Create README.md

README.md

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+---
+language:
+- en
+---
+# How to use model
+
+## Load model and tokenizer
+```
+import torch
+from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer
+
+torch.set_default_device("cuda")
+
+model_name = "dcipheranalytics/phi-2-pii-bbi"
+
+quantization_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_compute_dtype=torch.bfloat16,
+        bnb_4bit_quant_type="nf4",
+    )
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    # torch_dtype="auto",
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    quantization_config=quantization_config,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+```
+
+## Call generate method
+```
+def generate(msg: str, max_new_tokens = 300, temperature=0.3):
+    chat_template = "<|im_start|>user\n{msg}<|im_end|><|im_start|>assistant\n"
+    prompt = chat_template.format(msg=msg)
+
+    with torch.no_grad():
+        token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
+        output_ids = model.generate(
+            token_ids.to(model.device),
+            max_new_tokens=max_new_tokens,
+            do_sample=True,
+            temperature=temperature,
+            pad_token_id=tokenizer.eos_token_id,
+            eos_token_id=tokenizer.eos_token_id,
+        )
+        output = tokenizer.decode(output_ids[0][token_ids.size(1):-1]).strip()
+        return output
+
+instruction_template = "List the personally identifiable information in the given text below.\nText:########\n{text}\n########"
+text_with_pii = "My passport number is 123456789."
+generate(instruction_template.format(text=text_with_pii))
+```
+
+
+## Batch predictions
+```
+from transformers import TextGenerationPipeline
+
+def get_prompt(text):
+  instruction_template = "List the personally identifiable information in the given text below.\nText:########\n{text}\n########"
+  msg = instruction_template.format(text=text)
+  chat_template = "<|im_start|>user\n{msg}<|im_end|><|im_start|>assistant\n"
+  prompt = chat_template.format(msg=msg)
+
+  return prompt
+
+generator = TextGenerationPipeline(
+                     model=model,
+                     tokenizer=tokenizer,
+                     max_new_tokens=300,
+                     do_sample=True,
+                     temperature=0.3,
+                     pad_token_id=tokenizer.eos_token_id,
+                     eos_token_id=tokenizer.eos_token_id,
+                     )
+
+texts = ["My passport number is 123456789.",
+         "My name is John Smith.",
+]
+prompts = list(map(get_prompt, texts))
+outputs = generator(prompts, 
+                  return_full_text=False, 
+                  batch_size=2)
+```
+
+# Train Data
+
+GPT4 generated customer service conversations. 
+1. 100 unique banking topics, 8 examples per each,
+2. New 100 banking topics, 4 examples per each,
+3. 100 insurance topics, 4 examples per each.
+
+# Evaluation Results
+
+## Average
+```
+precision    0.836223
+recall       0.781132
+f1           0.801837
+```
+
+## Per topic:
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/63ea400bb1d9c4ef71ebb962/wUfwR-dmmyxF4pCYoebCX.png)
+
+## On TAB test split:
+```
+precision    0.506118
+recall       0.350976
+f1           0.391614
+```