Update app.py

app.py

@@ -1,63 +1,48 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering, Trainer, TrainingArguments
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+model = AutoModelForQuestionAnswering.from_pretrained("bert-base-uncased")
+
+train_dataset = ("squad")
+
+def tokenize_function(examples):
+    return tokenizer(
+        examples["questions"],
+        examples["context"],
+        truncation="only_second",
+
+        max_length=512,
+
+        padding="max_length",
+        stride=128,
+
+        return_overflowing_tokens=True,
+        return_offsets_mapping=True,
+        return_attention_mask=True,
+        return_token_type_ids=True,
+    )
+tokenized_datasets = dataset.map(
+    tokenize_function,
+    batched=True,
+    remove_columns=["id", "title", 
+                    "question", "context"],
+)
+training_args = TrainingArguments(
+    per_device_train_batch_size=8,
+    num_train_epochs=3,
+    logging_dir='./logs'
+)
+def compute_metrics(p):
+    return {}
+    
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset= tokenized_datasets["train"],
+    tokenizer=tokenizer,
+    compute_metrics=compute_metrics,
+)
+
+trainer.train()