update model card README.md

README.md

@@ -1,57 +1,12 @@
 ---
-language: 
-- pa-IN
-
-license: apache-2.0
+license: mit
 tags:
-- automatic-speech-recognition
-- robust-speech-event
+- generated_from_trainer
 datasets:
-- mozilla-foundation/common_voice_7_0
-metrics:
-- wer
-- cer
+- common_voice
 model-index:
 - name: wav2vec2-large-xlsr-53-punjabi
-  results:
-  - task: 
-      type: automatic-speech-recognition  # Required. Example: automatic-speech-recognition
-      name: Speech Recognition  # Optional. Example: Speech Recognition
-    dataset:
-      type: mozilla-foundation/common_voice_7_0  # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
-      name: Common Voice pa-IN  # Required. Example: Common Voice zh-CN
-      args: pa-IN         # Optional. Example: zh-CN
-    metrics:
-      - type: wer    # Required. Example: wer
-        value: 39.42  # Required. Example: 20.90
-        name: Test WER    # Optional. Example: Test WER
-        args: 
-        - learning_rate: 0.0003
-        - train_batch_size: 16
-        - eval_batch_size: 8
-        - seed: 42
-        - gradient_accumulation_steps: 2
-        - 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: 200
-        - num_epochs: 30
-        - mixed_precision_training: Native AMP         # Optional. Example for BLEU: max_order
-      - type: cer    # Required. Example: wer
-        value: 12.99  # Required. Example: 20.90
-        name: Test CER    # Optional. Example: Test WER
-        args: 
-        - learning_rate: 0.0003
-        - train_batch_size: 16
-        - eval_batch_size: 8
-        - seed: 42
-        - gradient_accumulation_steps: 2
-        - 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: 200
-        - num_epochs: 30
-        - mixed_precision_training: Native AMP         # Optional. Example for BLEU: max_order
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -59,11 +14,23 @@ should probably proofread and complete it, then remove this comment. -->
 
 # wav2vec2-large-xlsr-53-punjabi
 
-This model is a fine-tuned version of [manandey/wav2vec2-large-xlsr-punjabi](https://huggingface.co/manandey/wav2vec2-large-xlsr-punjabi) on the common_voice dataset.
+This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10) on the common_voice dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6752
-- Wer: 0.3942
-- Cer: 0.1299
+- Loss: 1.2101
+- Wer: 0.4939
+- Cer: 0.2238
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
 
 ## Training procedure
 
@@ -86,18 +53,19 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
 |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
-| 0.8899        | 4.16  | 100  | 0.5338          | 0.4233 | 0.1394 |
-| 0.3652        | 8.33  | 200  | 0.5759          | 0.4192 | 0.1349 |
-| 0.248         | 12.49 | 300  | 0.6309          | 0.4102 | 0.1327 |
-| 0.1898        | 16.65 | 400  | 0.6441          | 0.4007 | 0.1351 |
-| 0.1486        | 20.82 | 500  | 0.6790          | 0.4044 | 0.1393 |
-| 0.1245        | 24.98 | 600  | 0.6869          | 0.3987 | 0.1309 |
-| 0.1085        | 29.16 | 700  | 0.6752          | 0.3942 | 0.1299 |
+| 11.0563       | 3.7   | 100  | 1.9492          | 0.7123 | 0.3872 |
+| 1.6715        | 7.41  | 200  | 1.3142          | 0.6433 | 0.3086 |
+| 0.9117        | 11.11 | 300  | 1.2733          | 0.5657 | 0.2627 |
+| 0.666         | 14.81 | 400  | 1.2730          | 0.5598 | 0.2534 |
+| 0.4225        | 18.52 | 500  | 1.2548          | 0.5300 | 0.2399 |
+| 0.3209        | 22.22 | 600  | 1.2166          | 0.5229 | 0.2372 |
+| 0.2678        | 25.93 | 700  | 1.1795          | 0.5041 | 0.2276 |
+| 0.2088        | 29.63 | 800  | 1.2101          | 0.4939 | 0.2238 |
 
 
 ### Framework versions
 
-- Transformers 4.15.0
-- Pytorch 1.10.0+cu111
-- Datasets 1.17.0
-- Tokenizers 0.10.3
+- Transformers 4.17.0.dev0
+- Pytorch 1.10.2+cu102
+- Datasets 1.18.2.dev0
+- Tokenizers 0.11.0