Update README.md

README.md

@@ -1,54 +1,34 @@
 ---
 license: apache-2.0
-base_model: Jayveersinh-Raj/mpt5s-guj-grammar-2
-tags:
-- generated_from_keras_callback
-model-index:
-- name: Jayveersinh-Raj/mpt5s-guj-grammar-2-3
-  results: []
+language:
+- gu
 ---
 
 <!-- This model card has been generated automatically according to the information Keras had access to. You should
 probably proofread and complete it, then remove this comment. -->
 
-# Jayveersinh-Raj/mpt5s-guj-grammar-2-3
-
-This model is a fine-tuned version of [Jayveersinh-Raj/mpt5s-guj-grammar-2](https://huggingface.co/Jayveersinh-Raj/mpt5s-guj-grammar-2) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Train Loss: 0.0777
-- Validation Loss: 0.0375
-- Epoch: 0
-
-## 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:
-- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5.6e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 197899, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 100, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
-- training_precision: mixed_float16
-
-### Training results
-
-| Train Loss | Validation Loss | Epoch |
-|:----------:|:---------------:|:-----:|
-| 0.0777     | 0.0375          | 0     |
-
-
-### Framework versions
-
-- Transformers 4.32.1
-- TensorFlow 2.12.0
-- Datasets 2.14.4
-- Tokenizers 0.13.3
+# Model description
+The model is a mt5-small version of Gujarati Grammarly for spell correction given a sentence. Only this small version checkpoints are open source. 
+
+# Example usage:
+    from transformers import AutoTokenizer
+    import tensorflow as tf
+    from transformers import TFAutoModelForSeq2SeqLM
+    from transformers import create_optimizer
+
+    model_checkpoint = "Jayveersinh-Raj/guj-grammar-small"
+    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+    model = TFAutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
+     
+    sent="સુંદરકાંડના પ્રારંભમાં હનૂમાન બળવાન તો છે પણ સાથે-સાથે બુદ્ધિમાન પણ છે તેની રોચક ધર્મકથા છૈ"
+    inputs = tokenizer.encode(sent, return_tensors='tf')
+    output_ids = model.generate(inputs, max_length=128, num_beams = 4, early_stopping=True)
+    output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+    print("Generated Correction:")
+    print(output)
+
+# Notes:
+- Only supports Gujarati language for now
+- Private dataset is used
+- Only Tensorflow model is available for now, Pytorch checkpoints would be available soon.