update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- wikiann
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: indic-transformers-te-distilbert
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: wikiann
+      type: wikiann
+      args: te
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.5657225853304285
+    - name: Recall
+      type: recall
+      value: 0.6486261448792673
+    - name: F1
+      type: f1
+      value: 0.604344453064391
+    - name: Accuracy
+      type: accuracy
+      value: 0.9049186160277506
+---
+
+<!-- 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. -->
+
+# indic-transformers-te-distilbert
+
+This model was trained from scratch on the wikiann dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2940
+- Precision: 0.5657
+- Recall: 0.6486
+- F1: 0.6043
+- Accuracy: 0.9049
+
+## 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: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 125  | 0.3629          | 0.4855    | 0.5287 | 0.5062 | 0.8826   |
+| No log        | 2.0   | 250  | 0.3032          | 0.5446    | 0.6303 | 0.5843 | 0.9002   |
+| No log        | 3.0   | 375  | 0.2940          | 0.5657    | 0.6486 | 0.6043 | 0.9049   |
+
+
+### Framework versions
+
+- Transformers 4.15.0
+- Pytorch 1.10.0+cu111
+- Datasets 1.17.0
+- Tokenizers 0.10.3