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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%pip install transformers\n",
"%pip install torch\n",
"%pip install pandas\n",
"%pip install scikit-learn\n",
"%pip install datasets\n",
"%pip install evaluate\n",
"%pip install tqdm\n",
"%pip install openpyxl\n",
"%pip install numpy"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForMaskedLM\n",
"import torch"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of XLMRobertaForMaskedLM were not initialized from the model checkpoint at fine-tuned-512-8 and are newly initialized: ['lm_head.bias', 'lm_head.decoder.bias', 'lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
],
"source": [
"model = AutoModelForSequenceClassification.from_pretrained(\"fine-tuned-512-8\")\n",
"masked_model = AutoModelForMaskedLM.from_pretrained(\"fine-tuned-512-8\") \n",
"tokenizer = AutoTokenizer.from_pretrained('tokenizer', padding=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def predict(text):\n",
" inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=True)\n",
" labels_mapping = {0: 'negative', 1: 'positive'}\n",
" outputs = model(**inputs)\n",
" logits = outputs.logits\n",
" predicted_class = torch.argmax(logits, dim=1).item()\n",
" print(f\"Predicted Class: {labels_mapping[predicted_class]}\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Class: negative\n"
]
}
],
"source": [
"predict('αααααΈααΆαααααα·ααα·αααααΆααααααΆαααααααΆααααααα')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "cadtml",
"language": "python",
"name": "python3"
},
"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"
}
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"nbformat": 4,
"nbformat_minor": 2
}
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