Create README.md

README.md

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+---
+language:
+- en
+---
+# 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-agentflow-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. 
+
+This model should be used *only* for agent dialogs.
+
+# Usage
+
+## Installation
+```bash
+pip install tokenizers
+pip install onnxruntime
+git clone https://huggingface.co/minuva/MiniLMv2-agentflow-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-agentflow-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 = ["thats my mistake"]
+outputs = []
+model = InferenceSession("MiniLMv2-agentflow-v2-onnx/model_optimized_quantized.onnx", providers=['CPUExecutionProvider'])
+
+with open(os.path.join("MiniLMv2-agentflow-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
+# [('agent_apology_error_mistake', 0.9991708993911743)]
+```
+
+# Categories Explanation
+
+<details>
+  <summary>Click to expand!</summary>
+  
+    - OTHER: Responses or actions by the agent that do not fit into the predefined categories or are outside the scope of the specific interactions listed.
+
+    - agent_apology_error_mistake: When the agent acknowledges an error or mistake in the information provided or in the handling of the request.
+
+    - agent_apology_unsatisfactory: The agent expresses an apology for providing an unsatisfactory response or for any dissatisfaction experienced by the user.
+
+    - agent_didnt_understand: Indicates that the agent did not understand the user's request or question.
+
+    - agent_limited_capabilities: The agent communicates its limitations in addressing certain requests or providing certain types of information.
+
+    - agent_refuses_answer: When the agent explicitly refuses to answer a question or fulfill a request, due to policy restrictions or ethical considerations.
+
+    - image_limitations": The agent points out limitations related to handling or interpreting images.
+
+    - no_information_doesnt_know": The agent indicates that it has no information available or does not know the answer to the user's question.
+
+    - success_and_followup_assistance": The agent successfully provides the requested information or service and offers further assistance or follow-up actions if needed.
+</details>
+
+<br>
+
+
+# Metrics in our private test dataset
+| Model (params)    |    Loss      |    Accuracy |  F1 |
+|--------------------|-------------|----------|--------| 
+| minuva/MiniLMv2-agentflow-v2 (33M) |   0.1462 | 0.9773 | 0.9774 |
+| minuva/MiniLMv2-agentflow-v2-onnx (33M) |   -  |  0.97394 | 0.97392  |
+
+# Deployment
+
+Check [our repository](https://github.com/minuva/flow-cloudrun) to see how to easily deploy this (quantized) model in a serverless environment with fast CPU inference and light resource utilization.