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{
"cells": [
{
"cell_type": "code",
"execution_count": 19,
"id": "297ea6c7-1eae-47fc-8fa3-d49e6d3deb6c",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import torch\n",
"from transformers import AutoModelForSequenceClassification, AutoTokenizer"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "45ccf708-2a0b-43a1-bf5f-45294ab205d4",
"metadata": {},
"outputs": [],
"source": [
"with open(\"twiz-data/all_intents.json\", 'r') as json_in:\n",
" data = json.load(json_in)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "d9875b16-36f8-4289-9ddf-6907f74a975c",
"metadata": {},
"outputs": [],
"source": [
"id_to_intent, intent_to_id = dict(), dict()\n",
"for i, intent in enumerate(data):\n",
" id_to_intent[i] = intent\n",
" intent_to_id[intent] = i"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "01a87f85-e4d7-454c-b645-bf252161d458",
"metadata": {},
"outputs": [],
"source": [
"model = AutoModelForSequenceClassification.from_pretrained(\"roberta-based/checkpoint-925\", num_labels=len(data), id2label=id_to_intent, label2id=intent_to_id)\n",
"tokenizer = AutoTokenizer.from_pretrained(\"tokenizer\")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "f29489cf-fa4b-453e-8922-6e972db1cc7c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"NextStepIntent\n"
]
}
],
"source": [
"model_in = tokenizer(\"I really really wanna go to the next step\", return_tensors='pt')\n",
"with torch.no_grad():\n",
" logits = model(**model_in).logits\n",
" predicted_class_id = logits.argmax().item()\n",
" print(model.config.id2label[predicted_class_id])\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "ws2024",
"language": "python",
"name": "ws2024"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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