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Cataloged from microsoft/Sentiment-Analysis-7B
All models by microsoft →Sentiment-Analysis-7B is a 7B-parameter autoregressive language model fine-tuned for sentiment classification of user messages directed at a service, product, or agent (the "Target"). It is built on top of mistralai/Mistral-7B-Instruct-v0.2 and was fine-tuned with reinforcement learning, using an LLM as a judge, on a small internal training set of ~300 examples. No pre-defined labels were used for training.
Given a user message, the model generates a response in the following format: the first token is the sentiment label (Negative, Neutral, or Positive), followed by a short explanation of the decision. If only the label is needed, set max_new_tokens=3.
The evaluation metric used is accuracy. The following compares the base model with the fine-tuned model.
The model is a causal language model and is loaded with AutoModelForCausalLM. The sentiment label is always the first generated token, so setting max_new_tokens=3 returns the label only. Use the system prompt below, which defines the "Target" and the sentiment rules.
Output:
To get the explanation as well, increase max_new_tokens:
Output:
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "microsoft/Sentiment-Analysis-7B"
tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto")
system_prompt = """Return sentiment of user message as Positive/Neutral/Negative and explanation. Ex: Negative, the user expresses blame (as "you lie"), which conveys a negative tone.Definition of "Target": the current service, product, or agent the user is interacting with (including its website, app, API, or support).Rules of sentiment:* Questions/statements clearly unrelated to Target → Neutral* Otherwise, if related to Target: * Questions containing complaint/frustration/blame → Negative. Ex, What is the point of writing here if technical support does not respond? * Indirect incidents → Neutral. Ex, submit helpdesk ticket: mobile app crashes on startup * Statement of any inability/failure → Negative. Ex, I'm having trouble logging in. * Failure statement + polite/help request → Neutral. Ex, Login button is not working. Can you help?"""
sample = "I'm having trouble logging in to my account."
messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": sample},]inputs = tokenizer.apply_chat_template( messages, tokenize=True, return_dict=True, return_tensors="pt", add_generation_prompt=True,).to(model.device)
# Label onlyoutput = model.generate(**inputs, max_new_tokens=3, do_sample=False)print(tokenizer.decode(output[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True))Negativeoutput = model.generate(**inputs, max_new_tokens=64, do_sample=False)print(tokenizer.decode(output[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True))Negative, the user states an inability to log in, which is a failure related to the Target.