update model card README.md

README.md

@@ -1,57 +1,12 @@
 ---
-language: 
-- sv-SE
-
 license: apache-2.0
 tags:
-- automatic-speech-recognition
-- robust-speech-event
+- generated_from_trainer
 datasets:
-- mozilla-foundation/common_voice_8_0
-metrics:
-- wer
-- cer
+- common_voice
 model-index:
 - name: wav2vec2-large-xls-r-1b-Swedish
-  results:
-  - task: 
-      type: automatic-speech-recognition  # Required. Example: automatic-speech-recognition
-      name: Speech Recognition  # Optional. Example: Speech Recognition
-    dataset:
-      type: mozilla-foundation/common_voice_8_0  # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
-      name: Common Voice sv-SE # Required. Example: Common Voice zh-CN
-      args: sv-SE       # Optional. Example: zh-CN
-    metrics:
-      - type: wer    # Required. Example: wer
-        value: 18.03  # Required. Example: 20.90
-        name: Test WER Without LM   # Optional. Example: Test WER
-        args: 
-        - learning_rate: 7.5e-05
-        - train_batch_size: 32
-        - eval_batch_size: 8
-        - seed: 42
-        - gradient_accumulation_steps: 4
-        - total_train_batch_size: 128
-        - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-        - lr_scheduler_type: linear
-        - lr_scheduler_warmup_steps: 1000
-        - num_epochs: 50
-        - mixed_precision_training: Native AMP    # Optional. Example for BLEU: max_order
-      - type: cer    # Required. Example: wer
-        value: 5.69  # Required. Example: 20.90
-        name: Test CER  Without LM   # Optional. Example: Test WER
-        args: 
-        - learning_rate: 7.5e-05
-        - train_batch_size: 32
-        - eval_batch_size: 8
-        - seed: 42
-        - gradient_accumulation_steps: 4
-        - total_train_batch_size: 128
-        - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-        - lr_scheduler_type: linear
-        - lr_scheduler_warmup_steps: 1000
-        - num_epochs: 50
-        - mixed_precision_training: Native AMP
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -61,15 +16,21 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.3232
+- Wer: 0.1844
+- Cer: 0.0575
+
+## Model description
+
+More information needed
 
-**Without LM**
-- Loss: 0.3370
-- Wer: 0.1803
-- Cer: 0.0569
+## Intended uses & limitations
 
-**With LM**
+More information needed
 
+## Training and evaluation data
 
+More information needed
 
 ## Training procedure
 
@@ -77,11 +38,11 @@ It achieves the following results on the evaluation set:
 
 The following hyperparameters were used during training:
 - learning_rate: 7.5e-05
-- train_batch_size: 32
+- train_batch_size: 64
 - eval_batch_size: 8
 - seed: 42
 - gradient_accumulation_steps: 4
-- total_train_batch_size: 128
+- total_train_batch_size: 256
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_steps: 1000
@@ -92,15 +53,10 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
 |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
-| 3.1423        | 5.49  | 500  | 0.5523          | 0.4414 | 0.1313 |
-| 0.8615        | 10.98 | 1000 | 0.3877          | 0.2946 | 0.0942 |
-| 0.4848        | 16.48 | 1500 | 0.3580          | 0.2539 | 0.0798 |
-| 0.3538        | 21.97 | 2000 | 0.3391          | 0.2254 | 0.0709 |
-| 0.2879        | 27.47 | 2500 | 0.3392          | 0.2151 | 0.0680 |
-| 0.2466        | 32.96 | 3000 | 0.3687          | 0.2131 | 0.0680 |
-| 0.2146        | 38.46 | 3500 | 0.3551          | 0.1951 | 0.0618 |
-| 0.1916        | 43.95 | 4000 | 0.3601          | 0.1867 | 0.0590 |
-| 0.175         | 49.45 | 4500 | 0.3370          | 0.1803 | 0.0569 |
+| 3.1562        | 11.11 | 500  | 0.4830          | 0.3729 | 0.1169 |
+| 0.5655        | 22.22 | 1000 | 0.3553          | 0.2381 | 0.0743 |
+| 0.3376        | 33.33 | 1500 | 0.3359          | 0.2179 | 0.0696 |
+| 0.2419        | 44.44 | 2000 | 0.3232          | 0.1844 | 0.0575 |
 
 
 ### Framework versions