- Tokenizers 0.11.0