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-xls-r-300m-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: 38.57  # Required. Example: 20.90
-        name: Test WER    # Optional. Example: Test WER
-        args: 
-        - learning_rate: 0.0003
-        - train_batch_size: 64
-        - eval_batch_size: 8
-        - seed: 42
-        - gradient_accumulation_steps: 2
-        - 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: 30
-        - mixed_precision_training: Native AMP         # Optional. Example for BLEU: max_order
-      - type: cer    # Required. Example: wer
-        value: 10.98  # Required. Example: 20.90
-        name: Test CER    # Optional. Example: Test WER
-        args: 
-        - learning_rate: 0.0003
-        - train_batch_size: 64
-        - eval_batch_size: 8
-        - seed: 42
-        - gradient_accumulation_steps: 2
-        - 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: 30
-        - mixed_precision_training: Native AMP        # Optional. Example for BLEU: max_order
+- name: wav2vec2-large-xls-r-300m-Swedish
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -61,15 +16,28 @@ 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 common_voice dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.4286
-- Wer: 0.2729
-- Cer: 0.0858
+- Loss: 0.3641
+- Wer: 0.2473
+- Cer: 0.0758
+
+## 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: 0.0003
+- learning_rate: 7.5e-05
 - train_batch_size: 64
 - eval_batch_size: 8
 - seed: 42
@@ -78,18 +46,22 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_steps: 1000
-- num_epochs: 30
+- num_epochs: 50
 - mixed_precision_training: Native AMP
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
 |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
-| 4.6203        | 5.49  | 500  | 2.8904          | 1.0    | 1.0    |
-| 1.147         | 10.98 | 1000 | 0.5255          | 0.4107 | 0.1304 |
-| 0.5246        | 16.48 | 1500 | 0.4598          | 0.3342 | 0.1058 |
-| 0.378         | 21.97 | 2000 | 0.4316          | 0.2991 | 0.0949 |
-| 0.298         | 27.47 | 2500 | 0.4286          | 0.2729 | 0.0858 |
+| 6.1097        | 5.49  | 500  | 3.1422          | 1.0    | 1.0    |
+| 2.985         | 10.98 | 1000 | 1.7357          | 0.9876 | 0.4125 |
+| 1.0363        | 16.48 | 1500 | 0.4773          | 0.3510 | 0.1047 |
+| 0.6111        | 21.97 | 2000 | 0.3937          | 0.2998 | 0.0910 |
+| 0.4942        | 27.47 | 2500 | 0.3779          | 0.2776 | 0.0844 |
+| 0.4421        | 32.96 | 3000 | 0.3745          | 0.2630 | 0.0807 |
+| 0.4018        | 38.46 | 3500 | 0.3685          | 0.2553 | 0.0781 |
+| 0.3759        | 43.95 | 4000 | 0.3618          | 0.2488 | 0.0761 |
+| 0.3646        | 49.45 | 4500 | 0.3641          | 0.2473 | 0.0758 |
 
 
 ### Framework versions