Create README.md

README.md

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+# Text Classification of conversation flow
+
+This a ONNX quantized model and is fined-tuned version of [nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large).
+The original model can be found [here](minuva/MiniLMv2-userflow-v2)
+
+A flow label is orthogonal to the main conversation goal, implying that it categorizes actions or responses in a way that is independent from the primary objective of the conversation. 
+
+# Usage
+
+## 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 = ["I am angry", "I feel in love"]
+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
+# [('anger', 0.9745745062828064), ('love', 0.9884329438209534)]
+
+```
+
+# 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 |
+