add train model base code

README.md

@@ -24,6 +24,6 @@ docker run -it -p 8501:8501 --platform=linux/amd64 \
 
 ### API
 
-docker run -it -p 8501:8501 --platform=linux/amd64 \
+docker run -it -p 5001:5000 --platform=linux/amd64 \
 	-e LAUNCHDARKLY_SDK_KEY="sdk-142d656c-d430-4f8c-b2f1-7275f2ec65ff" \
 	registry.hf.space/asgface-sentimentai:latest python api.py

requirements.txt

@@ -2,4 +2,5 @@ streamlit
 transformers
 launchdarkly-server-sdk
 torch
-Flask
+Flask
+datasets

training/train.py

@@ -0,0 +1,41 @@
+from transformers import AlbertForSequenceClassification, AlbertTokenizer, Trainer, TrainingArguments
+from datasets import load_dataset
+
+# Load a dataset (replace with your dataset)
+dataset = load_dataset("text", data_files={"train": "path/to/train.txt", "test": "path/to/test.txt"})
+
+# Preprocess the dataset (tokenization, formatting, etc.)
+def preprocess_function(examples):
+	return tokenizer(examples["text"], padding="max_length", truncation=True)
+
+tokenizer = AlbertTokenizer.from_pretrained("albert-base-v2")
+tokenized_dataset = dataset.map(preprocess_function, batched=True)
+
+# Load the model
+model = AlbertForSequenceClassification.from_pretrained("albert-base-v2", num_labels=2) # Adjust num_labels as needed
+
+# Define training arguments
+training_args = TrainingArguments(
+	output_dir="./results",
+	num_train_epochs=3,
+	per_device_train_batch_size=8,
+	per_device_eval_batch_size=8,
+	warmup_steps=500,
+	weight_decay=0.01,
+	evaluate_during_training=True,
+	logging_dir="./logs",
+)
+
+# Initialize the Trainer
+trainer = Trainer(
+	model=model,
+	args=training_args,
+	train_dataset=tokenized_dataset["train"],
+	eval_dataset=tokenized_dataset["test"]
+)
+
+# Train the model
+trainer.train()
+
+# Save the fine-tuned model
+model.save_pretrained("path/to/save/model")