language:
- en
license: apache-2.0
LLM user flow classification
This a ONNX quantized model and is fined-tuned version of MiniLMv2-L6-H384.
This model identifies common events and patterns within the conversation flow. Such events include, for example, complaint, when a user expresses dissatisfaction.
This model is used only for the user texts. For the LLM texts in the dialog use this agent model.
Optimum
Installation
Install from source:
python -m pip install optimum[onnxruntime]@git+https://github.com/huggingface/optimum.git
Run the Model
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
pip install tokenizers
pip install onnxruntime
git clone https://huggingface.co/minuva/MiniLMv2-userflow-v2-onnx
Run the Model
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
Click to expand!
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.
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 for a FastAPI and ONNX based server to deploy this model on CPU devices.