Update README.md

README.md

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 ---
+language: en
 license: mit
+library_name: transformers
+pipeline_tag: text-generation
+base_model: mistralai/Mistral-7B-Instruct-v0.2
+tags:
+- sentiment-analysis
+- text-classification
+- reinforcement-learning
+- mistral
 ---
+
+### 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*](https://huggingface.co/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=1`.
+
+### 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=1` returns the label only. Use the system prompt below, which defines the "Target" and the sentiment rules.
+
+```python
+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=1, 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`:
+
+```python
+output = model.generate(**inputs, max_new_tokens=128, 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.
+```
+
+> Created by Microsoft