MiniLMv2-userflow-v2-onnx/README.md

---
language:
- en

inference: false
tags:
  - text-classification
  - onnx
  - int8
  - optimum
  - multi-class-classification
  - ONNXRuntime
license: apache-2.0
---

# LLM user flow classification

This model identifies common events and patterns within the conversation flow. 
Such events include, for example, complaint, when a user expresses dissatisfaction.
The flow labels can serve as foundational elements for sophisticated LLM analytics.

It is ONNX quantized and is a fined-tune of [MiniLMv2-L6-H384](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large).
The base model can be found [here](https://huggingface.co/minuva/MiniLMv2-userflow-v2)

This model is used *only* for the user texts. For the LLM texts in the dialog use this [agent model](https://huggingface.co/minuva/MiniLMv2-agentflow-v2-onnx). 


# Optimum

## Installation

Install from source: 
```bash
python -m pip install optimum[onnxruntime]@git+https://github.com/huggingface/optimum.git
```


## Run the Model
```py
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer, pipeline

model = ORTModelForSequenceClassification.from_pretrained('minuva/MiniLMv2-userflow-v2-onnx', provider="CPUExecutionProvider")
tokenizer = AutoTokenizer.from_pretrained('minuva/MiniLMv2-userflow-v2-onnx', use_fast=True, model_max_length=256, truncation=True, padding='max_length')

pipe = pipeline(task='text-classification', model=model, tokenizer=tokenizer, )
texts = ["that's wrong", "can you please answer me?"]
pipe(texts)
# [{'label': 'model_wrong_or_try_again', 'score': 0.9737648367881775},
# {'label': 'user_wants_agent_to_answer', 'score': 0.9105103015899658}]
```


# ONNX Runtime only

A lighter solution for deployment

## Installation 

```bash
pip install tokenizers
pip install onnxruntime
git clone https://huggingface.co/minuva/MiniLMv2-userflow-v2-onnx
```


## Run the Model

```py
import os
import numpy as np
import json

from tokenizers import Tokenizer
from onnxruntime import InferenceSession


model_name = "minuva/MiniLMv2-userflow-v2-onnx"

tokenizer = Tokenizer.from_pretrained(model_name)
tokenizer.enable_padding(
    pad_token="<pad>",
    pad_id=1,
)
tokenizer.enable_truncation(max_length=256)
batch_size = 16

texts = ["that's wrong", "can you please answer me?"]


outputs = []
model = InferenceSession("MiniLMv2-userflow-v2-onnx/model_optimized_quantized.onnx", providers=['CPUExecutionProvider'])

with open(os.path.join("MiniLMv2-userflow-v2-onnx", "config.json"), "r") as f:
            config = json.load(f)

output_names = [output.name for output in model.get_outputs()]
input_names = [input.name for input in model.get_inputs()]

for subtexts in np.array_split(np.array(texts), len(texts) // batch_size + 1):
            encodings = tokenizer.encode_batch(list(subtexts))
            inputs = {
                "input_ids": np.vstack(
                    [encoding.ids for encoding in encodings],
                ),
                "attention_mask": np.vstack(
                    [encoding.attention_mask for encoding in encodings],
                ),
                "token_type_ids": np.vstack(
                    [encoding.type_ids for encoding in encodings],
                ),
            }

            for input_name in input_names:
                if input_name not in inputs:
                    raise ValueError(f"Input name {input_name} not found in inputs")

            inputs = {input_name: inputs[input_name] for input_name in input_names}
            output = np.squeeze(
                np.stack(
                    model.run(output_names=output_names, input_feed=inputs)
                ),
                axis=0,
            )
            outputs.append(output)

outputs = np.concatenate(outputs, axis=0)
scores = 1 / (1 + np.exp(-outputs))
results = []
for item in scores:
    labels = []
    scores = []
    for idx, s in enumerate(item):
        labels.append(config["id2label"][str(idx)])
        scores.append(float(s))
    results.append({"labels": labels, "scores": scores})


res = []

for result in results:
    joined = list(zip(result['labels'], result['scores']))
    max_score = max(joined, key=lambda x: x[1])    
    res.append(max_score)

res
#[('model_wrong_or_try_again', 0.9737648367881775),
# ('user_wants_agent_to_answer', 0.9105103015899658)]
```

# Categories Explanation

<details>
  <summary>Click to expand!</summary>
  
  - OTHER: Responses that do not fit into any predefined categories or are outside the scope of the specific interaction types listed.

  - agrees_praising_thanking: When the user agrees with the provided information, offers praise, or expresses gratitude.

  - asks_source: The user requests the source of the information or the basis for the answer provided.

  - continue: Indicates a prompt for the conversation to proceed or continue without a specific directional change.

   - continue_or_finnish_code: Signals either to continue with the current line of discussion or code execution, or to conclude it.

  - improve_or_modify_answer: The user requests an improvement or modification to the provided answer.

  -  lack_of_understandment: Reflects the user's or agent confusion or lack of understanding regarding the information provided.

   - model_wrong_or_try_again: Indicates that the model's response was incorrect or unsatisfactory, suggesting a need to attempt another answer.

   - more_listing_or_expand: The user requests further elaboration, expansion from the given list by the agent.

   - repeat_answers_or_question: The need to reiterate a previous answer or question.

  - request_example: The user asks for examples to better understand the concept or answer provided.

  - user_complains_repetition: The user notes that the information or responses are repetitive, indicating a need for new or different content.

  - user_doubts_answer: The user expresses skepticism or doubt regarding the accuracy or validity of the provided answer.

  - user_goodbye: The user says goodbye to the agent.

  - user_reminds_question: The user reiterates the question.

  - user_wants_agent_to_answer: The user explicitly requests a response from the agent, when the agent refuses to do so.

  - user_wants_explanation: The user seeks an explanation behind the information or answer provided.

  - user_wants_more_detail: Indicates the user's desire for more comprehensive or detailed information on the topic.

  - user_wants_shorter_longer_answer: The user requests that the answer be condensed or expanded to better meet their informational needs.

  - user_wants_simplier_explanation: The user seeks a simpler, more easily understood explanation.

  - user_wants_yes_or_no: The user is asking for a straightforward affirmative or negative answer, without additional detail or explanation.
</details>

<br>


# Metrics in our private test dataset
| Model (params)    |    Loss      |    Accuracy |  F1 |
|--------------------|-------------|----------|--------| 
| minuva/MiniLMv2-userflow-v2 (33M) |   0.6738 | 0.7236 | 0.7313 |
| minuva/MiniLMv2-userflow-v2-onnx (33M) |   - | 0.7195 | 0.7189 |

# Deployment

Check our [llm-flow-classification repository](https://github.com/minuva/llm-flow-classification) for a FastAPI and ONNX based server to deploy this model on CPU devices.