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--- |
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library_name: peft |
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base_model: meta-llama/Llama-2-7b-chat-hf |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** [More Information Needed] |
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- **Funded by [optional]:** [More Information Needed] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** [More Information Needed] |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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### Infrence Function |
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#### for keyword brand |
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def generate_brand(keyword): |
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# Define the roles and markers |
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B_INST, E_INST = "[INST]", "[/INST]" |
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B_KW, E_KW = "[KW]", "[/KW]" |
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# Format your prompt template |
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prompt = f"""{B_INST} Extract the brand from keyword related to brand loyalty intent.{E_INST}\n |
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{B_KW} {keyword} {E_KW} |
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""" |
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# print("Prompt:") |
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# print(prompt) |
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encoding = tokenizer(prompt, return_tensors="pt").to("cuda:0") |
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output =model.generate(input_ids=encoding.input_ids, |
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attention_mask=encoding.attention_mask, |
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max_new_tokens=20, |
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do_sample=True, |
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temperature=0.01, |
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eos_token_id=tokenizer.eos_token_id, |
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top_k=0) |
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#print() |
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# Subtract the length of input_ids from output to get only the model's response |
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output_text = tokenizer.decode(output[0, len(encoding.input_ids[0]):], skip_special_tokens=False) |
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output_text = re.sub('\n+', '\n', output_text) # remove excessive newline characters |
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#print("Generated Assistant Response:") |
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return output_text |
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#### for keyword category |
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def generate_cat(list_cat,keyword): |
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# Define the roles and markers |
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B_INST, E_INST = "[INST]", "[/INST]" |
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B_KW, E_KW = "[KW]", "[/KW]" |
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# Format your prompt template |
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prompt = f"""{B_INST} Analyze the following keyword searched on amazon with intent of shopping. Identify the product category from the list {list_cat} {E_INST}\n |
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{B_KW} {keyword} {E_KW} |
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""" |
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# print("Prompt:") |
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# print(prompt) |
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encoding = tokenizer(prompt, return_tensors="pt").to("cuda:0") |
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output =model.generate(input_ids=encoding.input_ids, |
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attention_mask=encoding.attention_mask, |
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max_new_tokens=20, |
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do_sample=True, |
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temperature=0.01, |
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eos_token_id=tokenizer.eos_token_id, |
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top_k=0) |
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#print() |
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# Subtract the length of input_ids from output to get only the model's response |
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output_text = tokenizer.decode(output[0, len(encoding.input_ids[0]):], skip_special_tokens=False) |
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output_text = re.sub('\n+', '\n', output_text) # remove excessive newline characters |
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#print("Generated Assistant Response:") |
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return output_text |
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#### for keyword category and brand |
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def generate_cat(list_cat,keyword): |
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# Define the roles and markers |
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B_INST, E_INST = "[INST]", "[/INST]" |
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B_KW, E_KW = "[KW]", "[/KW]" |
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# Format your prompt template |
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prompt = f"""{B_INST} Analyze the following keyword searched on amazon with intent of shopping. Identify the product category from the list {list_cat}. |
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Extract the brand from keyword related to brand loyalty intent. Output in JSON with keyword, product category, brand as keys.{E_INST}\n |
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{B_KW} {keyword} {E_KW} |
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""" |
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# print("Prompt:") |
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# print(prompt) |
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encoding = tokenizer(prompt, return_tensors="pt").to("cuda:0") |
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output =model.generate(input_ids=encoding.input_ids, |
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attention_mask=encoding.attention_mask, |
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max_new_tokens=20, |
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do_sample=True, |
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temperature=0.01, |
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eos_token_id=tokenizer.eos_token_id, |
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top_k=0) |
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#print() |
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# Subtract the length of input_ids from output to get only the model's response |
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output_text = tokenizer.decode(output[0, len(encoding.input_ids[0]):], skip_special_tokens=False) |
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output_text = re.sub('\n+', '\n', output_text) # remove excessive newline characters |
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#print("Generated Assistant Response:") |
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return output_text |
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