Update README.md

README.md

@@ -1,11 +1,13 @@
 ---
-license: mit
+license: apache-2.0
 tags:
 - generated_from_trainer
-base_model: gpt2
+base_model: gpt3.5
 model-index:
 - name: yes_no_model_english
   results: []
+language:
+- en
 ---
 -----
 label_map = {'True': 0, 'False': 1, 'Invalid input': 2}
@@ -25,13 +27,70 @@ More information needed
 
 ## Intended uses & limitations
 
-More information needed
-
-## Training and evaluation data
+```from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
+from transformers import GPT2Tokenizer, GPT2ForSequenceClassification, Trainer, TrainingArguments
+
+# Replace 'your-username/your-model-name' with the actual model identifier
+model_id = 'tuskbyte/yes_no_model_english'
+label_map=["Yes","NO","Invalid Input"]
+# label_map = {'True': 0, 'False': 1, 'Invalid input': 2}
+
+# Load the model
+model = AutoModelForSequenceClassification.from_pretrained(model_id)
+
+try:
+    # Try to load the tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+except OSError:
+    # Fallback to a default tokenizer if loading fails
+    print(f"Tokenizer for '{model_id}' not found. Using  gpt as fallback.")
+    tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+
+# Initialize Trainer with dummy arguments for inference
+training_args = TrainingArguments(
+    output_dir='./results',  # specify your output directory
+    per_device_eval_batch_size=1  # batch size for inference
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    tokenizer=tokenizer
+)
+
+# Example input
+question = "Would you like to paticipate ?"
+answer = "yes i would"
+input_text = f"{question} {answer}"
+
+# Tokenize the input
+inputs = tokenizer(input_text, return_tensors="pt")
+model.to('cuda')
+inputs.to('cuda')
+# Perform inference using the model
+outputs = model(**inputs)
+logits = outputs.logits
+
+# Get the predicted label
+predicted_class_id = logits.argmax().item()
+print("predicted_class_id",predicted_class_id)
+labels = model.config.id2label
+print("labels",labels)
+predicted_label = labels[predicted_class_id]
+
+# Output the result
+print(f"Predicted label: {predicted_label}")
+print(f"Model predection is : {label_map[predicted_class_id]}")
+```
+```
+support english only
+```
 
-More information needed
 
 ## Training procedure
+```
+upcomming soon
+```
 
 ### Training hyperparameters
 
@@ -66,4 +125,4 @@ The following hyperparameters were used during training:
 - Transformers 4.41.2
 - Pytorch 2.1.2
 - Datasets 2.19.2
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1