microsoft/Sentiment-Analysis-7B

Sentiment-Analysis-7B

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.

Evaluation metrics

The evaluation metric used is accuracy. The following compares the base model with the fine-tuned model.

| model | accuracy | | ---------------------------------- | --------- | | mistralai/Mistral-7B-Instruct-v0.2 | 62.0% | | Sentiment-Analysis-7B (ours) | 72.8% |

How to use

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.

import torch
from 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 only
output = model.generate(**inputs, max_new_tokens=3, do_sample=False)
print(tokenizer.decode(output[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Output:

Negative

To get the explanation as well, increase max_new_tokens:

output = model.generate(**inputs, max_new_tokens=64, do_sample=False)
print(tokenizer.decode(output[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Output:

Negative, the user states an inability to log in, which is a failure related to the Target.